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zenodo44/100

Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes

Background Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc. A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation). <a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a> Methodology This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden. This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7). Data description The current data product covers seven data files. (1) Agents' experienced activity plans File name: 1_activity_plans.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> act_id </td> <td> Activity index of each agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> deso </td> <td> Zone code of Demographic statistical areas (DeSO)1 </td> <td> String </td> <td> - </td> </tr> <tr> <td> POINT_X </td> <td> Coordinate X of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> POINT_Y </td> <td> Coordinate Y of activity location (SWEREF99TM) </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> act_purpose </td> <td> Activity purpose (work, home, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> mode </td> <td> Transport mode to reach the activity location (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> dep_time </td> <td> Departure time in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> trav_time </td> <td> Travel time to reach the activity location </td> <td> String </td> <td> hour:minute:second </td> </tr> <tr> <td> trav_time_min </td> <td> Travel time in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> speed </td> <td> Travel speed to reach the activity location </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> distance </td> <td> Travel distance between the origin and the destination </td> <td> Float </td> <td> km </td> </tr> <tr> <td> act_start </td> <td> Start time of activity in minute (0-1439) </td> <td> Integer </td> <td> minute </td> </tr> <tr> <td> act_time </td> <td> Activity duration in decimal minute </td> <td> Float </td> <td> minute </td> </tr> <tr> <td> act_end </td> <td> End time of activity in decimal hour (0-23.99) </td> <td> Float </td> <td> hour </td> </tr> <tr> <td> score </td> <td> Utility score of the simulation day given by MATSim </td> <td> Float </td> <td> - </td> </tr> </tbody> </table> 1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a>   (2) Travel trajectories File name: 2_input_zip Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> time </td> <td> Time in second in a simulation day (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation2 </td> <td> String </td> <td> - </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> link </td> <td> Nearest road link consistent with (5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> vehicle </td> <td> Vehicle ID identical to person </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table> 2 One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)   (3) Overnight charger access File name: 3_home_charger_access.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> </tbody> </table>   (4) Car fleet composition File name: 4_car_fleet.csv <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> income_class </td> <td> Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> </tbody> </table>   (5) Road network with slope information File name: 5_road_network_with_slope.shp (5 files in total) <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> length </td> <td> The length of road link </td> <td> Float </td> <td> meter </td> </tr> <tr> <td> freespeed </td> <td> Free speed </td> <td> Float </td> <td> km/h </td> </tr> <tr> <td> capacity </td> <td> Number of vehicles </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> permlanes </td> <td> Number of lanes </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> oneway </td> <td> Whether the segment is one-way (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> modes </td> <td> Transport mode (car) </td> <td> String </td> <td> - </td> </tr> <tr> <td> link_id </td> <td> Link ID </td> <td> String </td> <td> - </td> </tr> <tr> <td> from_node </td> <td> Start node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> to_node </td> <td> End node of the link </td> <td> String </td> <td> - </td> </tr> <tr> <td> count </td> <td> Aggregated traffic (number of cars travelled per day) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> slope </td> <td> Slope in percent from -6% to 6% </td> <td> Float </td> <td> - </td> </tr> <tr> <td> geometry </td> <td> LINESTRING (SWEREF99TM) </td> <td> geometry </td> <td> meter </td> </tr> </tbody> </table>   (6) Simulation scenarios specifying the parameter sets File name: 6_scenarios.txt <table> <tbody> <tr> <td> Parameter set (paraset) </td> <td> Strategy 1 </td> <td> Strategy 2 </td> <td> Strategy 3 </td> <td> Fast charging power (kW) </td> <td> Minimum parking time for charging (min) </td> <td> Intermediate charging power (kW) </td> </tr> <tr> <td> 0 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 1 </td> <td> 0.2 </td> <td> 0.2 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 2 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 150 </td> <td> 5 </td> <td> 22 </td> </tr> <tr> <td> 3 </td> <td> 0.3 </td> <td> 0.3 </td> <td> 0.9 </td> <td> 50 </td> <td> 5 </td> <td> 22 </td> </tr> </tbody> </table>   (7) Time history of travel trajectories and charging of the simulated BEVs File name: 7_output.zip Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days. <table> <tbody> <tr> <td> Column </td> <td> Description </td> <td> Data type </td> <td> Unit </td> </tr> <tr> <td> person </td> <td> Agent ID </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> home_charger </td> <td> Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes) </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> car </td> <td> Car model class (B=40 kWh, C=60 kWh, D=100 kWh) </td> <td> String </td> <td> - </td> </tr> <tr> <td> seq </td> <td> Sequence ID of time history by agent </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> time </td> <td> Time (0-86399) </td> <td> Integer </td> <td> Second </td> </tr> <tr> <td> purpose </td> <td> Valid for activities (home, work, school, other) </td> <td> String </td> <td> - </td> </tr> <tr> <td> type </td> <td> Event type defined by MATSim simulation </td> <td> String </td> <td> - </td> </tr> <tr> <td> link </td> <td> Link ID (link_id in File 5) </td> <td> String </td> <td> - </td> </tr> <tr> <td> distance_driven </td> <td> Cumulative driven distance in the simulation day </td> <td> Float </td> <td> km </td> </tr> <tr> <td> energy_1 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 1) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_2 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 2) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> energy_3 </td> <td> Energy consumed while driving (-) or charging (+) (Strategy 3) </td> <td> Float </td> <td> kWh </td> </tr> <tr> <td> charger_1 </td> <td> Power rating of the charger (Strategy 1) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_2 </td> <td> Power rating of the charger (Strategy 2) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> charger_3 </td> <td> Power rating of the charger (Strategy 3) </td> <td> Float </td> <td> kW </td> </tr> <tr> <td> soc_1 </td> <td> State of charge (0-1, Strategy 1) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_2 </td> <td> State of charge (0-1, Strategy 2) </td> <td> Float </td> <td> - </td> </tr> <tr> <td> soc_3 </td> <td> State of charge (0-1, Strategy 3) </td> <td> Float </td> <td> - </td> </tr> </tbody> </table>

