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649 results for “Citizens”
Skills of science inquiry in citizen science - Datasets
<p>The dataset provides results of a prediction task aimed at predicting the presence of science inquiry skills in CS project descriptions. Only 2939 English project descriptions from the database were used for prediction and the results indicated 438 projects (around 15%) consist of one or more skills of science inquiry that we were interested in. In total 20 different types of science inquiry skills were considered for this study. </p> <p>See further detail in D2.2 section 7.4.</p> <p><strong>Content and grouping: </strong></p> <ul> <li> <p>The dataset contains the following details: Platform ID (from which platform the CS project descriptions were retrieved), Project Title (Name of the CS project), How many times each science inquiry skill (keywords) was mentioned in each project description.</p> </li> </ul>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p>Corresponding dataset for the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The structure of the dataset is as follows:</p> <p><strong>Annotations</strong> </p> <p><em>List of annotations made per day for each of the analysed projects.</em></p> <p><code>annotations.csv </code></p> <p><strong>Comments </strong></p> <p><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></p> <p><code>comments.csv </code></p> <p><strong>Rolechanges</strong> </p> <p><em>List of roles per user to determine number of role changes </em></p> <p><code>478_rolechanges.csv</code></p> <p><code>1104_rolechanges.csv</code></p> <p><code>...</code></p> <p><strong>Totalnetworkdata</strong> </p> <p><em>Network data (edge and node sets) for the given projects (without time slices).</em></p> <p>Edges </p> <ul> <li> <p><code>478_edges.csv</code></p> </li> <li> <p><code>1104_edges.csv</code></p> </li> </ul> <p>Nodes </p> <ul> <li> <p><code>478_nodes.csv</code> </p> </li> <li> <p><code>1104_nodes.csv</code> </p> </li> </ul> <p><strong>Trajectories</strong> </p> <p><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></p> <p>478 </p> <ul> <li>Edges <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> </ul> </li> <li> <p>...</p> </li> <li>Nodes <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> <p> </p> <p>1104 </p> <ul> <li> <p>Edges </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p>Nodes </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><code>...</code></p> </li> </ul> <p> </p>
Datasets containing the results from the analysis on SDGS and eHealth inside the Citizen Science Community on Twitter
<p>This datasets contain the results from our analyses of the Citizen Science Community on Twitter. These analyses have been done to better understand the discussion about SDGs, eLearning and eHealth.</p> <p><strong>T</strong>he purpose of sharing these datasets is to provide the basis to reproduce the results reported in the associated deliverable. These files are not raw data, since due to privacy concerns we can not share personal information from Twitter.</p> <p><strong>dominant_topics_anonym.xlsx</strong>: Excel datasheet. This dataset contians the distribution of the most discussed topics inside the SDGs discussion.</p> <p><strong>Edges_Hashtag_connected.csv</strong>: CSV file. This dataset contains the edges to build the network of connected hashtags. This edges can be used to build a network and explore the connections or to statiscally analyse the results.</p> <p><strong>hashtags.csv</strong>: CSV file. This dataset contains the results of the most used hashtags in the analysis about eLearning. <br> </p> <p><strong>hashtags_treemap_health.xlsx</strong>: Excel datasheet. This dataset contains the results of the most frequent hashtags in the eHealth analysis.</p> <p><strong>ldavis_prepared_ieee17.html</strong>: HTML file. This file contains the Intertopic distance map and most salient terms from the topic modelling analysis done in the SDGs conversation study.</p> <p><strong>Most_retweeted_accounts.xlsx</strong>: Excel datasheet. This dataset contains the top 20 users that receive more retweets in the conversation around eHealth. The column called Indegree refers to the topological value calculated from the network of retweets. This indegree is equivalent to the number of retweets received. On the other hand, Outdegree is the opposite, so number of retweets given to others.</p> <p><strong>Most_retweeting_account.xlsx</strong>: Excel datasheet. This dataset presents the opposite part of the previous one, the accounts that retweet the most from the eHealth analysis. The columns contain the same indicators: Indegree and Outdegree.</p> <p><strong>sdgs_count_publish.csv</strong>: CSV file. This dataset contains the number of tweets assigned to the different SDGs from the analysis done on the conversation about these Goals.</p> <p><strong>sdgs_tweets_sdgsaccess.xlsx</strong>: Excel datasheet. Same file as the previous one in other format to ease the handling in Excel.</p> <p><strong>top_hash_health.xlsx</strong>: Excel datasheet. The most used hashtags inside the conversation about eHealth.</p> <p><strong>topics_tweets_sdgsaccess.xlsx</strong>: Excel datasheet. Tweets by topic extracted using Machine Learning in the SDGs analysis.</p> <p> </p> <p>This repository will receive updates in the future in order to present all the data available and publishable from the different analysis that were described.</p>
