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747 results for “Open Data”
Stakeholders in Croatian Agriculture Open Data Ecosystem
<p>The dataset shows the identified stakeholders in the Croatian agriculture open data ecosystem in the researched literature available on national portals Hrčak (the Portal of Croatian scientific and professional journals) and Dabar (Digital Academic Archives and Repositories). The complex query: "stakeholder" OR "persons" OR "actors" OR "agriculture" OR "agriculture business" OR "farms" OR "agriculture sector" OR "agriculture area" OR "agriculture field" AND "open data" was used for search of the national databases Hrčak and Dabar. The stakeholders identified in the query were classified and grouped into five key stakeholder groups: Agriculture producers/Farmers, Suppliers, Management and Support Organizations, Consumer Organizations/Consumers, Researches and Scientists, and Others.</p>
OneNet Portuguese demonstration - Open Data sets
<p>File containing the open data sets from the Portuguese demonstration of the OneNet project. The file includes the flexibility assets data used for the demonstration, as well as: 1) the data series for the estimation of the accumulated flexibility potential of MV customers (supermarkets) connected at the two substations considered; 2) the consumption and generation forecasts, with generation disaggregated by source; 3) short-circuit current values calculated at the EHV/HV interface level, including the TSO contribution, the DSO contribution and the joint TSO-DSO contribution. </p><p>Scope/objective of the demonstration: Test an optimized procedure for data exchange between the Portuguese DSO and TSO for flexibility and operational planning purposes.</p>
Northeastern Puerto Rico open-canopy and under-canopy temperature and moisture data on an elevational gradient
The data archive is here:https://doi.org/10.2737/RDS-2022-0051 please use this DOI when citing this dataset. This data publication contains monthly means of temperature and moisture data collected from August 2006 through September 2021 from 22 locations along an elevational gradient, from 0 to 1045 meters, in Northeastern Puerto Rico. The higher elevational data are in the Luquillo Experimental Forest (El Yunque National Forest). Five kinds of data are included: air temperature and precipitation measured in the open (not under canopy) at 20 locations, air temperature and soil temperature both measured under the canopy at all 22 locations, and soil moisture under the canopy at 4 locations. Two locations have 3 sites each, measured in different canopy types at the same location. The other 20 locations have one site each, making a total of 26 measurement sites. Data are provided as monthly averages at each site. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Data to "Point-wise correlations between 10-2 Humphrey visual field and OCT data in open angle glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Cirafici, P., Maiello, G., Ancona, C., Masala, A., Traverso, C.E., & Iester M. (in press) Point-wise correlations between Humphrey visual field and OCT data in open angle glaucoma. Eye</p>
Cooperative Proactive resource management for 5G in the unlicensed spectrum open data
<p>The data set consists of the following files:</p> <p><strong>1)COT information:</strong> The channel occupancy time of each channel for the first 5000 measurements. The COT values range from 0 to 1.</p> <p><strong>2)QL decisions uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider uniform traffic generation patterns.</p> <p><strong>3)QL decisions NON uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider non-uniform traffic generation patterns.</p> <p><strong>4)Performance measurements: </strong>The final results of the experiment in respect to the transmit power control and throughput measurements under different QL configurations.</p>
Data for "Systematic Mapping of Open Data Studies: Classification and Trends from a Technological Perspective"
<p>Data used to perform a systematic mapping to classify and analyse existing research on open data from a technological viewpoint from 2006 to 2019. This dataset contains information from six key facets from the collected publications coming from several scientific repositories/databases: publication venue, impact, subject, domain, life-cycle and research type.</p>
A dissymmetric [Gd2] coordination molecular dimer hosting six addressable spin qubits. Open data sets
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1038/s42004-020-00422-w">10.1038/s42004-020-00422-w</a> plus a table with information about how the data were obtained and processed.</p>
Raw and analyzed data for manuscript: "An open-source surface barrier discharge plasma pretreatment for reduced cracking of outdoor wood coatings"
<p><strong>Highlights:</strong></p> <ul> <li>Surface barrier discharges are an affordable and available plasma technology for industrial, laboratory and home-workshop applications.</li> <li>Plasma pretreatments had no impact on the appearance of different protective wood coating for outdoor usage.</li> <li>The weathering performance of outdoor wood coatings improved by plasma, showing less cracks and less biotic factors.</li> </ul>
Fixed-Wing Micro UAV Open Data With Digicam And Raw INS/GNSS - IGN Flight 8
<p>The data set originate from a series of flights conducted with fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications and will also be used in ISPRS workshop on dynamic networks given during the 2021 ISPRS Congress. This is part of a larger series of data that will be released gradually after incorporating user's feedback (e.g., on formats, description,etc.). The data set contains the sensor measurements from GPS, IMU and Camera.</p>
Survey of open data and information seeking in Kenya's Urban Slums and Rural Settlements