opencc-by-4.0Feb 2023View details →

zenodo44/100

Data from the OPERAS business models survey on open access books

OPERAS (the European Research Infrastructure for the development of open scholarly communication in the social sciences and humanities) has conducted a survey of publishing organisations throughout Europe to identify and better understand existing and potential business models to support the Open Access publication of research monographs. The results of the survey are used to inform the formulation of recommendations about how to create a sustainable open access book publishing ecosystem within Europe. The survey was designed to serve two core aims:  1. To further, better or improve our understanding of the scholarly publishing landscape and of the challenges that publishers face in the context of publishing OA monographs; 2. To identify main trends (including opportunities and challenges) and the knowledge of collaborative funding and infrastructure models in OA publishing in SSH.  The survey was open between 16 February and 14 April 2021. The results are presented in two versions of the white paper of the Open Access Business Models Special Interest Group:  Stone, Graham, Błaszczyńska, Marta, Lebon, Chloé, Morka, Agata, Mosterd, Tom, Mounier, Pierre, Proudman, Vanessa, Speicher, Lara, & Melin&scaron;čak Zlodi, Iva. (2021). Collaborative models for OA book publishers (1.0). Zenodo. https://doi.org/10.5281/zenodo.5494731 and the second version to be published in Spring 2023.

opencc-by-4.0Feb 2023View details →

zenodo44/100

Open Soil Atlas (OSA) - Raw data from Phase I (Mar-Aug 2021, Berlin)

The Open Soil Atlas citizen science project collected 77 data observations, cathegorized in a set of 10 inficators, which offer information about the quality and fertility of the soil. Area of investigation: Berlin. Data collection period: March-August 2021.

opencc-by-4.0Sep 2021View details →

zenodo44/100

New Particle Search at CERN open classification data

This is a reduced, anonymised dataset containing the classifications made in the New Particle Search at CERN demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.

opencc-by-4.0Mar 2023View details →

zenodo44/100

Cosmic Muon Images open classification data

This is a reduced, anonymised dataset containing the classifications made in the Cosmic Muon Images demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.

opencc-by-4.0Mar 2023View details →

zenodo44/100

GWitchHunters open classification data

This is a reduced, anonymised dataset containing the classifications made in the GWitchHunters demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.

opencc-by-4.0Mar 2023View details →

zenodo44/100

Deep Sea Explorers open classification data

This is a reduced, anonymised dataset containing the classifications made in the Deep Sea Explorers demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.