Availability of information on citizen science activities, checked against the Activities & Dimensions Grid of Citizen Science on the basis of some projects
<p>The research resulting in this report aimed at answering the following questions:</p> <ul> <li> <p>Which information on citizen science activities is online available that matches the Activity & Dimension Grid of Citizen Science or goes beyond it? </p> </li> <li> <p>Is there any contradictory information?</p> </li> <li> <p>What can be the reason for the availability or non-availability of information about citizen science activities?</p> </li> <li> <p>How does/could this impact on the CS Track’s recommendations?</p> </li> </ul> <p>The corresponding dataset consists of the results of a keyword-based search in the WP2 project database. The information retrieval resulted in 3318 projects on which information is available in German or English.</p> <p>More information on this research can be found in D2.2 section 3.2.</p>
CS Track Citizen Science Survey Data 2021
<p>CS Track is launching a survey to gather citizen scientists’ (16 year old and older) perspectives on activities and forms of participation, learning and knowledge-building in citizen science (CS) projects. The aim of CS Track is to broaden our knowledge about CS and the impact CS activities can have. CS Track will do this by investigating a large and diverse set of CS activities, disseminating best practices and formulating knowledge-based policy recommendations in order to maximise the potential benefits of CS activities for individual citizens, organisations and society. This multi-perspective approach will allow us to shed light on the role of citizen science in society and social attitudes and emerging cultures in communities that engage with science and technology challenges.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.csv</strong>: CSV File. CS Track Citizen Science Survey Data in a CSV file.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.xlsx</strong>: Excel datasheet. Same file as previous in an Excel datasheet.</p> <p><strong>CSTrack_Citizen_Science_Survey_Final.pdf</strong>: PDF-file. CS Track Citizen Science Survey.</p> <p> </p> <p> </p>
CSI-COP Citizen Scientists App Investigations
<p>This open-access dataset presents project results from CSI-COP project work package 4 task T4.2 in which citizen scientists conducted investigations on apps in their smart devices. The App investigations dataset was created using Excel spreadsheet. Users can explore the information recorded by the citizen scientists by using the filters on the column headings, for example, on type of app (games; news, etc.), or language of app (including English, Greek, Hebrew).</p>
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
The Effect of Soundscape Composition on Bird Vocalization Classification in a Citizen Science Biodiversity Monitoring Project
<p>This archive includes sound clips (.wav files) and associated mel-scale spectrograms of bird vocalizations for 54 species in Sonoma County, California, USA. These data were used for training and validating convolutional neural network (CNN) models for bird species detection. We also include xeno-canto training and validation mel spectrograms used to pretrain CNNs. Details on these data are explained in the paper by Clark et al. (2023) titled "The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project". These data are available for use without restrictions, with no warranty on data quality or utility for a given application. We request that any work that does use these data cite the Clark et al. (2023) paper.<br> <br> Clark, M.L., Salas, L., Baligar, S., Quinn, C., Snyder, R.L., Leland, D., Schackwitz, W., Goetz, S.J., Newsam, S. (2023). The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. <em>Ecological Informatics</em>. <a href="https://doi.org/10.1016/j.ecoinf.2023.102065">https://doi.org/10.1016/j.ecoinf.2023.102065</a></p> <p>Associated code for training CNN models, performing inference, and applying post-classification corrections can be found in the GitHub archive <a href="https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species">https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species</a></p> <p>Raw sound data from the Soundscapes to Landscapes project are available upon request: Dr. Matthew Clark, matthew.clark@sonoma.edu</p> <p>These data were collected as part of the Soundscapes to Landscapes project (<a href="https://soundscapes2landscapes.org/">soundscapes2landscapes.org</a>), funded by NASA’s Citizen Science for Earth Systems Program (CSESP) 16-CSESP 2016-0009 under cooperative agreement 80NSSC18M0107.<br> <br> ----------------------------<br> This depository includes the following archives:</p> <ul> <li> <p>mel_specs.zip: contains 2-sec mel spectrograms split into training (“tr”), validation (“val”), testing (“test”) data for each target bird species (n = 54) used to fine-tune the CNNs. Select spectrogram files are appended with “aug” if they are augmented versions for the training data.</p> </li> <li> <p>wav.zip: contains the associated wav-format sound recordings used to generate the training, validation, testing mel spectrograms found in mel_specs.zip.