Biodiversity Informatics, 17, 2022, pp. 27-49 27 BEST PRACTICES FOR DATA MANAGEMENT IN CITIZEN SCIENCE: AN INDIAN OUTLOOK Thomas Vattakaven1*, Vijay Barve2, Geetha Ramaswami3, Priya Singh4, Suneha Jagannathan5, Balasubramanian Dhandapani6 1Strand Life Sciences, Ground Floor, UAS Alumni Association Building, Veterinary College Campus, Bellary Road, Bangalore, 560 024 2Nature Mates Nature Club, 6/7, Bijoygarh, Kolkata 700032 West Bengal, India 3Nature Conservation Foundation, 1311,”Amritha”, 12th Main Vijayanagar 1st Stage, Mysore, 570 017. 4Researchers for Wildlife Conservation (RWC), National Centre for Biological Sciences, GKVK Campus, Bangalore, 560065. 5Dakshin Foundation, #2203, D Block, 8th Main, 16th D Cross, Sahakar Nagar, Bengaluru, 560092. 6French Institute of Pondicherry, 11, St. Louis Street, Pondicherry, 605001. *Corresponding author: Thomas Vattakaven, Email: thomas.vee@gmail.com Abstract. Citizen science has been in practice since the 1800s and is an important source of data for scientists and other applied users. It plays a vital role in democratizing science, providing equitable access to scientific participation and data, helps build the capacity of its participants, inculcates the spirit of scientific endeavor and discovery and sensitizes participants towards species and habitat conservation, creating a sense of stewardship towards nature. In recent years, citizen science, especially in biodiversity, has rapidly developed with the rising popularity of smartphones, and widespread access to the internet, leading to wider adoption globally. India has also witnessed a surge in the number of new citizen science projects being initiated and increased participation in these projects. With more proponents looking at initiating such projects, there is little documentation from an Indian perspective on setting up, collecting, managing, and maintaining biodiversity-focused citizen science projects, especially in a data-management context. We have attempted to fill this void by examining the best practices across the data life cycle of citizen science projects while keeping in mind sensitivities and scenarios in India. We hope this will prove to be an important reference for citizen science practitioners looking to better manage their data in their projects. Key words: Biodiversity, Citizen science, Data, Data management, India, Licensing, Metadata, Protocol, Quality assurance, Standards, Data lifecycle Citizen science has evolved as a significant field of practice, and its role in contributing to new knowl- edge on biodiversity is well established (e.g., Kobori et al. 2016; Schuttler et al. 2019). While originating in the West, it has spread globally, particularly in India in the last decade (Sekhsaria and Thayyil 2019). The evolution of citizen science as a field of practice and research has necessitated an inquiry towards overar- ching insights, standards, vocabulary, and guidelines (Vohland et al. 2021). In 2020, India hosted its first CitSci India Con- ference for Biodiversity1, bringing citizen science 1 https://citsci-india.org/. practitioners, researchers, educators, students, and policymakers interested in biodiversity and citizen science. This virtual meeting was hosted as part of the preparatory phase of the National Mission on Biodiversity and Human Well-being proposed by the Biodiversity Collaborative, with the National Bio- diversity Authority as a nodal agency. CitSci India 2020 was a starting point to bring together the citizen science community in India under one platform to share experiences, inspire each other, and engage in discussions related to citizen science. Two prominent topics that surfaced during these discussions were the importance of ethics, diversity and inclusion, and mailto:thomas.vee@gmail.com https://citsci-india.org/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 28 data in citizen science. In this context, we focus on best practices in data management for Indian practi- tioners. Following global trends, citizen science efforts involving biodiversity in India have rapidly gained pace over the past few years. With larger and small- er-scale citizen science projects increasingly launched in India each year, voluminous data is generated on various aspects of biodiversity (see list of Indian citizen science projects2). However, this also raises several issues regarding data, such as ownership, ac- cessibility, attribution, storage, interoperability, and quality. The working group on Citizen Science Data was tasked with identifying significant aspects relat- ed to data on which project proponents should have clear procedures and policies. To put together this document, we surveyed existing global practices and standards and described various options that projects could adopt, with some guidance about benefits and costs associated with each option. The document is intended to form a toolkit for citizen science prac- titioners in India and elsewhere who seek to make informed decisions on various aspects of data. For this document, we use a definition of ‘citizen science’ provided by Guerrini et al. (2019), as per which, citizen science “... generally refers to an ap- proach to scientific inquiry in which members of the public participate in one or more steps of the research process other than, or in addition to, allowing person- al data or biospecimens to be collected from them for analysis by others’’. At this juncture, it is worthwhile to mention that there is an effort to replace the us- age of the word “citizen” with “community” science to be more inclusive (Cooper et al. 2019), although some prefer to retain the distinction between these two terms (Dosemagen and Parker 2019). For the context of this paper, we primarily limit our refer- ence to citizen science projects within biodiversity that at least partially utilize online participatory me- diums with databases and servers that make data and its products accessible online. Types of Citizen Science Projects Citizen science initiatives vary extensively in their aims and objectives across disciplines and cit- izen engagement. An attempt to classify common citizen science projects can be undertaken based on parameters such as the research question, modes of participation, medium of participation, and mode of the survey, as discussed in detail below. 2 https://citsci-india.org/citizen-science-projects/. Research Question/Focus. Although citizen science has traditionally been used to address targeted re- search questions and hence involves specified pro- tocols, the advent of online mediums and the ability to crowdsource content has paved the way for more open-ended platforms. Such platforms may engage citizen scientists in tasks such as gathering sight- ings of species or transcribing or classifying data for which the uses may be unknown or changing (Luk- yanenko et al. 2016). Based on the above criteria, projects may be classified as generalist or specialist projects. An alternate way of looking at this type of focus may be to classify projects based on the taxa of fo- cus. There are larger generalist initiatives that have little or no restriction based on the taxonomic focus (e.g., India Biodiversity Portal3, IBP), while targeted projects often tend to focus on selected or a single taxonomic group or species (e.g., Biodiversity Atlas - India4, Bird Count India5, Wild Canids-India Proj- ect6, Marine Life of Mumbai7). Modes of Participation. Citizen science initiatives can vary in terms of who initiates a project or the lev- el and stage of involvement of volunteers or the gen- eral public in an initiative depending on the project’s objectives. Project initiators play an essential role in defining the nuances of a project and, hence, deter- mining the end goals that influence ‘the political au- thority of science’ (Kimura and Kinchy 2016). Simi- larly, the composition and training of citizen science initiators vary across projects. For this document, we highlight different types of public-scientist collabo- rations that qualify as citizen science engagements based on Veeckman et al. 2019. Citizen science projects can be categorized based on multiple criteria: the extent of citizen participa- tion, taxonomic focus of the project, or medium of participation. Table 1 describes different types of citizen science programs based on the extent of in- volvement of citizens, as described in Veeckman et al. 2019. Medium of participation. Currently, two distinct channels allow establishing a citizen science initia- tive. These include: 3 https://indiabiodiversity.org/. 4 https://www.bioatlasindia.org/. 5 https://birdcount.in/. 6 https://www.wildcanids.net/. 7 https://www.marinelifeofmumbai.in/. https://citsci-india.org/citizen-science-projects/ https://indiabiodiversity.org/ https://www.bioatlasindia.org/ https://birdcount.in/ https://www.wildcanids.net/ https://www.marinelifeofmumbai.in/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 29 A. Independent platforms via web or smartphone-based methods using protocols built explicitly for the project context. Such platforms allow for flexibility in developing independent protocols tailor-made to suit the requirements of the study. B. Larger aggregator platforms with the ability to host independent projects within them, e.g., India Biodiversity Portal, Biodiversi- ty Atlas - India, iNaturalist8, CitSci.org9. These platforms usually host a range of projects that collectively benefit from an existing user-base of citizen science contributors, are easy to use with access to pre-vetted guidelines, instructions of usage, terms, and conditions, and other legal and technical formalities addressed. They are also equipped with measures to ensure data security and data-quality regulations. All these features allow them to be used with ease across a diversi- ty of projects and overcome the lack of technical know-how amongst project managers. A few initiatives are platform-independent and use social media and mobile messaging applications such as Facebook, Whatsapp, or email to gather bio- diversity data. Data collected through such mediums are primarily not structured by default, nor are they controlled environments with binding data policies or licenses. Most of these are still emergent, and al- 8 https://www.inaturalist.org/. 