opencc-by-4.0Mar 2023View details →

zenodo44/100

Linked Open Data Management Services: A Comparison

Thanks to a variety of software services, it has never been easier to produce, manage and publish Linked Open Data. But until now, there has been a lack of an accessible overview to help researchers make the right choice for their use case. This dataset release will be regularly updated to reflect the latest data published in a comparison table developed in Google Sheets [1]. The comparison table includes the most commonly used LOD management software tools from NFDI4Culture to illustrate what functionalities and features a service should offer for the long-term management of FAIR research data, including: <ul> <li>ConedaKOR</li> <li>LinkedDataHub</li> <li>Metaphacts</li> <li>Omeka S</li> <li>ResearchSpace</li> <li>Vitro</li> <li>Wikibase</li> <li>WissKI</li> </ul> The table presents two views based on a comparison system of categories developed iteratively during workshops with expert users and developers from the respective tool communities. First, a short overview with field values coming from controlled vocabularies and multiple-choice options; and a second sheet allowing for more descriptive free text additions. The table and corresponding dataset releases for each view mode are designed to provide a well-founded basis for evaluation when deciding on a LOD management service. The Google Sheet table will remain open to collaboration and community contribution, as well as updates with new data and potentially new tools, whereas the datasets released here are meant to provide stable reference points with version control. The research for the comparison table was first presented as a paper at DHd2023, Open Humanities – Open Culture, 13-17.03.2023, Trier and Luxembourg [2]. [1] Non-editing access is available here: <a href="http://docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link">docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link</a> To get editing access contact the authors. [2] Full paper will be made available open access in the conference proceedings.

opencc-by-4.0Mar 2023View details →

zenodo44/100

Compilation of open asset-level data, as of Dec 2022

This dataset is a compilation of open asset-level data, which means the location of sites (e.g., operation, manufacturing, processing facilities of global supply chains), as of December 2022. This included data from 9 publicly available sources, that after data cleaning and harmonization, resulted in 189,075 data points.  <table> <tbody> <tr> <td>Data source</td> <td>Number of data points</td> </tr> <tr> <td><a href="https://opensupplyhub.org/">Open Supply Hub (former Open Apparel Registry)</a></td> <td>96,736</td> </tr> <tr> <td><a href="https://datasets.wri.org/dataset/globalpowerplantdatabase">Global Power Plant Database</a></td> <td>35,419</td> </tr> <tr> <td><a href="https://climatetrace.org/downloads">Climate trace</a></td> <td>19,945</td> </tr> <tr> <td><a href="https://www.fda.gov/drugs/drug-approvals-and-databases/drug-establishments-current-registration-site">FDA database</a></td> <td>12,898</td> </tr> <tr> <td><a href="https://www.globaldamwatch.org/database">Global Dam Watch</a></td> <td>11,017</td> </tr> <tr> <td><a href="https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-manufacturing-practice/eudragmdp-database">EudraGMDP database</a></td> <td>5,181</td> </tr> <tr> <td><a href="https://www.cgfi.ac.uk/spatial-finance-initiative/geoasset-project/geoasset-databases/">Sustainable Finance Initiative GeoAsset Databases</a></td> <td>4,716</td> </tr> <tr> <td><a href="https://tailing.grida.no/disclosures">Global Tailings Portal</a></td> <td>1,956</td> </tr> <tr> <td><a href="https://www.fineprint.global/resources/mining-database/">Fine print Mining Database</a></td> <td>1,207</td> </tr> </tbody> </table> This data was assigned with the industry in which the asset is. The summary table below shows the number of assets by industry. <table> <tbody> <tr> <td>Industry</td> <td>Number of assets        </td> </tr> <tr> <td>Textiles, Apparel & Luxury Good Production             </td> <td>96,736</td> </tr> <tr> <td>Health Care, Pharma and Biotechnology</td> <td>18,079</td> </tr> <tr> <td>Energy - Solar, Wind</td> <td>16,282</td> </tr> <tr> <td>Energy - Hydropower</td> <td>14,515</td> </tr> <tr> <td>Energy - Geothermal or Combustion</td> <td>11,724</td> </tr> <tr> <td>Metals & Mining</td> <td>11,210</td> </tr> <tr> <td>Transportation Services</td> <td>4,872</td> </tr> <tr> <td>Construction Materials</td> <td>3,117</td> </tr> <tr> <td>Agriculture (animal products)</td> <td>2,388</td> </tr> <tr> <td>Agriculture (plant products)</td> <td>1,896</td> </tr> <tr> <td>Oil, Gas & Consumable Fuels</td> <td>1,194</td> </tr> <tr> <td>Water utilities / Water Service Providers</td> <td>892</td> </tr> <tr> <td>Hospitality Services</td> <td>294</td> </tr> <tr> <td>Fishing and aquaculture</td> <td>14</td> </tr> <tr> <td>Other</td> <td>5,862</td> </tr> </tbody> </table> Note that this compilation is based on an extensive search, however, we acknowledge that there is a significant discrepancy in data coverage/comprehensiveness among the different industries. The industry "Textiles, Apparel & Luxury Good Production" is by far the most complete, while other are clearly far from complete, for example, “Construction Materials”, "Agriculture (animal products)”, “Agriculture (plant products)”, “Oil, Gas & Consumable Fuels”, “Water utilities / Water Service Providers”, “Hospitality Services”, “Fishing and aquaculture”. Therefore, any comparison between industries should take this coverage/comprehensiveness bias into consideration.