</p> </li> <li> <p>Xeno-canto_pretrain.tar: contains 2-sec mel spectrograms split into training and validation data for 40 bird species used for CNN pre-training that were generated using a warbleR segmentation methodology described in the paper. The sound files used to generate these mel spectrograms came from the Kaggle competition, <a href="https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset">https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset</a><br> Mel spectrogram naming reflects the XC number used for cataloging on Xeno-canto in the format XC123456_2.png. The six numbers following the XC characters can be used to search for unique recordings on Xeno-canto (<a href="https://xeno-canto.org/">https://xeno-canto.org/</a>) using the search query “nr:123456” in the search tool or queried using the Xeno-canto API (<a href="https://xeno-canto.org/explore/api">https://xeno-canto.org/explore/api</a>). Unique recording names can be extracted from the mel spectrogram filenames.</p> </li> <li> <p>soundscape_test_wavs.zip: the wav-format sound recordings used to perform soundscape testing.</p> </li> </ul>
Citizen Perception of the NBS in Valladolid city
<p>Citizens’ perceptions, both individuals and communities, are essential when evaluating the well-being benefits from urban green spaces (Kothencz et al, 2017). Public and stakeholder perceptions of urban nature, and specifically the quality or functionality of nature, are critical to our understanding of the “value” people place on local environments (Priego et al., 2008).</p> <p>Exploring visitors’ perceptions of green spaces is challenging as it depends on cognitive, affective, and behavioural components and, therefore, sensory perceptions are individually different (Kothencz et al, 2017).</p> <p>This KPI measures identified green space characteristics by the two following well-being variables and one geolocation variable:</p> <ul> <li>Green space visitors’ level of satisfaction. Directly related with the urban green space (UGS) quality.</li> <li>Self-reported quality of life (QoL).</li> <li>Frequency of green space visitors’ crowd-sourced geo-tagged data in NBS sites.</li> </ul> <p>Visitors’ level of satisfaction and perceived QoL contributions of UGS are key individual-level measures that are subjectively affected by area-based green space characteristics.</p> <p>This KPI will reflect on how people assess change in their local environments in terms of satisfaction, quality of life and citizens’ presence of urban green space (UGS) at a site (NBS), neighbourhood and city scale.</p>
Data from: Trends in butterfly populations in UK gardens – new evidence from citizen science monitoring
<p>This data package describes the annual abundance indices and trend estimates for 22 butterfly species in UK gardens for the period 2007-2020.</p> <p>These data form the basis of the results presented in: Plummer, K.E., Dadam, D., Brereton, T., Dennis, E.B., Massimino, D., Risely, K. et al. (2023) Trends in butterfly populations in UK gardens—New evidence from citizen science monitoring. <em>Insect Conservation and Diversity</em>, 1– 13. Available from: <a href="https://doi.org/10.1111/icad.12645">https://doi.org/10.1111/icad.12645</a></p> <p>Please refer to the paper for an explanation of the underlying BTO Garden BirdWatch (GBW) data and modelling protocols used to produce the datasets included here.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers
<p>Raw data (occurrences and environmental predictors) used in the manuscript "Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers"</p>
Data from Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus
These data include annotations for life stage, mating behavior, and plant part occupancy of large milkweed bug observations in North America as well as information about climate and environment.
DNR Citizen Monitoring Lake Level Data from 2010 to 2015 in Wisconsin
The lake level data from the DNR citizen monitoring project. This ongoing project has been collecting lake level data from 20 lakes in 11 counties in Wisconsin since 2010. It features the citizen voluntary action in the monitoring work. The data are hosted by Surface Water Integrated Monitoring System so its short name in this dataset is SWIMS.
Water quality data collected by the Citizen-Led Environmental Observatory (CLEO) from multiple nearshore sites in Lake Lillinonah, Connecticut, USA, 2010-current
Included in this data package are water quality data from the Citizen-Led Environmental, a Observatory (CLEO) volunteer water quality monitoring program run by Friends of the Lake (FOTL) and Fairfield University at Lake Lillinonah, Connecticut, USA. The program has been operational since 2008 (data available 2010-current). Trained volunteer monitors collect data from dock locations across the lake on water temperature, Secchi disk depth, water color, presence of floating woody debris, recreation potential, trash, particle type and surface scum. Volunteers collect data between 3:00 and 7:00 PM three times per week from Memorial Day through Labor Day. In addition to the variables listed above, CLEO volunteers collect routine water samples on a biweekly basis, as well as any time there is a notable algal bloom. The routine samples are analyzed for nutrient (total nitrogen and total phosphorus) concentrations as well as concentrations of the cyanobacterial toxin microcystin. The blooms samples are analyzed for microcystin only. These data are available in EDI packages EDI568 (nutrients) and EDI569 (toxins).
