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747 results for “Open Data”
Open-source DGGS comparison data supplement
<p>A DGGS is a type of spatial reference system that partitions the globe into many individual, evenly spaced, and well-aligned cells to encode location. We calculated normalized area and compactness of cell geometries for 5 open-source DGGS implementations - Uber H3, Google S2, RiskAware OpenEAGGR, rHEALPix by Landcare Research New Zealand, HEALPix by NASA Jet Propulsion Labs, and DGGRID by Southern Oregon University - to evaluate their suitability for a global-level statistical data cube.</p> <p>This repository contains all generated data and statistics.</p> <ul> <li>EAGGR doesn't seem to have a predefined logic of hierarchical cell resolutions for ISEA3H</li> <li>EAGGR doesn't seem to have a region filling algorithm available, neither for ISEA4T nor ISEA3H</li> <li>rHEALPix is pure Python (with Numpy/Scipy support), but cell generation/conversion is slower than the other C/C++ based implementations</li> <li>DGGRID is a commandline tool and can predominantly only be used to generate a grid and fill with sampling data, the Python API is only a wrapper</li> <li>healpy is a Python package to handle pixelated data on the sphere. It is based on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) scheme and bundles the HEALPix C++ library.</li> </ul> <p>Kmoch et. al (2022). Area and Shape Distortions in Open-Source Discrete Global Grid Systems. <strong><em>Big Earth Data</em></strong></p>
Data for: The State of Open Infrastructure Grant Funding, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to better understand the amount, distribution, impact, and limitations of grant funding to open infrastructures that support research and scholarship.</p> <p>The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “The state of open infrastructure grant funding.” The full report is available at https://doi.org/10.5281/zenodo.10934089.</p> <p>A readme, data dictionary, and additional metadata definition file are provided with the dataset with additional detail.</p>
Data for: Open Infrastructure Governance: Current structures, nomenclature, composition, and service trends, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to<span> record information about community governance groups for open infrastructures, focused primarily on the individuals and institutions that serve in these groups. The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “Open infrastructure governance: Current structures, nomenclature, composition, and trends.” The full report is available at <a href="The%20data%20were%20summarized%20and%20reported%20in%20the%20&ldquo;2024%20State%20of%20Open%20Infrastructure%20Report&rdquo;%20section%20&ldquo;Open%20infrastructure%20governance:%20Current%20structures,%20nomenclature,%20composition,%20and%20trends,&rdquo;%20available%20at%20https:/doi.org/10.5281/zenodo.10934089.">https://doi.org/10.5281/zenodo.10934089</a>.</span></p> <p><span>A readme is provided with the dataset with additional detail.</span></p>
Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research"
<p>Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research" published in <em>Frontiers in Marine Science</em></p>
Method Classification of Open Access INTACT Molecular Interaction data.
<p>Simple classification data derived from open access papers indexed in the INTACT database (https://www.ebi.ac.uk/intact/downloads) based on PSI-MI25 codes for interaction detection methods or participant detection methods based on the subfigure caption text. <br> <br> intact_records_and_captions_complete.tsv - This file links available text of subfigure captions to PSI-MI25 codes for the interaction detection method and participant detection method. </p> <p>evidx_run_file.txt - This file provides execution codes for the 'EvidX' machine learning text classifier (https://github.com/SciKnowEngine/evidX/releases/tag/v0.1.0)</p> <p> </p> <p> </p> <p> </p>
Resource Metadata Harvested from Government and Research Open Data Portals
<p>This dataset consists of resource metadata harvested from the APIs of hundreds of government and research data portals from all over the world. This dataset was harvested between the 13<sup>th</sup> and 15<sup>th</sup> of September 2018. The metadata harvested from these portals was translated to a single metadata format (see <em>metadata_format.odt</em>). An overview of all harvested domains is given in <em>portal_list.txt</em>.</p> <p>The harvested data is divided into five gzipped json-lines files, based on the ‘type’ of the resource that is derived from the data of the APIs:</p> <ul> <li><em>dataset_metadata.jsonl.gz</em>: Resources classified as a Dataset, or subsets of dataset (e.g. Dataset:Image and Dataset:Audio) [6 246 250 resources]</li> <li><em>document_metadata.jsonl.gz</em>: Resources classified as a Document, or subset of document (e.g. Document:Paper:Conference and Document:Book) [15 626 541 resources]</li> <li><em>software_metadata.jsonl.gz</em>: Resources classified as Sofware (including Software:Model) [42 036 resources]</li> <li><em>service_metadata.jsonl.gz</em>: Resources classified as a service (e.g. WMS, APIs) [1257 resources]</li> <li><em>other_metadata.jsonl.gz</em>: Resources of which the ‘type’ could not be determined from the data the API returned. This set still contains many datasets [1 502 979 resources]</li> </ul>