9 https://citsci.org/. though there is potential to crowdsource content us- ing these increasingly popular mediums, due to their free-form nature of the interaction, much effort will be needed to extract, curate, and cleanse the content before use as meaningful, structured data. Mode of survey. Citizen science may be carried out in a conventional scientific framework with a standardized field protocol. However, the most pop- ular citizen science initiatives, especially those that allow for data entry through online interfaces and recruit online participation, are increasingly being done without standardized field protocols, giving rise to the term “opportunistic sampling.” We discuss below some pros and cons of opportunistic versus structured data sampling from the perspective of par- ticipant motivation and data quality. Citizen Science and Data-Life Cycle Like most scientific data, citizen science data fol- low a general data life cycle. For the purpose of this paper, we have chosen to adopt the high-level Sci- ence Data Lifecycle Model (SDLM) (Faundeen et al. 2014), to illustrate how data management activities relate to citizen science data workflows and recom- mend actions and activities at each stage of the mod- el. SDLM comprises primary elements that proceed sequentially and cross-cutting elements performed across stages of the data life cycle. The SDLM and its elements are summarized in Figure 1. Keeping in mind the above data model, we have attempted to structure our paper into broad sections Table 1. Types of citizen science programs based on the extent of participation by citizens, as described in Veeckman et al. (2019). Type of project Extent of citizen participation Crowdsourcing Volunteers remain passive while contributing time and equipment only Distributed intelligence Volunteers are involved with simple interpretations or categorizing material from gathered data Participatory science Volunteers define a problem, collect data and assist scientists in analyzing the data. Interpretation and analysis handled by scientists Extreme citizen science Volunteers and scientists collectively determine stages of the project, with the for- mer handling all tasks related to the study and executing them. Scientists only act as facilitators on these projects. Contributory project Volunteers are invited to contribute data, while scientists decide the research focus of the study, and analyze and interpret data. Collaborative project Flexible projects where the scientist involved may identify the research focus of a project, while volunteers participate at different stages of the study based on their interest Co-created project Aimed at influencing public policy or educational agenda. Volunteers identify a set of questions, answers to which are thereafter pursued in consultation with scientists on the project. https://www.inaturalist.org/ https://citsci.org/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 30 that cater to aspects of data within citizen science: before starting a project (planning), during the imple- mentation of the project (acquiring), and after gath- ering data (processing, analyzing, preserving, and publishing data). Certain aspects covered below have cross-cutting implications and may be relevant at multiple stages of the data life cycle but may be cov- ered in more detail in one section to avoid repetition. Before Starting In conformity with scientific practice, before initiating a citizen science project, it is essential to premeditate on a project’s objectives, develop hy- potheses, identify methods of acquiring, analyzing, and interpreting data, and ideal ways of disseminat- ing results. Potential challenges need to be identified to ensure that project end goals are achieved effec- tively. This section summarises points to keep in mind at different stages of a citizen science project. Identify Project Goals and Means of Implementation Citizen science programs vary in their primary goals and the extent and role of public participation, with some projects exclusively aiming to achieve public engagement (refer to Types of Citizen Science Projects). Projects can have a broad focus, such as creating generic biodiversity repositories or targeting focused research questions. Preliminary intensive re- view of the topic being pursued allows determination of the suitability of citizen science as a study technique. Identify Target Participants/Stakeholders Delineating target participants helps one design appropriate strategies to recruit, train and engage vol- unteers for a program. These can be selected based on need (not all citizen science projects require a target- ed volunteer base), required skill sets (such as swim- ming, diving, climbing, identifying species), access to technology (such as smartphones), or age (adults or children). At times, engagement with intermediar- ies (such as schools, colleges, tourism ventures) may be required to enlist participation. When soliciting involvement from local communities, planning for localizing content in regional languages can help en- hance participation and outreach. Building Online and Offline Infrastructure The backbone of a citizen science project is the infrastructure that it requires to function: to main- tain registers of participants; collect, manage, curate, Figure 1: The Science Data Life Cycle Model has primary and cross-cutting elements that help determine action at different stages of data collection, preservation and analyses. Primary elements include planning, acquiring, processing, analyzing, preserving, and publishing data, while the cross-cutting elements run parallelly throughout the data life cycle and involve describing metadata, managing data quality, and data security/backup (Faundeen et al 2014). The primary elements of the data life cycle can be further mapped to ‘before’ data acquisition (pink arrow), ‘during’ a project (orange arrow), and ‘after’ data are acquired (green arrows). Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 31 and store data; and disseminate results and maintain regular communication with participants. Online in- frastructure such as websites, smartphone applica- tions, or even simple forms of communication like Whatsapp groups allows contributing data. Suitable back-end databases that store data in appropriate formats and enable interfaces to query and retrieve data efficiently need to be chosen at this stage. Es- tablishing offline infrastructure requires high human effort, such as for collaborations, acquiring permits for access to protected areas (if needed), and initial outreach to gauge interest within target participants. It is also crucial to design data collection protocols (explained in greater detail in Data Collection Meth- odologies) for citizen science projects and pilot them within small focus groups to devise appropriate data collection methods. Volunteer Recruitment, Engagement, and Outreach Citizen science initiatives depend extensively on volunteer engagement to be successful. This engagement can be divided into three general phases, as follows: 1) Volunteer recruitment involves outreach to the target participants, testing out protocols with focus groups, and seeking volunteer feedback on initial processes. Typical methods employed are social media outreach, publicity articles in print media, tapping email list services, and presentations at target institutions such as nature clubs, schools, or colleges. In the case of projects targeting niche species that are uncommon or restricted in their distribution, establishing partnerships with local communities or tour operators may also be considered. 2) Volunteer education and capacity building: in addition to gathering data, citizen science initiatives also endeavor to increase scientific and ecological literacy among the public. In some cases, volunteers might require specific knowledge (species identification skills, basic survey skills) to participate in a program. The extent of skill training and knowledge exchange often depends on the data collection methodology. Volunteer education is a long-term exercise that needs to be carried out regularly. Practitioners need to ensure that volunteers and contributors understand the scientific problem being addressed, are trained well in collecting information, can use technology (if any) required for data contribution, and collect data in a standardized manner. Errors can be minimized by training and reiterating the collection protocol. The contribution process needs to be tested periodically to recognize new error sources and iteratively update training and contribution processes. 3) Volunteer retention: citizen science efforts benefit from retaining volunteers over the long term, as their expertise and skill are likely to increase with time. However, this exercise requires innovative methods to sustain the interest of long-term volunteers. Leaderboards (to track the highest participation) or games and contests may encourage participation. Not all projects start with a captive volunteer base, and citizen participants may see turnover throughout the project duration. For long-term projects renewing interest in the project to recruit newer participants is crucial. Data Management, Analysis, and Dissemination Maintaining, curating, and analyzing data are critical aspects of a citizen science program. Data management involves data storage, curation, and backup techniques, ensuring that data are not lost once a project is deemed to be complete. It is import- ant to note that citizen science is a long and evolving effort - the goals of a project might change over its lifetime. Considering this, one must follow data stan- dards to maintain the usefulness of the data collect- ed. Furthermore, it is essential to ensure that data are analyzed and visualized by participants, including non-experts, in an engaging manner. Mechanisms for user interaction with the data, roles, and permissions for data validators, strategies to flag erroneous data, etc., need to be thought of at this point and must be a continuous endeavor throughout the project, evolv- ing with time. Data Collection Methodologies Citizen science projects vary in the rigor of sam- pling protocol, from simple occurrence reporting to more structured data collection techniques. This presents practitioners with a trade-off between vol- unteer participation and the quality of data collected. Often, programs with rigorous volunteer training and sampling protocols obtain better quality data but with reduced levels of participation. In contrast, those with simple data collection methods report higher partici- pation, often with biased and noisy data. Techniques such as data collection forms or semi-structured sur- veys can reduce the need for rigorous training while still ensuring that data are collected in a prescribed format (Bonney et al. 2009; Kelling et al. 2019). Incentivizing quality over the number of obser- vations in leaderboards and gamification techniques can be helpful. Gamification is used to motivate par- Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 32 ticipants to contribute data to maximize contribu- tions and enhance volunteer retention. It can range from adding a point system to ranking, creating lead- er-boards, giving badges or rewards, to creating an actual game that requires enhanced engagement from the participant. Prioritization of spatial and temporal scales where there are data gaps, rather than species or numbers of records, could result in more even dis- tribution of biodiversity records, thus reducing spa- tial and temporal biases (Callaghan et al. 2019). Planning for Data Quality Assurance The credibility and quality of citizen science data is often questioned even though recent studies have