<p>This dataset provides survey responses from 240 people surveyed as part of the "Investigating the Impact of Kenya’s Open Data Initiative on Marginalized Communities: Case Study of Urban Slums and Rural Settlements" project.</p> <p>The data, collected in mid-2013 looks at issues of where citizens look for data, and how successful they have been in getting government information from different sources, as well as their awareness of the Kenya open data portal, and their interest in getting information through different digital channels in future.</p> <p>Descriptive statistics have been analysed in the publication "Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating The Kenya's Open Data Initiative in Urban Slums and Rural Settlements", but no further analysis has yet been carried out.</p> <p><strong>Data descriptions</strong></p> <p>The Codebook.csv file lists variable names and the questions asked to elicit each response.</p> <p>JHC-Data.csv contains the results from the questionnaires collected through structured in-person interview in the three locations. The questionnaires were administered at chiefs centres, community resource centres, constituency development fund office and religious centres). The questionnaires were filled in by every 2nd these centres.</p> <p><strong>More information</strong></p> <p>More information on the research project can be found at http://opendataresearch.org/project/2013/jhc</p>
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
ROARMAP Open Access Policy data - Country list
<p>This data is a sub set of a dump from ROARMAP [http://roarmap.eprints.org/] taken on 24th August 2014.</p> <p>ROARMAP is the Registry of Open Access Repository Mandates and Policies, a searchable international registry charting the growth of open access mandates and policies adopted by universities, research institutions and research funders that require or request their researchers to provide open access to their peer-reviewed research article output by depositing it in an open access repository.</p> <p>Number of Open Access policies and a ranking is shown for each country.</p> <p>The data is being used for a series of data visualisations [http://pasteur4oa-dataviz.okfn.org/] for the PATEUR4OA Project [http://pasteur4oa.eu/].</p> <p> </p> <p>PASTEUR4OA (Open Access Policy Alignment Strategies for European Union Research) aims to support the European Commission’s Recommendation to Member States of July 2012 that they develop and implement policies to ensure Open Access to all outputs from publicly-funded research. </p> <p>PASTEUR4OA will help develop and/or reinforce open access strategies and policies at the national level and facilitate their coordination among all Member States. It will build a network of centres of expertise in Member States that will develop a coordinated and collaborative programme of activities in support of policymaking at the national level under the direction of project partners.</p>
Open-data release of aggregated Australian school-level information. Edition 2016.1
<p>The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes:<br /> (a) the names of schools<br /> (b) staffing levels, including full-time and part-time teaching and non-teaching staff<br /> (c) student enrolments, including the number of boys and girls<br /> (d) school financial information, including Commonwealth government, state government, and private funding<br /> (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'<br /> <br /> Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.<br /> <br /> The database was constructed directly from the other included files by running the following command at a command-line prompt:<br /> <em>sqlite3 school-data-2016-1.db < school-data-2016-1.sql</em><br /> Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.<br /> <br /> Briefly, the data are organized as follows.<br /> (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this.<br /> (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'.<br /> (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line).<br /> (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'.<br /> (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages.<br /> (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it.<br /> (7) Geocoding of the schools is not available in this edition.<br /> (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy.<br /> (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.</p>
Data of the Open Access Repository Ranking 2015
<p>The Open Access Repository Ranking 2015 ranks open access repositories from Germany, Austria and Switzerland. Data for the 2015 ranking was partly submitted by the respective repository managers, and partly automatically validated via the OAI interfaces. The OARR team reviewed all submissions assuring the quality and validity.<br> The ranking is based on an open and transparent metric that was developed in accordance with the open access community. This metric is a synthesis of different schemes and studies that surveyed and describe open access repositories.<br> Data includes the 2015 scores and descriptive information on the repositories as well as the 2015 metric.</p>
Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the entire ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a> since git is not suited for handling large changing files. Instead, we provide separate data bundles and cutouts to be downloaded and extracted, as noted in the documentation.</p> <p>The provided <strong>cutouts </strong>are merged spatiotemporal subsets of the European weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V003">CMSAF SARAH-3</a> solar surface radiation dataset for the years 1996, 2010, 2012, 2013, 2019, 2020 and 2023. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p>Solar irradiation data is taken from SARAH-3 while all other weather data is from ERA5.</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul> <p><strong>CMSAF SARAH-3</strong></p> <ul> <li>Pfeifroth, Uwe; Kothe, Steffen; Drücke, Jaqueline; Trentmann, Jörg; Schröder, Marc; Selbach, Nathalie; Hollmann, Rainer (2023): Surface Radiation Data Set - Heliosat (SARAH) - Edition 3, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V003, <a href="https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003" target="_blank" rel="noopener">https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003</a>.</li> <li><strong>Terms of Use:</strong> All intellectual property rights of the CM SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.</li> </ul>