opencc-by-4.0Mar 2023View details →

zenodo44/100

Value creation stories anonymized open data set (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)

# Title Immunization Agenda 2030 (IA2030) 1st Movement Full Learning Cycle (FLC 2022) – “How are you doing?” Value Creation Stories Survey (Version 1.0) # Research audience Education researchers interested in the application of the “value creation stories” (VCS) conceptual framework elaborated by Etienne Wenger et al. in the study of communities of practice and other types of digital communities. # Credits ## Author The Geneva Learning Foundation 18 avenue Louis Casaï CH-1209 Geneva, Switzerland research@learning.foundation ### Principal Investigator and corresponding author Reda Sadki, The Geneva Learning Foundation (TGLF) reda@learning.foundation ## Project partners Bridges to Development University of South Australia Centre for Change and Complexity in Learning (C3L) ## Roles and responsibilities - Design: The Geneva Learning Foundation - Implementation (sample collection): The Geneva Learning Foundation - Processing: The Geneva Learning Foundation, Bridges for Development, Centre for Complexity and Change in Learning (C3L) - Anonymization: The Geneva Learning Foundation and Bridges for Development - Data cleaning: Bridges to Development - Submission: The Geneva Learning Foundation ## Funding sources or sponsorship that supported the data collection Wellcome, Bill & Melinda Gates Foundation (BMGF) ## Recommended citation The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: 10.5281/zenodo.7763922 # Description of the sample ## File list: This file is IA2030_FLC_2022_Value_Creation_Stories.README.md IA2030-EN_FLC_2022_Value_Creation_Stories-questions_mapping.csv : List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023.  IA2030-EN_FLC_2022_Value_Creation_Stories.csv : Dataset Response of participants that replied in English. (n: 2101, obs:5601) - Version 1: Geneva Learning Foundation, 31 March 2023.  IA2030-FR_FLC_2022_Value_Creation_Stories-questions_mapping.csv: List of the survey’s questions and their code in English as well as their unit. (21 questions) - Version 1: Geneva Learning Foundation, 31 March 2023. IA2030-FR_FLC_2022_Value_Creation_Stories-Google_translation.csv: Dataset Response of participants that replied in French translated to English using “Google Translate” (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023. IA2030-FR_FLC_2022_Value_Creation_Stories.csv: Dataset Response of participants that replied in French (n: 1585, obs:4493) - Version 1: Geneva Learning Foundation, 31 March 2023. Relationship between files:  The questions codes data set are the same code as the column variables and can be connected. ## Relationship between files The questions codes data set are the same code as the column variables and can be connected. ## Related data sets This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC). The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities.  Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit [insights@learning.foundation](insights@learning.foundation) ## Other publicly accessible locations of the data The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/ ## 1. Purpose and Objectives ### Primary goal of the survey: This survey had two goals in the context of TGLF’s IA2030 Movement Full Learning Cycle programme (2022):  1. Provide an asynchronous mechanism for support between peers (participants) and from the TGLF team; and 2. collect and measure programme participants’ value creation stories (VCS) during the programme. Martin de Laat’s “value creation stories” (VCS) has been used primarily in small-scale, qualitative studies of communities of practice, online forums, and education activities. This data set includes both quantitative (Likert) and qualitative (open text) responses to the VCS questions, collected over a period of four months (7 March – 20 June 2022) from a cohort that began with 6,185 participants on the start date. ## 2. Population and Sample The target population were participants of the Geneva Learning Foundation’s Movement for Immunization Agenda 2030 (IA2030) learning programme. The initial cohort admitted to the programme was 6,185 individuals from 99 countries. Only participants who were formally admitted to the programme received the invitation to complete the survey. Programme participants were free to choose if and when to report (self-selection), and their responses were not checked against any other measures (self-reporting). ### Languages: French and English ## 3. Survey Design and Methods Data collection period: 7 March 2022 – 20 June 2022 Between 7 March and 20 June 2023, participants in the Geneva Learning Foundation’s “Immunization Agenda 2030” (IA2030) Movement Full Learning Cycle (FLC) were asked to respond to a questionnaire titled “How are you doing?”. Participants received a personalized email with the request to share feedback about their experience during the week. The link to share feedback was also included in other reminder and information emails sent in response to participant needs. The first survey was launched on the 11 of March 2022 and the last at 17 of March 2022, totalizing 15 requests. Participants could answer the survey at any time and as many times that they wished.   The group of 6,185 participants grew over the course of the Cycle, as additional participants were able to join the initiative throughout the four-month period. ### Software- or Instrument-specific information needed to interpret the data - Automated translation of French data was performed using [Google Translate](https://translate.google.com/?sl=en&tl=fr&op=docs) - Methods used for removing or anonymizing personal identifiers or sensitive information: - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site [Miracle Salad](https://www.miraclesalad.com/webtools/md5.php.).Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing.*Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.** - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.) The macro did not account for typos, in case any country information is found please contact: [research@learning.foundation](research@learning.foundation) ### Data collection start and end dates: 7 March 2023 until 20 June 2023 #### Events or circumstances during data collection that may have influenced results: No requests for responses were sent during TGLF’s “Term break” between 16-30 April 2022. ## 4. Data Processing and Cleaning - Incomplete or inconsistent responses: Not cleaned, as respondents were able to opt out of specific sections of survey or skip questions. - Data transformations or imputations: None - Treatment of outliers or extreme values: None ## 5. Variables and Measures The survey included Likert scale questions and qualitative open texts based the conceptual framework for Value Creation Stories (VCS) developed by Wenger et. al. (2011). There are no derived or calculated variables. Items are Likert scale, multiple choice, and open text. ## 6. Data Quality and Reliability All the responses done before or after the FLC period (7 March – 20 June 2022) were excluded of the sample. ## 7. Data Privacy and Anonymization ### Methods used for removing or anonymizing personal identifiers or sensitive information: - Unique identifier: Unique identifiers were anonymized using MD5 Hashing via the web site https://www.miraclesalad.com/webtools/md5.php. Unique identifiers can be used to identify respondents who may have answered the survey more than once, at different points in time. This approach provides a method to anonymize sensitive data using MD5 hashing. - Limitation: MD5 hashing is a one-way function; it is not possible to dehash the data and recover the original information.  - Macros developed in Excel to replace Country names in qualitative responses. (No country information were collected in this survey, but some respondents referred to their specific contexts in their responses.) ## 8. Data Availability and Accessibility This data set is made available on Zenodo.org in the Zenodo community “Movement for Immunization Agenda 2030 (IA2030)” https://zenodo.org/communities/ia2030/ Requests for additional information should be addressed to research@learning.foundation. This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the 1st IA2030 Full Learning Cycle (FLC). The complete data set is more comprehensive, and includes: demographic information (gender, country), health system information (respondent’s health system level), respondents’ analyses of challenges and priorities. ### Other publicly accessible locations of the data The Geneva Learning Foundation publishes data sets in relation to its Immunization Agenda 2030 (IA2030) Movement learning programme in the Zenodo open repository community: https://zenodo.org/communities/ia2030/ ### Related data sets Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from The Geneva Learning Foundation (TGLF) Insights Unit insights@learning.foundation ## 10. Ethical Considerations ### Ethical guidelines followed during data collection: TGLF’s research abides by the principles of the Cantonal Commission for Research Ethics (CCER), the Federal Law on Research on Human Beings (RS 810.30), Swiss Human Research Act (HRA) and the Ordinance on Organisational Aspects of the Human Research Act (HRA Organisation Ordinance, OrgO-HRA) ### Informed consent and participant rights information: In order to join TGLF’s IA2030 Full Learning Cycle programme, participants had to confirm their agreement to use of their responses “for research, learning, evaluation, communication, and advocacy, in line with the Foundation’s mission”. Participants were able to opt out of the VCS questions by selecting “No” when asked “Could we ask you five questions about your participation?”. They were informed these questions were asked in order to “share your feedback in the next weekly Assembly”, the weekly synchronous meeting for programme participants. The rationale for sharing such feedback was also explained; “Your contribution will help everyone understand how we are doing as a group, and also help us to better support you.” Data protection and confidentiality Consent was requested during the application and submitted of action plan period for sharing data, in line with the Geneva Learning Foundation’s data protection and confidentiality policy. # Copyright and license The Geneva Learning Foundation © 2022. This data set and all associated files are licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) © The Geneva Learning Foundation 2023 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International; https://creativecommons.org/licenses/by-nc-sa/4.0/. Under the terms of this license, you may copy, redistribute and adapt the data set for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this data set, there should be no suggestion that the Foundation endorses any specific organization, products or services. The use of the Foundation logo is not permitted. If you use the data set, then you must license your work under the same or equivalent Creative Commons license. If you create a translation of this data set, you should add the following disclaimer along with the suggested citation: “This translation was not created by the Geneva Learning Foundation. The Foundation is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition.” Any mediation relating to disputes arising under the license shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization. General disclaimers. The designations employed and the presentation of the data set do not imply the expression of any opinion whatsoever on the part of the Foundation concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the Foundation in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by the Foundation to verify the information contained in this data set. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the Foundation be liable for damages arising from its use. This data set contains individual views and does not necessarily represent the decisions or the policies of the Foundation. Version 1.0 (31 March 2023): reviewed internally; reviewed externally.  # References Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum. Wenger, E., Trayner, B., de Laat, M., 2011. Promoting and assessing value creation in communities and networks: a conceptual framework (Rapport No. 18). Oitpen Universiteit, Ruud de Moor Centrum. Victoria J. Marsick, Rachel Fichter, Karen E. Watkins, 2022. From Work-based Learning to Learning-based Work: Exploring the Changing Relationship between Learning and Work, in: The SAGE Handbook of Learning and Work. SAGE Publications. Watkins, K.E., Sandmann, L.R., Dailey, C.A., Li, B., Yang, S.-E., Galen, R.S., Sadki, R., 2022. Accelerating problem-solving capacities of sub-national public health professionals: an evaluation of a digital immunization training intervention. BMC Health Serv Res 22, 736. https://doi.org/10.1186/s12913-022-08138-4 Watkins, K.E., Kim, K., 2019. Measuring the Impact of the WHO Scholar Programme Courses for Immunization (2016-2018) (Evaluation report). University of Georgia at Athens, Athens, United States. Watkins, K.E., Bhattarai, A., 2019. Analysis of the Impact Accelerator Launch Pad Individual Acceleration Reports in July 2019. University of Georgia at Athens, Athens, United States. # Questionnaire ## Hello {{fname}} {{lname}}. How are you doing in the Movement for Immunization Agenda 2030? ## Do you need help? Do you want to share your experience? We would like to know how you are doing. - I am doing fine. - I have a problem and need help. - I want to share my experience. ## Tell us more about what you want to share. Be specific and detailed so that we can understand. Share your lessons learned, successes, and challenges. ## Did you complete your action for the week? If you did it, how did it turn out? What did you learn in the process? Did anything surprise you? What will you do next? If you did not complete your action, what will you do differently next week? This is a good way to write your thoughts if you did not get to speak in the last session. You can also record an audio message in the IA2030 Movement Dialogue https://t.me/+-PwJxPpyWfQ0ZjRk or share an idea or practice https://accelerator.wazoku.com/ccc/learning in the Ideas Engine. ## What do you need help with? - I do not know what I am supposed to do - I need help with my IA2030 challenge - I want to catch up - I have poor connectivity - I have a problem with technology - Something else ## Tell us more about the problem you are facing. What have you tried to solve this problem? Where did you get stuck? The more information you provide, the better colleagues will be able to help you. ## Have you tried taking time to read and follow the instructions?  Click here https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022 to access the video tutorials and slide decks on www.learning.foundation https://www.learning.foundation/login.  Use your email email to log in. Don’t remember your password? Click here to recover it https://www.learning.foundation/password/new.  Take the time to read the instructions – and then follow them step-by-step. Do not forget to come back to finish this questionnaire.  ## Do not suffer in silence. It sounds like you should ask for help from your Movement colleagues.  Click here to connect with colleagues https://t.me/IA2030 in the IA2030 Movement Telegram channel. - When you join Telegram, please introduce yourself and explain the problem that you are facing. Your colleagues can only help you if you describe the issue and explain what you have already done to solve it. - We encourage you to share your challenge in the next short session where we share experience and problem-solve. Click here to register https://us02web.zoom.us/j/86171141804, and then come back to finish this questionnaire. - Surely, someone will be able to help you. But you do have to register https://us02web.zoom.us/j/86171141804 and actually show up at the right time! - Poor connectivity? Click here to listen https://podcasts.google.com/feed/aHR0cHM6Ly9saXN0ZW5ib3guYXBwL2YvODRTTFI0eTY5X05h to our low-bandwidth podcast. And then come back to finish this questionnaire. - You can listen to most sessions in our podcast. This is audio-only, like listening to radio on demand. ## Could we ask you five questions about your participation? We will share your feedback in the next weekly Assembly. Your contribution will help everyone understand how we are doing as a group, and also help us to better support you. Yes No ## Participation changed me as a professional (change in skills, attitudes, identity, self-confidence, feelings, etc.). ## Can you explain how participation changed you as a professional? ## Participation affected my social connections (change in the number, quality, frequency, emotions, etc.) ## Can you explain how participation affected your social connections? ## Participation helped my professional practice (get new ideas, insights, materials, procedures, etc.) ## Can you explain how participation helped your professional practice? ## Participation changed my ability to influence my world as a professional (enhance my voice, contribution, status, recognition, etc.) ## Can you explain how participation changed your ability to influence your world as a professional? ## Participation made me see my world differently (change in perspective, new understandings of the situation, redefine success, etc.) ## Can you explain how participation made you see your world differently? ## Do you remain committed to the Movement for Immunization Agenda 2030? You remain a Member even if you are not actively participating. - Yes, and I am actively participating - Yes, but I am not actively participating - No, I wish to leave the Movement ## We are sorry to see you go. Could you let us know what went wrong? What could we have done better to support you? - (Or just hit RETURN to skip.) ## What is the email you are using? We need your email to follow up and respond to what you shared with us. Do not forget to press the SUBMIT button. ## URL redirection upon completion: https://www.learning.foundation/products/movement-for-immunization-agenda-2030-full-learning-cycle-1-march-2022 ## Thank you [fname] [lname] for sharing your feedback. We will share your feedback in the next weekly Assembly. Your contribution will help everyone understand how we are doing as a group, and also help us to better support you. Click here to check http://cal.ae/eudusmw the IA2030 Movement calendar so you do not miss upcoming event