challenged this view (e.g., Barve 2014). Hence, data quality assurance is an important topic to consider at each data cycle stage, i.e., data collection, upload and ingestion, storage, management, and analysis. Data should be accurate, precise, and representative to enhance credibility10, but when the desired accu- racy and precision are not achieved, it is important to document its known quality as data quality and usability depends on the users’ questions (Chapman 2005b,a). Data needs to be thoroughly vetted for accuracy by curators or professionals pre-identified by the project and reflect reality. Consistency and replicability can ensure data precision, while spatial and temporal representativeness is necessary for any scientific exercise. Other ancillary information such as date, location, time of observation, weather condi- tions, etc. further aid in improving data quality and reliability. Maintaining records with data provenance allows preserving information about the evolution of data and methodologies used to acquire it, which is vital for debugging, tracking changes, auditing, and evaluating quality. Wiggins et al. (2011) categorize potential bias- es in ecological data obtained using citizen science. Some of these include (a) positive spatial bias in- duced by areas with a higher human population, bet- ter or easier accessibility, and better accessibility to the internet in urban spaces (Geldmann et al. 2016). This may also be true for frequently visited hotspots (Boakes et al. 2016; Tiago et al. 2017); (b) Positive temporal bias induced by a more significant number of records during weekends, holidays, and contests, which is particularly of concern in phenology stud- ies, where data seasonality is an integral part of the research (Courter et al. 2013). (c) Taxonomic bias is noticeable, with rare, cryptic, or difficult-to-identify 10 https://citizenscienceguide.com/design-sample-collection. species often being underrepresented or remaining unidentified leading to a paucity of data for such spe- cies (Falk et al. 2019). In addition, very commonly observed species tend to be overlooked and under- reported, while highly sought after species may also be over-reported, leading to non-representative sam- pling (Troudet et al. 2017; Callaghan et al. 2021). Finally, (d) observer bias induced by individual perceptions and levels of experience (Gonsamo and D’Odorico 2014; Callaghan et al. 2021). Identifying sources of bias is necessary to help managers design appropriate strategies at different phases of projects to mitigate or manage them effectively. A variety of end-users can utilize data from cit- izen science projects and it is important to identify the likely end-users of the data (e.g., scientists, poli- cymakers, amateur naturalists) at the initial stages of a project. It should be noted that the onus of main- taining data quality is the shared responsibility of the project as well as data-users who must examine and put thought into the use of data and correct for biases, error rates, or quirks. Data quality and minimization of biases can be accounted for before data collection and during the data contribution stage. Baker et al. 2021, summarize the types of data and levels of evidence at the data contribution stage which would require verification - a) Levels of evidence: reporting of sightings with- out evidence, and reporting sightings with evidence such as photo/video/audio/specimen, b) Types of observations: direct observation, where the taxon is observed directly, and indirect observation, wherein taxon signs (such as tracks, dung, etc.) are recorded. The mechanisms and criteria required for data validation need to be considered at this stage and suitably incorporated into the collection procedure to provide for the availability of fields or the target pre- cision levels to be achieved. The ability to validate or curate records may be contingent on the presence of such information fields and without which data may be unverifiable. Data analyses should also be planned and an- ticipated before data collection and should be ap- propriate for the kind of data collected and driven by the project’s goals (Wiggins et al. 2011; Balázs et al. 2021). Factors affecting data quality need to be identified. This may include improper data col- lection, incorrect implementation of data collection protocols, a mismatch between project goals and data collection protocols, incomprehensive protocols that do not match end-user expectations, and use of data Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 33 in wrong contexts. Balázs et al. (2021) suggest the following at the planning stage of a citizen science project to ensure data quality and to make data conducive for further analyses: (a) simple and intuitive data collection pro- tocol supplemented by a simple user interface design that is engaging and can be applied across a diverse group of users with varied skills, (b) calibrating and standardizing devices and recognizing limitations of technology, (c) appropriate documentation, and (d) metadata to prevent misuse of data in incorrect con- texts. Conferring with experts could enhance the qual- ity of analyses. Inferences should be cautious and consider all the caveats of data accuracy and analy- sis. It is also beneficial to get the analyses reviewed by experts and peer groups. Quality assurance through following standards. It is essential to incorporate data collection meth- ods and protocols, fitness for use, and data quality assessment as part of the metadata/documentation (Assumpção et al. 2018). Adapting and adhering to standards helps in improving data quality and usabil- ity due to breaking up data attributes into appropriate terms and following controlled vocabularies to en- sure each term conveys the correct meaning. In the biodiversity realm, standards developed by the Bio- diversity Information Standards, originally called the Taxonomic Databases Working Group (TDWG11), like Darwin Core and Audubon Core (Table 1) and tools built around them, are readily available for citi- zen science projects to use and adapt. Planning for Data Infrastructure Contemporary citizen science projects are most- ly born-digital, conceived and implemented pre- dominantly in the digital ecosystem of information technology platforms, software applications, and toolchains. There are multiple online and offline infrastructure concerns. Proponents may wish to choose between larger aggregator platforms that al- low projects within them vis-a-vis building indepen- dent applications. Fast-growing mobile application technologies have made it possible to quickly deploy data collection and integration tools with little effort (Lemmens et al. 2021). On the other hand, several large biodiversity data aggregating platforms are well established and have gained a reputation across the globe (ebird12, iNaturalist), or country-level data (India Biodiversity Portal and Biodiversity Atlas for 11 https://www.tdwg.org/. 12 https://ebird.org/home. India, Atlas of Living Australia13 for Australia, na- tional GBIF nodes, etc.). Others look at specific taxa or simply to address a particular question. There are apparent advantages of using an existing platform for biodiversity data collection and aggregation, as they readily provide technological infrastructure, commu- nities, and tested infrastructures across data lifecycle (de Sherbinin et al. 2021). Depending on the project’s larger goals, there could be challenges in fitting the needs of a citizen science project to pre-existing templates and applica- tions provided by such platforms. Such larger-scale citizen science initiatives should allow for flexibility in engaging at different ecological levels, different aspects of ecosystem changes, and conservation is- sues (Devictor et al. 2010). Many large aggregator platforms already support infrastructure that allows such flexibility. For example, the IBP allows creat- ing groups within its infrastructure for any theme of interest, such as a taxonomic group. Forms for gath- ering data can be extended with the capability to in- clude custom queries and fields, as required. Data infrastructures for citizen science projects need to be adaptable to address the unique nature of each citizen science project. The data infrastructure should generally allow for data collection, aggrega- tion, analysis, and dissemination, thus covering the whole data life-cycle management or digital informa- tion supply chain (Brenton et al. 2018). For exam- ple, citizen science projects could use a phone or web application to collect data, a cloud server to store the data, automated code to verify data, a web portal to promote interaction among contributors, and a back- end database structure such that it can be aggregated with other types of data. This would also mean that the infrastructure enables the participation of citizen scientists in the full range of scientific methods from problem definition, research design, analysis, and ac- tion (McQuillan 2014). Citizen science projects in biodiversity tend to collect data across a wide assortment of attributes. These include taxonomic, evolutionary, biogeo- graphic, functional, and interspecific interaction at- tributes of a taxon (König et al. 2019). Data infra- structure should be flexible enough to accommodate the diversity of data types in a variety of formats such as text, tabular, geo-spatial, and varying media types, including images, audio, and video. Such capabilities will influence the scalability of storage required, par- ticularly for long-term projects. Cloud-based storage and content delivery networks in the mainstream IT 13 https://www.ala.org.au/. https://www.tdwg.org/ https://ebird.org/home https://www.ala.org.au/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 34 ecosystem have matured enough to ensure scalability and high availability across geographies. Apart from these fundamental concerns on data models and data storage, one must ensure that the platform is stable and provides continuous access to participants with minimum downtime. Platforms need to cater to data security with well-defined data access policies, user authentication systems with de- fined roles, transparent workflows, and user-centered design (Bowser et al. 2020). Regular backup of data with multiple copies in multiple locations and a con- sistent preservation policy across sites is essential for the security and integrity of citizen science data. In keeping with the spirit of open science, one can also insist on using, developing, and deploying free/ open-source technology stacks to help collaborative- ly build, share and replicate developed technologies for wider and unrestricted use. Data Ownership Data ownership is crucial for a project and needs deliberation at the planning stage. Participant per- ception of data ownership can influence their moti- vation in future participation. Yet, studies have in- dicated ambivalence in how participants feel about data ownership. On the one hand, most