Total nitrogen and total phosphorus concentrations from surface water samples collected by the Citizen-Led Environmental Observatory (CLEO) from multiple nearshore sites in Lake Lillinonah, Connecticut, USA, 2011-current
Included in this data package are water quality data from the Citizen-Led Environmental Observatory (CLEO), a volunteer water quality monitoring program run by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University at Lake Lillinonah, Connecticut, USA. The program has been operational since 2008 (data available 2011-current). Trained volunteer monitors collect surface water samples from multiple nearshore sites twice a month from Memorial Day through Labor Day. These samples are analyzed for total nitrogen and total phosphorus concentrations. Water samples are also analyzed for levels of the toxin microcystin. Additionally, CLEO volunteers collect data on water temperature, Secchi disk depth, water color, presence of floating woody debris, recreation potential, trash, particle type and surface scum every 1-3 days during the same period. These additional data are available in EDI packages EDI567 (general water quality) and EDI569 (toxins).
Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?
<p>Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @</p> <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> <p>Answers to the online survey in "Call for ideas_answers online_survey.xlsx"</p> <p>Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf</p> <p> </p>
Biological soil covers: data on lichen, bryophyte and algae coverage in soils gathered by SoilSkin citizen science program using eBryoSoil app for smartphones
<p>Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change.</p> <p>The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play <a href="https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US">https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US</a>) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded.</p> <p>The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats:</p> <ul> <li>Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%.</li> <li>Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%.</li> <li>Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall.</li> <li>Grassland – Habitat characterized by herbaceous plants.</li> <li>Agricultural land – Habitat characterized by temporary or woody crops.</li> <li>Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s.</li> <li>Urban green spaces – Habitat characterized by a landscape in which man-made structures are present.</li> </ul> <p>The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers).</p> <p>The data file contains the following columns:</p> <ul> <li>Date: numerical variable indicating the “day”/”month”/”year” when the register was generated.</li> <li>User_ID: categorical variable with the identification number of the user who gathered the record.</li> <li>Transect: categorical variable with the identification of the number of the transect.</li> <li>Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect.</li> <li>Photo_label: character string with the identification of the photograph from each record.</li> <li>Register_localization: categorical variable with the identification of the geographic area where the record was done.</li> <li>Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees.</li> <li>Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees.</li> <li>Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS.</li> <li>Habitat_type: categorical variable with the description of the main type of habitat of the sampling location.</li> <li>Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record.</li> <li>Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record.</li> <li>Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record.</li> <li>Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record.</li> <li>Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record.</li> <li>Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record.</li> <li>Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record.</li> <li>Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record.</li> <li>Algae: integer, variable indicating the percentage of algae cover quantified in the record.</li> <li>Soil: integer, variable indicating the percentage of soil visible in the record.</li> </ul> <p> </p>
Explaining non-adoption of electronic government services by citizens. A study among non-users of public e-services in Latvia
<p>This data was collected as part of the H2020 Citadel project, http://www.citadel-h2020.eu, project no. 726755. The objective was to analyse citizen motives for not using electronic government services. Using interviews among users of Citizens´ Service Centres in Latvia, the data is used to analyses the motives of citizens not to use electronic government services but to rely on non-electronic equivalents or on in-person assistance. Findings and fieldwork details are available in D2.1 of this project.</p>
EU HORIZON2020 CSI-COP Anonymous dataset on Citizen scientists
<p>This dataset complements CSI-COP deliverable report D4.3: '<strong>CSI-COP Citizen Scientists Age, Gender, Socio-Economic and Geographical (AGSEG) Distribution Report</strong>'. </p><p>Researchers can review the dataset and analyse anonymous data on who the citizen scientists were in the EU Horizon2020 funded CSI-COP project with citizen scientists investigating GDPR compliance in websites and apps.</p>
SeaPaCS graphic elaboration of the Protocol for marine micro-plastic collection and monitoring in citizen science and for building a L.A.D.I. trawling tool
<p>This is a graphic elaboration (in Italian) of the protocol "SeaPaCS deliverable - protocol for plastic monitoring in citizen science" in English and Italian is a deliverable of the SeaPaCS project (Participatory Citizen Science Against Marine Pollution), funded by IMPETUS (project ID 101058677). The protocol and the visual elaboration has been freely adapted from "<i>LADI and the Trawl</i>" by Coco Coyle with Melissa Novaceski, Emily Wells and Max Liboiron, as published by the Civic Laboratory for Environmental Action Research, August 2016. The graphic elaboration (as the protocol) in both languages, consists of three parts: 1) how to build a DIY low cost manta trawl device (LADI - Low-Tech Aquatic Detection Debris Instrument) to monitor plastic pollution, adjusted to materials availability and costs in Italy; 2) how to monitor (the sampling itself and towing procedure); and 3) how to categorize plastic debris back on land. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.