LEAD Madrid Living Lab Open Data
<p>December 2021 to May 2023 monthly list of all services used to conduct daily route optimization, calculate energy consumption, and calculate environmental KPIs in the project.</p>
Closed and Open Eyes EEG Data
<p>Seven volunteers agreed to participate in the study. Their mean age was 29.67 years (range 24 – 56 years). The participants indicated that they did not have hearing or visual impairments. </p><p>Gold cup electrodes were O1 and O2 placed following the 10-20 International System for electrode placement and attached to the subject scalp using a conductive paste. Electrode-skin impedances were checked to be below 15 kΩ at all electrodes. The reference and ground electrodes were placed in the Fp2 and A2 positions, respectively, where the absence of hair facilitates their placement, thus optimizing the setup time and EEG signal quality. </p><p>The signals were captured using the hardware presented in [1], and the signal processing algorithms were executed on a PC using Matlab, allowing us to repeat the simulations offline with different parameters.</p><p>During the experimental sessions, the signals from the two channels were recorded for a total duration of 10 minutes per participant. Specifically, the recording process involved 60 seconds of signal acquisition while the participant had their eyes open, followed by another 60 seconds of signal acquisition while the participant had their eyes closed. To indicate the transition between the two eye states, a sound alert was played for the participant. Once the electrodes had been placed and the impedance checked to be below 15 kΩ, the recordings started without individual calibration for any of the participants. All the experiments were conducted in a sound-attenuated and controlled environment. Participants were seated in a comfortable chair and asked to be relaxed and focused on the task, trying to avoid any distractions or external stimuli. To mimic real-life conditions, the participants were allowed to freely move their gaze during the eye-open tasks, without the requirement of maintaining fixation on a specific point. To reduce possible artifacts, participants were asked not to move or speak during the experiments. After each recording session, data for each subject were visually inspected and the recording was repeated if any of them was corrupted by a high level of noise or artifacts.</p><p>Data is organized in a folder for each subject (S1, S2, S3, etc.). Inside each subject folder, another folder named 'PP' contains the EEG recordings in a .csv file. </p><p>Each .csv file contains 3 columns: timestamp, O1 channel and O2 channel.</p><p> </p><p>[1] Laport F, Dapena A, Castro PM, Iglesias DI, Vazquez-Araujo FJ. Eye State Detection Using Frequency Features from 1 or 2-Channel EEG. Int J Neural Syst. 2023 Dec;33(12):2350062. doi: 10.1142/S0129065723500624. Epub 2023 Oct 12. PMID: 37822240</p>
Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries
<p>This dataset contains data collected during a study <a href="https://www.sciencedirect.com/science/article/pii/S0740624X23000989"><em><strong>"Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries"</strong></em></a> conducted by <em>Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).</em></p> <p>It is being made public both to act as supplementary data for "<em>Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries</em>", Government Information Quarterly*, and in order for other researchers to use these data in their own work. </p> <p>***Methodology***</p> <p>The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. </p> <p>This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.</p> <p>***Description of the data in this data set***</p> <p>The file "OpenDataIndex_<em>2013_</em>2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:</p> <p>1) Global Open Data Index (GODI) (4 editions)</p> <p>2) Open Data Maturity Report (ODMR) (8 editions)</p> <p>3) Open Data Inventory (ODIN) (6 editions)</p> <p>4) Open Data Barometer (ODB) (5 editions)</p> <p>5) Open, Useful and Re-usable data (OURdata) Index (3 editions)</p> <p>6) Open Government Development Index (OGDI) (2 editions)</p> <p>These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.</p> <p>Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.</p> <p>***Format of the file***<br>.xls, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br>CC-BY</p> <p> </p> <p>For more info, see README.txt<br> </p>
RAYUELA - Open Data - Data collected through a serious game created to identify patterns and profiles of young potential victims/perpetrators of cybercrimes.
<p>The data of this dataset have been collected in the pilots carried out by the RAYUELA project in different countries of the European Union. The participants are minors and the game sessions have been carried out in schools and summer camps in a supervised way.</p>
Video 5 - Open Science: why do we need data stewards.
<p><span>An interview on the need of data professionals and Open Science skills with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Jessica Lindvall, Head of Training at SciLifeLab Training Hub; Anne Sophie Fink, Head of Data Management at DeiC (Denmark); and Sally Chambers, Director at DARIAH-EU.</span></p> <p><span>Modern research and technology can require not only large amounts of data, but also good data quality. Ensuring good data quality requires specialized expertise. Data stewards and other data management professionals support researchers with providing and working with good quality data which are also FAIR. Open software, open infrastructures are also important bits in the Open Science puzzle, all of which require funding. The uptake of Open Science depends on widespread Open Science awareness and skills. These require outreach, training and formal education.</span></p>
ScienceDex guides
Understand access before you commit
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.