participants appear far removed from thoughts of data and its ownership. Each record is more of a personal nature experience and less so as data with legal ownership. Ganzevoort et al. 2017, best summarize this as con- stituting “an “imagined contract” between volunteer naturalists and nature, based on respect and won- derment…”. On the other hand, participants believed that “...data extracted from nature should properly be used towards its preservation” and hence “wrong” use of data can result in citizens being resentful and withholding contribution. In some instances of data sharing, moral rights may get infringed, especial- ly if the user of such data distorts or mutilates the data contributed by volunteers through re-use or if some private/sensitive information gets accidentally disclosed. Although most participants surveyed were against the unconditional use of data generated using citizen science, most participants were undecided on ownership, with some feeling data is nobody’s prop- erty and others that it could be owned by the organi- zation conducting the study (Ganzevoort et al. 2017). It may also be said that participants may feel strongly about data in ways that are not covered un- der legal ownership and may not qualify for legal protection. However, it may be possible to validate such feelings outside of traditional law through pol- icies that put in practice exclusive or non-exclusive access to or control over data (Guerrini et al. 2019). Sometimes traditional knowledge belonging to communities related to bio-resources or conservation practices might be part of such data. Establishing who owns this knowledge can be very challenging. In the case of community-held knowledge, it is easy to attribute ownership to a particular community. Still, there is ambiguity when the traditional knowledge is from an unidentifiable source or shared between communities spread across large territories. The knowledge may also be based on specific practices, beliefs, and linguistic representations, which may get lost in translation. One must be mindful of specific communities’ cultural sensitivities and secretiveness to divulge knowledge. The communities must have the freedom to say no to sharing their knowledge if they wish, and if they agree, they should be allowed to choose how their knowledge is used. Data Accessibility It is essential to have prior clarity on data acces- sibility regarding who can access the data, at what stages, and for what purposes. Accessibility to data generated through citizen science projects is a core aspect. Open access to data allows for democratizing science and upholding the values of universal and equitable access to scientific data, especially when gathered through public participation. Just as we strive to make citizen science accessible to a diver- sity of participants and make ‘doing science’ inclu- sive, the resulting data must be accessible in ways that support reproducible science and can influence policy through bridging gaps between knowledge and action. There are outlying concerns that, more often than not, a citizen scientist’s contribution dis- appears into the closed databases within institutions, and particular emphasis needs to be paid to alleviate these concerns. What is open data? There are variable interpreta- tions of the term ‘open data’. As stated by the Open Knowledge Foundation, “data is open if it can be freely accessed, used, modified and shared by anyone for any purpose14 - subject only, at most, to require- ments to provide attribution and/or share-alike”. Specifically, open data is defined by the Open Defi- nition and requires that the data be, both, a) Legally open: where it is made available under an open (data) license that allows anyone to freely access, reuse and 14 https://opendefinition.org/. https://opendefinition.org/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 35 redistribute the data; b) Technically open: where the data is made available freely or at a cost, no more than what is required for its reproduction and in for- mats that are in bulk and machine-readable. Therefore, open data means that it is complete, preferably downloadable over the internet in a con- venient and modifiable format without requiring pro- prietary software to process. It should also be “pro- vided under terms that permit reuse, redistribution, allow intermixing with other datasets and must not discriminate against fields of endeavour or against persons or groups such as against commercial use.” In this context, the data should conform to the FAIR open data principles to be Findable, Accessible, In- teroperable, and Reusable. Why is citizen science data not always open? Due to the varied nature of citizen science projects, proponents, and contexts of funding, not all projects may likely be in a position to adhere entirely to the tenets of open data. Some disagree with open data with justifications that vary in their context. With data serving as the currency for competition between scientists for limited funding and prestige through publications, the conventions of traditional academ- ic publishing have resulted in a tendency to hoard data in closed silos (Hampton et al. 2013). Others cite the burden and expense of running massive data projects, curating data and processing, and manag- ing people involved as a justification for exclusive access and reaping the resulting benefits (Walker et al. 2016). The data can be used as leverage to fund further project activities or, more importantly, to ob- tain acknowledgment, particularly as authors on pub- lications. Some might be willing to share data but on request to keep track of how it is being used, hence not publishing them under an open-access license. Other important reasons for data not being open are projects anchored at institutions having restrictive blanket data policies, especially concerning intellec- tual property rights for work generated as a part of the institution. Similarly, funding bodies sometimes impose conditions on data release as a part of their terms, which may be restrictive. Finally, privacy concerns regarding those of the participants generat- ing the data and when the data is about a species of concern may be a key consideration in limiting open access (Groom et al. 2017). Why it is recommended that citizen science data be open-access. There are many reasons for recom- mending citizen science data be open-access. Groom et al. 2017, state that “The voluntary aspect of the time invested by citizen scientists is generally interpreted as being motivated primarily by its contribution to society and that society should profit from this effort through openly accessible data.” Open-access allows participants to track their participation alongside ag- gregated data from other participants, learn from it, and incorporate the learning into improving their knowledge. Opening the data has also been shown to motivate participants with greater frequency and depth (Bonney et al. 2009). The availability of open data allows easy and quick access for citizens and decision-makers to use as evidence towards influenc- ing policy without waiting for formal assessments to emerge and closing the gap between knowledge and action. Open citizen science data thus enable partic- ipants to be at the “forefront of socially relevant sci- ence” (Hampton et al. 2013). Open data also supports reproducible science. Ethical Considerations to be Made at This Stage Some of the key ethical considerations in the planning stage of a citizen science initiative cover the realms of recognizing contributor rights in citi- zen science and designing socially inclusive projects. It also includes information on making data public or open-access versus limiting access (addressed in Data Accessibility). With the growing popularity of citizen sci- ence across geographies and academic disciplines, it is being rapidly incorporated as a methodology to identify scientific queries and find means of an- swering socially relevant questions with the aid of citizen contributors or collaborators. This has led to a growing recognition of contributor rights and the need to address power imbalances between project handlers and contributors. Hence, project managers must recognize the participatory nature of citizen science, where volunteers contribute data and time obligingly and ensure that projects are socially in- clusive. Projects must include participants irrespec- tive of gender, geographical location, socio-cultur- al, religious, linguistic, and academic backgrounds (Paleco et al. 2021). Simultaneously, participation at all stages of a project must be permitted. To maxi- mize participation, project designers should identify means of reaching out to all potential stakeholders. These could include interested citizen participants, such as members of the public, established citizen scientists or those from the scientific fraternity, aca- demic institutions/ organizations, policy experts, etc. (Veeckman et al. 2019). Collaborating with schools, Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 36 communities directly associated with the study sub- ject, or government bodies also helps in increasing participation (Veeckman et al. 2019). Project Implementation Data and Metadata Standards and Data Infrastructure Once the study design, data quality, adherence to standards, data infrastructure, and accessibility are planned for in a citizen science project, imple- mentation is next. At this stage, data infrastructure should be appropriate, scalable, and highly available for organizing the acquired data. It is also essential to incorporate data standards at this stage. Biodiver- sity data standards are shared rules and conventions to describe, record, and structure biodiversity data to enable data aggregation and exchange across differ- ent organizations generating and managing differ- ent data sets. Data standards enforce unambiguous definitions of what kind of data are being collected, follow well-defined ontologies and vocabularies, and standardize the usage of established protocols. It is recommended that citizen science projects follow prevalent international standards and adopt recom- mended storage formats and protocols. The use of biodiversity data standards has two key objectives, as follows. (1) Data standards pro- vide a comprehensive set of relevant attributes for most projects and meeting individual project needs for the collection and management of data. (2) Data standards aid in identifying a subset of core biodiver- sity data attributes that can be used to aggregate data. The different international standards available for citizen science data are described in Table 2. Us- er-contributed data are typically restructured slightly to adhere to project-specific standards before stor- ing in databases. However, this standardization and large-scale aggregation may lead to a loss of contex- tual richness (Turnhout and Boonman-Berson 2011; Ganzevoort et al. 2017). Apart from adhering to stan- dards, it is good