opencc-by-4.0Mar 2023View details →

zenodo44/100

Open-population models for estimating roadkill rates - Data and R Code

Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.

opencc-by-4.0Jan 2023View details →

zenodo44/100

Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"

This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023).  MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia.  The MESWA model is provided in NetCDF format (readable by for example, xarray, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with ParaView (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with Salvus (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).    Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format.  Lastly, we include a list of all receivers used in the creation and validation of MESWA.  This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).   The following table provides a listing of the files in the dataset: <table> <tbody> <tr> <td> File </td> <td> Description </td> </tr> <tr> <td> MESWA.nc </td> <td> MESWA model in NetCDF format </td> </tr> <tr> <td> MESWA.h5 </td> <td> MESWA model in HDF5 format, used by Salvus </td> </tr> <tr> <td> MESWA.xmdf </td> <td> Auxiliary file for MESWA.h5, used to import model into Paraview </td> </tr> <tr> <td> events_project.csv </td> <td> Table of event source parameters for all 327 events considered in the project </td> </tr> <tr> <td> inversion_events_192.csv </td> <td> Table of 192 inversion events  (ASCII comma separated value) </td> </tr> <tr> <td> validation_events_66.csv </td> <td> Table of 66 validation events  (ASCII comma separated value) </td> </tr> <tr> <td> events_receivers_inversion.csv </td> <td> Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value) </td> </tr> <tr> <td> events_receivers_validation.csv </td> <td> Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value) </td> </tr> </tbody> </table>     References Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469   Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a>   Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a>   Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.   Acknowledgements This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration.  This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402

opencc-by-4.0Aug 2023View details →

zenodo44/100

Daily opening stock gas storage data for Great Britain from 2010-10-01 in kWh

version 1.0.3 has data to 2023-08-23, data values are in kWh Original data from: https://www.nationalgas.com/data-and-operations/transmission-operational-data under section Supplementary reports under link ‘Daily storage and LNG operator information (1)’ Please check the licence conditions from National Gas - the data published here is merely combined from different files and parsed into a more useable format.