practice to ensure that data are Find- able, Accessible, Interoperable and Reusable (FAIR, Wilkinson et al. 2016) and consider other principles like Collective benefit, Authority to control, Respon- sibility and Ethics (CARE, Carroll et al. 2021) in the context of data originating from indigenous commu- nities. The FAIR data principles facilitate the discov- ery of knowledge, integration, and use by the larger scientific community. FAIR principles ensure that data are discoverable to computational agents (e.g., computers and apps) and humans through standard- ized protocols – otherwise referred to as ‘machine actionable’ data (Wilkinson et al. 2016). In highly linguistically diverse contexts, such as in India, data infrastructure should be developed with support for multiple languages to enable data input and support accesses to facilitate large-scale participation. Quality Assurance and Quality Control Citizen science projects with high data quality adhere to good practices related to standards, meta- data, and documentation. Such projects also ensure that data errors, biases, uncertainty, and ethical con- cerns are addressed through volunteer training and validation, calibration of data collection tools, iter- ative evaluation, and enhancements, and flagging of erroneous records via appropriate validation methods (Kosmala et al. 2016; Ratnieks et al. 2016; Downs et al. 2021). While integrating data from different citizen science programs, incompatible design and inconsistencies in nomenclature can also affect data quality (Campbell et al. 2020). The following fail-safes can ensure that data col- lection is accurate before and during data collection: profiling contributors and assessing their skill levels, piloting a citizen science project to get a sample of data and potential sources of errors and biases, fol- lowing standardized methods of data collection and adopting established standards for terminology, par- ticipant training, auto-correction (e.g., erroneous geocoding), data verification, and facilitating access to data use (Balázs et al. 2021). Projects using de- vices should calibrate sensors, perform initial checks on devices and ascertain the ability of observers to use these devices to make accurate observations (de Sherbinin et al. 2021). Depending on the types of observation, post-col- lection data verification is a crucial step to ensure data accuracy. Community/peer consensus, expert verification, automated verification, model-based verification (statistical models address random/indi- vidual variation, residual errors, and uncertainty of devices to flag erroneous observations), and linked data analysis (combine existing datasets to serve as reference data and use data mining tools to flag erro- neous observations; Kelling et al. 2015; Balázs et al. 2021) are some of the existing methods that can be used for assessing data quality. In biodiversity data, species (identity, geographic co-occurrence with oth- er species, rarity), environmental (time, date, loca- tion) and expertise (experience of the recorder) con- texts should be verified through one or more methods Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 37 Standard Description URL DarwinCore (DwC) “A glossary of identifiers, labels, and definitions that facilitate the sharing of biodiversity information. DwC is based on taxa and their distribution document- ed through observations, specimens, samples, and related information. It is being regularly improved with the addi- tion of terms as well as the development of extensions to map various sources of data accurately.” https://www.tdwg.org/standards/dwc/ Audubon Core Multime- dia Resources Metadata Schema (AC): “A set of vocabularies designed to repre- sent metadata for biodiversity multime- dia resources and collections, with the aim of determining the suitability of the media for specific biodiversity science applications. Among others, the vocab- ularies address such concerns as the management of the media and collec- tions, descriptions of their content, their taxonomic, geographic, and temporal coverage, and the appropriate ways to retrieve, attribute and reproduce them.” https://www.tdwg.org/standards/ac/ The Access to Biological Collections Data (ABCD) “An evolving comprehensive standard for the access to and exchange of prima- ry biodiversity data (i.e. specimens and observations)” https://www.tdwg.org/standards/abcd/ Ecological Metadata Language (EML) “Defines a comprehensive vocabulary and a readable XML markup syntax for documenting research data. EML in- cludes modules for identifying and citing data packages, for describing the spatial, temporal, taxonomic, and thematic extent of data, for describing research methods and protocols, for describing the structure and content of data within sometimes complex packages of data, and for precisely annotating data with semantic vocabularies.” https://eml.ecoinformatics.org/ Taxonomic Concept Transfer Schema (TCS) “A schema to allow the representation of taxonomic concepts as defined in published taxonomic classifications, revisions and databases. It specifies the structure for XML documents to be used for the transfer of defined concepts. Currently, this standard is not followed widely.” https://www.tdwg.org/standards/tcs/ Table 2. International standards for citizen science data. https://www.tdwg.org/standards/ac/ https://www.tdwg.org/standards/abcd/ https://www.tdwg.org/standards/tcs/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 38 (Baker et al. 2021). Observer biases can also be ac- counted for in the post-collection stage through data filters and models that account for levels of contribu- tor expertise and using AI-based techniques to reduce biases in model training data (Johnston et al. 2018; Chen and Gomes 2019; Steen et al. 2019). Data that pass through the validation stages need to be curated. This involves processing raw data in terms of end-user requirements, ensuring that data meet reproducibility standards (for analyses), and lending themselves well to being combined with oth- er standardized datasets. If the end-use of the data includes re-use or integration, data credibility can be increased by doing analyses on sampling approach- es and quality and triangulating against other data sources. It is ideal to store citizen science data in the most disaggregated form with minimal privacy con- cerns and documented data quality assurance proto- cols (de Sherbinin et al. 2021; Downs et al. 2021). Licensing Licensing is necessary to ensure that media contributed by users to a citizen science project is used appropriately and data is appropriately cited. Copyright is a state-guaranteed right covering ‘work’ that includes intellectual creations, such as text, photographs, diagrams, maps, movies, etc. Ideas, knowledge, information, or data are traditionally not copyright-protected, and scientists have traditionally been content with being cited for their original work (Hagedorn et al. 2011), to facilitate public access and dissemination of knowledge. Although it is common- ly assumed that data with no license applied is free for unrestricted use, this is not the case. The lack of a license poses ambiguity in its reuse, especially where the data usage terms have to be made explicit, es- pecially for commercial usage (Groom et al. 2017), and may lead to unwitting copyright violations. Data must be made available under carefully crafted li- censes where the terms and conditions for its reuse are made clear. Adopting open, machine-readable licenses are recommended to meet the FAIR data principles dis- cussed earlier (de Sherbinin et al. 2021). The most common license employed in citizen science data is the Creative Commons License (CC15). This license seeks to find a ‘balance between public and private interests, and between the free flow of expressions of ideas and knowledge and state-guaranteed control and monopolies’ (Hagedorn et al. 2011). Creative Commons licenses are not an alternative to copyright 15 https://creativecommons.org/. and work alongside copyright, enabling one to modi- fy copyright terms to best suit their needs. A violation of a CC license is a copyright violation. The CC li- censes provide standardized terms-of-use definitions that have been adapted for various jurisdictions and upheld in court in several countries (Hagedorn et al. 2011). The license has been adapted for India under the aegis of Wikimedia India, Centre for Internet and Society, and Acharya Narendra Dev College16. CC licenses by default allow people to reuse, re- mix and adapt original works while still providing attribution to the original author. However, it under- stands that no single license can cover all use cases and instead offers a set of licenses to cover a wide range of use cases (Table 3). The CC licenses are accordingly adopted in whole or part by large data repositories such as the Global Biodiversity Informa- tion Facility (GBIF17 ), Wikipedia, and Wikimedia Commons, among others. CC0, CC‐BY, and CC‐BY‐ NC are the only CC license options recommended by GBIF. As scientific data is primarily facts and is not copyrightable, CC0 is the recommended license for data18. If any media are contributed as part of the data, the terms of use of the platform gathering the data should be clear on the applicability of the CC license to such media as well. Another such relevant license is the Open Data Commons (ODC) maintained by the Open Knowl- edge Foundation19. Although Open Data Commons licenses are more suitable for data licensing, they are more specific to databases and apply only to database frameworks and structures, not to the particular con- tent within a database. It allows for the “distinction between the data (base) and material (content) gen- erated from it (“produced works”)”. ODC provides three types of licenses (Table 4). India’s open government data initiative started with the notification of the National Data Sharing and Accessibility Policy (NDSAP), by the Depart- ment of Science and Technology to the Union Cab- inet in 2012 and the subsequent launch of the Open Government Data Platform India. The recommended licenses to be used for datasets published under ND- SAP through the OGD platform remained unspeci- fied until the release of the Government Open Data 16 https://wiki.creativecommons.org/wiki/india. 17 https://www.gbif.org/. 18 https://wiki.creativecommons.org/wiki/CC0_use_for_data. 19 https://opendatacommons.org/. https://creativecommons.org/ https://wiki.creativecommons.org/wiki/india https://www.gbif.org/ https://wiki.creativecommons.org/wiki/CC0_use_for_data https://opendatacommons.org/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 39 Icon Right Description Attribution (“BY”) Is a part of all CC licenses and requires users to give appropriate attribution to the creators of a work Share-Alike (“SA”) Allows the distribution of derivative works, but requires that all such works must also be shared under the same conditions ensuring that more restrictive licenses are not applied to derivatives. No Derivative Works (“ND”) States that the user “may not alter, transform, or build upon this work” Non-Commercial (“NC”) States that one “may not use this work for commercial purposes” Types of CC licenses Icon Name Applicable rights CC BY22 Attribution CC BY-SA23 Attribution-ShareAlike CC BY-ND24 Attribution-NoDerivatives CC BY-NC25 Attribution-NonCommercial CC BY-NC-SA26 Attribution-NonCommercial-ShareAlike CC BY-NC-ND27 Attribution-NonCommercial-NoDerivatives Table 3: Creative Commons rights and licenses for data. The Creative Commons logo and icons used are from Wikipedia21 and are under the public domain. Name Applicable rights Open Data Commons Open Database License28 (ODbL) Attribution Share-Alike Open Data Commons Attribution License29 (ODC-By) Attribution Open Data Commons Public Domain Dedication and License30 (PDDL) Public Domain (All rights waived) 2021222324252627 20https://en.wikipedia.org/wiki/Creative_Commons_license. 21https://creativecommons.org/licenses/by-sa/4.0. 22https://creativecommons.org/licenses/by-nd/4.0. 23https://creativecommons.org/licenses/by-nc/4.0. 24https://creativecommons.org/licenses/by-nc-nd/4.0. 25https://opendatacommons.org/licenses/odbl/. 26 https://opendatacommons.org/licenses/by/. 27 https://opendatacommons.org/licenses/pddl/. Table 4: Open Data Commons licenses. https://en.wikipedia.org/wiki/Creative_Commons_license https://creativecommons.org/licenses/by-sa/4.0 https://creativecommons.org/licenses/by-nd/4.0 https://creativecommons.org/licenses/by-nc-nd/4.0 https://opendatacommons.org/licenses/odbl/ https://opendatacommons.org/licenses/by/ https://opendatacommons.org/licenses/pddl/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 40 License - India28, which is governed by Indian law. It allows end-users to “use, adapt, publish (either in original, or in adapted and/or derivative forms), translate, display, add value, and create derivative works (including products and services), for all lawful commercial and non-commercial purposes.” However, the terms of the license remain ambigu- ous and have been criticized for being incomplete in many aspects, such as privacy and accountability of data providers (Kodali 2017). In addition, it is possible to set up custom be- spoke licenses for a citizen science project. However, this is not a trivial endeavor and will almost certain- ly have to include legal offices and organizational research departments (Ball 2011). Such cases are usually unnecessary considering the availability of standard licenses as documented above, except when exceptional circumstances require the same. Creat- ing additional bespoke licenses adds to the burden on end-users of the data in ensuring compliance and adhering to multiple license requirements. Once a suitable license has been decided upon, one must attach that license to the data. This main- ly involves putting out a statement that the data is released under the chosen license or public domain and a mechanism for retrieving the full text of the license itself. The rights statement must be displayed prominently to avoid ambiguity and confusion. Add- ing the rights statement within downloaded zip files in an RDF/XML format for machine recognition is highly recommended (Ball 2011). Ethical Considerations at This Stage Some of the key ethical considerations in the stage of data acquisition include clear, prior commu- nication with potential participants before collecting data, information on data licenses, encouraging par- ticipants to contribute data collected following fair practices, and legal and social conformity to data be- ing incorporated from indigenous communities. Pri- or communication regarding objectives of a project, terms of data usage, methods for data storage, rec- ognition of participant roles, etc., should be commu- nicated to participants at one or more stages during project implementation. Before participation, partici- pant consent should be sought to ensure that contrib- uted data are not misused, and participants are aware of data licenses. While participant data-usage trends can be used to communicate about project develop- ments, unauthorized usage of participant personal in- 28 https://data.gov.in/government-open-data-license-india. formation should be prevented (Sullivan et al. 2014). Project managers need to be aware of region- al laws and other legal components that govern the usage of information pertaining to indigenous com- munities. Traditional knowledge must be handled sensitively and collected only after receiving con- sent from indigenous groups. National laws related to copyright or protection of imagery and text nar- ratives must be well understood before accessing such data and complied with. Efforts must be made to ensure project participants abide by government and community laws and regulations while accessing contributed data. In the case of biodiversity projects, the safety of biodiversity and participants should be a priority over data collection. Gaming elements are often used in citizen sci- ence projects to positively influence participant en- gagement by creating an environment of fun, com- petition or both (Bowser et al. 2013; Iacovides et al. 2013). Gamification may reward participants for attaining high scores and can enhance user participa- tion. However, it may also demotivate participants who do not achieve competitive targets, distract par- ticipants from scientific data collection, trigger unfair practices to inflate competitive scores, and withhold ‘winning’ information, conflicting with citizen sci- ence principles of open data. Skills and resources re- quired to participate in games may create inequity by putting some participants at an advantage over oth- ers. Ponti et al. (2018), note that game design and its influence on participant strategies, contributor values and motivations, and acknowledgment of participa- tion outside of the gaming context are essential as- pects of citizen science gamification. At the data-validation stage, artificial intelli- gence techniques such as deep learning and con- volutional neural networks are now used in citizen science projects to classify images, especially for species identification. AI applications may be used to automatically classify visual, acoustic, and spatial information through learning algorithms that utilize vast datasets and extract and organize images from social media (Lamba et al. 2019; August et al. 2020). Here, ethical challenges arise when black-boxed ar- tificial intelligence systems are trained with citizen science contributed open data but exclude citizens from understanding how their data contributions are used. Transparency in such AI systems is essential and can help detect biases in training datasets, thus improving the efficacy of these systems. For ex- ample, eBird’s human/computer learning network https://data.gov.in/government-open-data-license-india Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 41 (Kelling et al. 2012) is cited as an example of such a transparent system (McClure et al. 2020). What To Do With the Data Once data begins accumulating, considerations related to data storage, processing, analysis, and dis- semination in standardized and verified forms and ensuring its longevity have to be implemented. Data Infrastructure Managing physical risks associated with data storage, ensuring security, and ensuring access to data across its lifecycle is critical. The USGS data lifecycle model recommends that such security mea- sures cover “raw and processed research data, origi- nal science plan, data management plan, data acqui- sition strategy, processing procedures, versioning, analysis methods, published products, and associated metadata” (Faundeen et al. 2014). The Bouchout Declaration for Open Biodiversity Knowledge Management29, which aims “to promote free and open access to data and information about biodiversity by people and computers and to bring about an inclusive and shared knowledge manage- ment infrastructure,” lists some core principles that are vital towards data perseverance: ● An agreed infrastructure, standards, and pro- tocols to improve access to and use of open data; ● Persistent identifiers for data objects and physical objects such as specimens, images, and taxonomic treatments with standard mechanisms to take users directly to content and data; ● Tracking the use of identifiers in links and citations to ensure that sources and suppliers of data are assigned credit for their contributions; ● Registers for content and services to allow discovery, access, and use of open data; ● Linking data using agreed vocabularies, both within and beyond biodiversity, that enable par- ticipation in the Linked Open Data Cloud. These approaches to data lifecycle management point to implementing various strategies pertaining to aggregation and processing of data for analysis, transformative action, and tracking usage. Multiple techniques in deploying persistent identifiers and URLs, Digital Object Identifiers, LifeScienceID, and Personally Identifiable Information are adopt- ed across platforms to ensure that data in its various 29 http://www.bouchoutdeclaration.org/declaration/. types and stages are traceable. Implementation of such persistent identifiers will become a norm soon and will help ensure data quality, access and accred- itation. Trustworthy data repositories like Zenodo/Dry- ad, Mendeley Data, among others, could be consid- ered for storing citizen science data that has been curated for research quality. Developed as part of efforts from Research Data Alliance, a set of har- monized common requirements for certification of research data repositories certifies that these remain trustworthy (CoreTrustSeal Standards And Certifica- tion Board 2019). For occurrence data, global repos- itories like GBIF, eBird, and IBP in India could act as apt data repositories to ensure the perpetuity of data. While many such data repositories are evolving with Long Term Ecological Observatories30 and oth- er state-sponsored initiatives, it is pertinent to note the significance of archiving citizen science initia- tives with their raw data and the context within which they are conducted (Williams et al. 2018). This will ensure the dual goals of securing the perpetuity of citizen science data and maximizing re-use. Such public data archiving for citizen science initiatives are required but a challenge to build (Pearce‐Higgins et al. 2018). Data Standards Data standards play an important role in biodi- versity data publishing. Following data standards makes publishing either through aggregators like GBIF or in the form of data papers simple. It saves effort in describing metadata and makes published data readily usable for the intended user base. Data papers and data repositories often require the meta- data to be marked up in standardized formats such as EML. Independent projects may use software such as R or Morpho31 to markup the metadata from their datasets. Many larger platforms like iNaturalist or IBP serve as an archive and a publishing platform. They already have some standardization inbuilt within their structure, allowing data downloads to be served under such standards. Such platforms also have arrangements on publishing the data to global biodiversity repositories such as GBIF through com- mon standards. Data Accessibility As stated earlier, Open Access data means that “data must be freely available for download online.” 