opencc-by-4.0Aug 2023View details →

zenodo44/100

Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"

In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course. The upload consists of the following .csv files: <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table>

opencc-by-4.0Aug 2023View details →

zenodo44/100

MediaFutures Open Calls Data-Set

The Data-Set includes the data collected through the cascade funding open calls carried out during the H2020 MediaFutures project with the title “MediaFutures, Data-driven innovation hub for the media value chain”. The responsible and innovative use of data is instrumental in today's digitalised media industry. The EU-funded MediaFutures project has addressed this challenge by reshaping the media value chain. It has set up a virtual European data innovation hub to support entrepreneurial and innovative projects. It has also established a participatory inclusive innovation program encouraging synergies between businesses and creators and organised a competition to identify innovative digital entrepreneurs, creatives and data-empowered solutions. By delivering data and experimentation facilities to the winners, the project has showcased and improved their ideas. It has also facilitated the technical, legal, business and sustainability mentoring of businesses and artists and helped them achieve further access to funding. The virtual European data innovation hub has been supported by an international network of European organisations. The project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement 951962. The file contains data from the open calls, webinars, matchmaking events and help-desk activities carried out during the MediaFutures project. It includes data such as the number of applications received, data regarding eligibility and in-eligibility of applications, from which country the applications came, how many projects were evaluated and funded, data on the gender and ethnicity of applicants, etc. For more information and context related to the data, see also the following deliverables published on the MediaFutures website (https://mediafutures.eu/resources/): D1.4: Summary of Calls v1, D1.5: Summary of Open Calls v2, D1.6: Summary of Calls v3.

opencc-by-4.0Oct 2023View details →

zenodo44/100

OneNet project - T9.3 - Spanish demo open data

-Anonymized technical data from flexible resources participating in OneNet Spanish demonstration   -Market results assessed by the local market platforms considering market bids and DSOs requirement for the OneNet Spanish demonstration

opencc-by-4.0Oct 2023View details →

edi44/100

Weather data for the period 2009 to 2022 from the Open Field location at University Farms, Case Western Reserve University

Data from the Open Field weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. From 10/20/2009 to 10/30/2014, the weather station was located at N 41.496883, W 81.436117, when it was relocated to N 41.49759, W81.43738. Data include date/time (in 15-minute intervals), wind speed, wind gust speed, wind direction, air temperature, relative humidity, solar radiation, rainfall, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.

openCC (other)Jan 2023View details →

zenodo40/100

Data for Research Assessment in the Transition to Open Science. 2019 EUA Open Science and Access Survey Results

This database refers to the data collected by the European University Association (EUA) for its Open Science and Access Survey 2019, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html">https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html</a>. The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=174). All information that could lead to the identification of individual universities and higher education institutions was removed from the database (cf. cells highlighted in red). The following files are available: <ul> <li>2019 EUA Open Science and Access Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel)</li> <li>Survey Codebook: includes information on all the variables and their coding.</li> </ul>

opencc-by-4.0Jan 2020View details →

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Data from Open Research Data: SNSF monitoring report 2017-2018

This file collection is part of Open Research Data: SNSF monitoring report 2017-2018 (doi 10.5281/zenodo.3618123). This report gives a first overview on the research data management practices adopted by researchers since the introduction of the SNSF Open Research Data Policy in 2017.  Please cite this data collection as: Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Data from Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618209) Further information is given in the corresponding monitoring report Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618123)   Contact Swiss National Science Foundation (SNSF) Open Research Data group E-mail: ord@snf.ch

opencc-by-4.0Feb 2020View details →

zenodo40/100

Agenda Cultural Alcobendas - Open data website

For this dataset, the data set of Agenda Cultural has been chosen, accessible through the following link: https://datos.alcobendas.org/dataset/2458d605-1fd0-4d16-a1ef-795bc243e152/resource/90836555-54b5-45af-ae67-e7545937f591/download/recurso.json We have chosen Alcobendas open data website: https://datos.alcobendas.org/ as it provides the exposed data sets are offered under open property licenses. For this dataset some of the fields provided by the json web have been selected: Temáticas, Barrio, Nombre del evento, Subtemas, FechaInicio, HoraInicio, FechaFin, HoraFin, Tipos, Perfiles y URL_Ficha.

openodc-byApr 2020View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record