30 https://lteo.iisc.ac.in/. 31 https://old.dataone.org/software-tools/morpho. http://www.bouchoutdeclaration.org/declaration/ https://lteo.iisc.ac.in/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 42 This also implies that the data is accessible in for- mats that do not need proprietary software to open and must have an open license for reuse. The CSV format is generally used for tabular data download and ensures compatibility for machine-reading of the data in a machine-readable format. Many sites require prior registration or serve download requests via a user’s registered email. Im- posing registration for data downloads is an accepted means of tracking data usage, ensuring compliance with the project’s policies and the site’s data licens- ing. From the accessibility perspective, there is a need to involve citizens beyond the mere act of data collection and provide them with opportunities and incentives to interact with the data they have generat- ed. Participants are rarely given opportunities beyond data collection, such as data analysis or interpretation (Kennett et al. 2015; Lukyanenko et al. 2016). Activ- ities such as data consumption influence learning and conservation outcomes and may lead to better user retention in the project (Cooper et al. 2017). Such in- teraction can be achieved through participatory data analysis and visualization that can be user-generat- ed as per their needs and variables of interest. Many projects are increasingly gravitating towards devel- oping such interactive visualizations for participant engagement. However, since data analysis is usually an end-user’s specific perspective, generic visualiza- tions and analyses inbuilt into portals may be limited as they are usually set up to predefined criteria. Such limitations can be overcome through developing and offering APIs and client packages for popular data analysis software such as R or Python. Some exam- ples of such packages are the ‘rgbif’32 and ‘pygbif’33 clients for interfacing with GBIF and the ‘galah’ R package34 for acquiring data from the Atlas of Living Australia. This capability would allow users to fetch data flexibly, do further analysis and generate custom visualizations as per their needs. Dissemination of Knowledge Gathered Through Citizen Science Citizens should not be viewed only as data con- tributors in the scientific endeavor; they are also the end-users in many situations. While the purpose of a citizen science project may vary (publishing a scien- tific paper, data repositories, outreach to the public, etc.), knowledge generated through citizen science 32 https://cran.r-project.org/web/packages/rgbif/index.html. 33 https://github.com/gbif/pygbif. 34 https://atlasoflivingaustralia.github.io/galah/index.html. must find its way back to its contributors. Citizen science participation can be enhanced by incorporating clear channels of communication and data dissemination (Vohland et al. 2021). This allows access to a wide audience, makes people aware of the project, and keeps them in continued engagement with the project. The traditional means of dissemi- nating scientific knowledge through publication in peer-reviewed journals can often be too technical for the lay public to understand. Involving the pub- lic in science is one of the core principles in citizen science, and hence knowledge should also reach the public in a digestible manner. This can be done through activities such as creating data visualizations to communicate results attractively e.g., eBird Status and Trends abundance animations that reveal migra- tory pathways of birds35.; writing articles in popular media sources like newspapers, magazines, and on- line magazines; visual communication of knowledge through art, videos, and graphic design and using so- cial media to disseminate results. It is worth noting that disseminating knowledge to the public is crucial to ensure long-term participa- tion and collaboration in any citizen science program through various means, targeting multiple stakehold- er communities. However, excessive emails or other means of contacting participants can adversely affect and discourage participation. Data Attribution Attribution is the act of giving credit to data providers during publication. Author attribution has historically been a tricky issue across disciplines and this has only been accentuated with the advent of big data and data papers with proper guidelines on giving authorship not being stabilized even today (Venka- traman 2010; Escribano et al. 2018). While protocols such as the Science Commons advocate publishing data openly, there is no mention of providing attri- bution. Authors typically negotiate their order within the author list, assuming that the first author is the most coveted and has led the publication idea. The last typically is the head of the lab and the point of contact (Venkatraman 2010). As the contributor list grows, especially in large collaborative projects, the contribution order becomes less understandable and meaningless. Some journals provide a separate text or list stating individuals’ roles and contributions in- stead of authorship. There is much ambiguity in citizen science as to who should get attributed and how and whether indi- 35 https://ebird.org/science/status-and-trends/abundance-animations. https://cran.r-project.org/web/packages/rgbif/index.html https://github.com/gbif/pygbif https://atlasoflivingaustralia.github.io/galah/index.html https://ebird.org/science/status-and-trends/abundance-animations Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 43 vidual citizens will be acknowledged in publications. The Joint Declaration of Data Citation Principles (Crosas 2013), states that when cited, there should be ‘legal attribution to all contributors to the data, but recognizes that a single style or mechanism of attri- bution may not be applicable to all data’. However, large datasets or data involving many contributors, such as citizen science data, are prone to the issue of ‘attribution stacking’ where cit- ing every person involved in the generation of the dataset may become unwieldy and difficult to man- age. This issue is further magnified when citizen sci- ence and other data from multiple projects are com- bined for further use. Ensuring the correct citation formats are maintained manually or by machines it- self becomes challenging. To tackle this, it becomes necessary to allow for ‘lightweight attribution mech- anisms’ (Ball 2011). In this context, it is also worth considering that citizens may be less likely to be motivated by citation in academic journals as against acknowledgment of their contribution that is visible to their local peers and that projects should support attribution in a way that matters to the citizen scientists. Some sites, such as ebird, provide the option to hide user names and anonymize them. However, in such cases, attribution for the data is not provided to the contributor for ap- parent reasons. Attribution and user privacy are inter- linked, and setting conditions on one of these usually has inverse effects on the other. Data Policy Having clear and robust data policies is a means of ensuring that the data collected through citizen science projects are stored, shared, attributed, and utilized ethically. Citizen science project proponents should be mindful of different stakeholders, from contributors to end-users of data and data policies, of the differing rights and responsibilities that each party may possess. While the definition of citizen sci- ence is still evolving, it generally encompasses par- ticipation from individuals without specific scientific training who participate as volunteers in activities. Such activities may cover the breadth of the data life, including study design, data collection and analysis, and dissemination of results (Guerrini et al. 2018). This information is then used in ways that may or may not be fully understood by volunteers, and so informed consent must be obtained from volunteers on how the data will be used and what credit they will receive for it. Informed consent and refusal are some of the essential components of research ethics that the volunteer willingly gives themselves up for use as a resource (Reiheld and Gay 2019). Informed consent can be ensured by using easy-to-understand documents with minimal text and ensuring participants have agreed to the project terms. It is advisable to place the documents in a con- spicuous place on the portal. These documents are a collection of guidelines that constitute the project’s policies that determine how a citizen science project and the users, a website, or a citizen science volunteer may interact or transact. Such documents are usually presented as different types of formalized policy doc- uments (Bowser et al. 2013). These include: Terms of use - These form the conditions that a user is expected to know and accept before they begin using the portal. It also encompasses guide- lines for acceptable behavior between the user and the portal. Terms and conditions may be ex- plicit, requiring the user to accept and consent to the site’s terms before proceeding with registra- tion and usage (clickwrap), or it may be implicit, assuming that the user agrees to the terms sim- ply by continued use of the portal (browsewrap). The terms and conditions set out the conditions of usage of the portal, covering aspects along the lifecycle of the data. It indicates the portal’s stand on data ownership, data access, reuse, and providing attribution to users or recommended citation policies. Clarity on aspects of data own- ership, including any media uploaded by the user, is imperative. Further, the terms need to specify how owners of the site will use the data. It would also need to indicate terms of being contacted for communication regarding outreach or marketing purposes, acceptance of terms and conditions of any third party website linked to the portal (such as YouTube or Google Maps), liability clauses that protect the owner of the portal from any inap- propriate content posted on the website by a third party and indemnity clauses against harm caused to any third party from the content of the portal. It would be beneficial to list all the activities that are prohibited on the portal, similar to what is ob- served in the European Citizen Science portal36. Additional terms of use may allow the portal to block a user in case they violate the terms of use. In some countries, the project may need to clarify if it is merely an intermediary where the adminis- 36 https://eu-citizen.science/terms/. https://eu-citizen.science/terms/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 44 trator does not initiate the transmission by posting the information or select who will be able to view the information or make changes to the informa- tion, thereby claiming exemption from liability arising out of the conduct of its users. Otherwise, the portals should safeguard themselves from po- tential legal liability through clear terms of use for all classes of users with clear contracts. Legal policies - This would cover informa- tion on how the site deals with the legal aspects such as its obligations to national or local laws, liabilities of the project, disclaimers, and waivers. It is best practice to include or link to texts con- taining specific legal or non-legal documentation. Privacy policies - This covers information on how and what kind of information the project gathers from participants, including information collected during registration, data upload, and how such information is saved, used, and kept confidential. It would also need to disclose the usage of cookies, whether for functionality within the portal such as for login and role-based permis- sions or through the usage of features provided by third-party sites such as social media networks or advertising providers. Privacy Concerns in Citizen Science Much attention has been paid to privacy con- cerns about citizen science data involving medical and genetic information participants. However, data obtained as part of biodiversity inventories or eco- logical phenomena may also require close perusal for violations of the privacy rights of participants and federal laws that prevent sharing of sensitive in- formation that could jeopardize the safety of endan- gered species. When collecting biodiversity-related informa- tion, privacy breaches can occur at two levels: ● Personal information of the observer ● Georeferenced data associated with a species record being contributed Most projects collect basic personal informa- tion of participants, such as names, email IDs, and addresses to keep them informed of the progress of the project. Through these mediums citizen science projects wittingly or unwittingly end up with person- ally identifiable information (PII) of participants in their projects. Additionally, smartphones equipped with tools that utilize cameras, audio-recorders, and location-capturing applications to capture biodiver- sity-related information often end up revealing PII (Cartwright 2016), that may reveal near real-time in- formation about their locations, patterns of daily or weekend travel, types of phones used, etc. Geo-locations of species, commonly required by biodiversity inventories, may reveal sensitive infor- mation related to endangered species. Information on the location of species could lead to poaching, un- ethical collection, or disturbance through excessive attention from nature enthusiasts and photographers. This is particularly important when dealing with range-restricted, endangered, frequently traded, or breeding populations of uncommon species. Although participants are generally aware of these issues while contributing data (Bowser et al. 2013), it is still imperative to get informed consent and brief them on the terms of service employed by the project. A recent study showed that 51% of proj- ects that did not focus exclusively on people data often overlooked the fact that they were still collect- ing PII (Cooper et al. 2019). The Personal Genome Project37 (PGP) has been globally acclaimed for its approach to informed consent that transcends tradi- tional boundaries. The project proponents ensure that all participants pass an examination that tests their knowledge of genomic science and privacy issues. After that, they sign access to their personal and ge- nomic data for the project (Angrist 2009). The US and the EU have implemented legal pro- visions to safeguard the privacy of citizen science contributors. Under the US privacy laws, citizen science project managers are mandated to make us- ers aware of their rights and are provided with the Privacy Act Statement. Under the Children’s Online Privacy Protection Rule, collection of personal in- formation of children below the age of 13 is illegal; and the Freedom of Information and the Privacy Acts require cleansing all personal information of partic- ipants from data collected by projects supported by the federal government before such databases are made public. In the EU, the General Data Protection Regulation (GDPR) seeks the right to be informed, the right of access, the right to rectification, the right to erasure, the right to restrict processing, the right to data portability, the right to object and rights around automated decision making and profiling. Under GDPR, project managers are mandated to get fully informed consent from contributors and inform them of how data contributed by them would be used. 37 https://www.personalgenomes.org/. https://www.personalgenomes.org/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 45 Such existing and upcoming legal provisions have potential implications for the privacy of participants in Citizen Science portals (Ganzevoort et al. 2017). Conclusions Including the above considerations, every proj- ect has to consider its unique situation in terms of biodiversity such as between the explored and un- explored, the documented and undocumented, con- servation threats, along with specific challenges to discover, document, and disseminate. Each design is significant on its own, reflecting the needs of the socio-ecological system that information technology has to integrate into and co-evolve. Although citizen science is rapidly gaining pop- ularity, data generated through it still deals with a perceived “image problem” regarding data quality. While the debate around this issue rages, several studies have indicated that with the appropriate data quality checks in place, citizen science data is no less reliable than data gathered by experts (Jordan et al. 2012; Ganzevoort et al. 2017). The sole objective of a citizen science project is not necessarily data. Through the duration of the project, it builds the ca- pacity of its participants and inculcates the spirit of scientific endeavor and discovery while also sensi- tizing participants towards species and habitat con- servation, creating a sense of stewardship towards nature. Another challenge with citizen science is ensur- ing sustained participation both from citizens and scientists to help validate the data (Irwin 2018). From this perspective, imposing too much rigor in data col- lection and quality can reduce inclusivity and lead to reduced participation. As one of citizen science’s objectives involves broader participation, holding participants to unrealistic scientific standards could mean missing out on opportunities to “fully engage with people in the core objective of discovery” (Luk- yanenko et al. 2016). Multiple competing citizen science initiatives operating within the same region and data sharing between various sources often result in duplication of data contributed in multiple places. This issue will need attention and effort to identify and de-duplicate. Global aggregators such as GBIF are already invest- ing effort in algorithms to identify potentially related records and cluster them. Identifying individual con- tributors across portals such as through an ORCID id can also help in these efforts, although this is still not widely used beyond the academic community yet. To conform to the expectations of its varied user bases, citizen science has to meet the dual objectives of providing high-quality summarized data to the general public as well as spatially, temporally, and taxonomically explicit data to the research commu- nity. These have to be achieved while protecting sen- sitive information and providing privacy protection. Achieving these objectives requires significant in- vestment in technology solutions, clear data policies, and transparency. Anhalt-Depies et al. (2019), give a set of recommendations that may be apt to cater to data quality, privacy, transparency, and trust in citi- zen science. These include constant communication and consultation with stakeholders, addressing vol- unteer needs on aspects such as data sharing and user privacy through clear policy documents that evolve through iterative evaluation based on user feedback. Among other resources, we refer readers to the 10 Principles of Citizen Science developed by the Euro- pean Citizen Science Association, which set out the key principles that underlie good practice in citizen science38. In the Indian context, it would be ideal for en- visaging a directory of citizen science projects and a repository for citizen science projects, which could allow design, host, store, and archive initiatives. This is necessitated by the nature of present-day data in- frastructures, which are stretched to provide the full set of features for citizen science practitioners to engage through all the stages of the data lifecycle. Many act as platforms for data collection, organiza- tion, and aggregation but for various reasons focus less on providing tools to analyze collected data by citizen science practitioners. Given the immense potential to contribute to biodiversity monitoring at different scales, a culture of integration covering various tenets of biodiversity information, technical design, and stakeholder networks needs to be pro- moted (Kühl et al. 2020). This is truer for small, fo- cused, and independent citizen science projects for which there is a dire need in a mega-diverse country like India. Technology and data infrastructures need to evolve in a direction where modular, decentral- ized, and federated architectures are imagined and attempted. Such architectures will help address the spatial, temporal, and taxon bias and empower com- munities in sensitive socio-ecological systems to par- ticipate in biodiversity conservation effectively. Such infrastructure could help transform data infrastruc- ture into knowledge infrastructures, helping enhance 38 https://eu-citizen.science/about/. https://eu-citizen.science/about/ Thomas Vattakaven et al. – Best Practices for Data Management in Citizen Science 46 the biodiversity knowledge commons and shape pol- icy and practice. Acknowledgments We are grateful to Suhel Quader, Pankaj Sekh- saria, Farida Tampal, Shannon Olsson and Prabha- kar Rajagopal, all members of the Organizing Com- mittee of CitSci India for conceptualizing the idea of this working group and providing guidance and feedback at various stages of developing this toolkit. We thank Akshata Pradhan for facilitating the func- tioning of the working group. Mridula Vijairagha- van, Sushmitha Viswanathan (Wildlife Conservation Society- India), and Shyama Kuriakose (Wildlife Conservation Society-India) vetted the legal com- ponents of this document for accuracy and provided additional inputs. 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