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
Citations to software and data in Zenodo via open sources
<p>In January 2019, the Asclepias Broker harvested citation links to Zenodo objects from three discovery systems: the NASA Astrophysics Datasystem (ADS), Crossref Event Data and Europe PMC. Each row of our dataset represents one unique link between a citing publication and a Zenodo DOI. Both endpoints are described by basic metadata. The second dataset contains usage metrics for every cited Zenodo DOI of our data sample. </p> <p> </p>
Data and Code open availability for the FRETsael paper
<p>This dataset includes the raw files of the measurements of Actin-Myosin interactions in SH-SY5Y cells, as well as the Matlab code for the simulations and analyses used in the paper about FRETsael: "<strong>FRET-sensitized acceptor emission localization (FRETsael) – nanometer localization of biomolecular interactions using fluorescence lifetime imaging</strong>"</p>
The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database
<p>This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features:</p> <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> <p>The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English).</p> <p>Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>. </p> <p> </p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
Secondary Data from Insights from Publishing Open Data in Industry-Academia Collaboration
<h1>Secondary Data from Insights from Publishing Open Data in Industry-Academia Collaboration</h1> <h2>Authors</h2> <p>Per Erik Strandberg [1], Philipp Peterseil [2], Julian Karoliny [3], Johanna Kallio [4], and Johannes Peltola [4].</p> <p>[1] Westermo Network Technologies AB (Sweden).<br>[2] Johannes Kepler University Linz (Austria)<br>[3] Silicon Austria Labs GmbH (Austria).<br>[4] VTT Technical Research Centre of Finland Ltd. (Finland).</p> <h2>Description</h2> <p>This data is to accompany a paper submitted to Elsevier's data in brief in 2024, with the title <em>Insights from Publishing Open Data in Industry-Academia Collaboration</em>.</p> <p><em>Tentative Abstract:</em> Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.</p> <h2>Secondary data from Survey</h2> <p>The file <code>survey.txt</code> contains secondary data from a survey of participants that published open data sets in the 3-year European research project InSecTT.</p> <h2>Secondary data from Zenodo</h2> <p>The file <code>secondary_data_zenodo.json</code> contains secondary data from an analysis of data sets published in Zenodo. It is accompanied with a <code>py</code>-file and a <code>ipynb</code>-file to serve as examples.</p> <h2>License</h2> <p>This data is licenced with the Creative Commons Attribution 4.0 International license. You are free to use the data if you attribute the authors. Read the license text for details.</p>
Navigating the complex policy landscape for carbon farming in The Netherlands and the EU -- Open Research Europe Extended Data-- Tables 1-6, Figures 1-2
<p>This is extended data for the article entitle 'Navigating the complex policy landscape for carbon farming in The Netherlands and the EU' submitted to Open Research Europe by Eise Spijker. </p>
Open-Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection"
<p>This archive contains three folders which are supplementary material for the paper accepted for publishing in IEEE Sensors Journal.</p> <p><strong>Contents:</strong></p> <ul> <li> The folder `open-access-data/upb/` contains the measurements acquired at UPB. The subfolders are named as `upb_ble_*`, where an asterisk masks the directory number. Whenever UPB is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/tau/` contains the measurements acquired at TAU. The subfolders are named as `tau_ble_*`, where an asterisk masks the directory number. Whenever TAU is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/wifi-on-off/` contains a sample code to read the files and plot the data from Fig. 14 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.py` and Fig. 15 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.ipynb`.</li> </ul> <p><strong>Results based on the data have been presented in the paper:</strong><br> Flueratoru, L., Shubina, V., Niculescu, D., Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection, IEEE Sensors, Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation</p> <p><strong>To cite these data sets please use the following:</strong><br> Laura Flueratoru, Viktoriia Shubina, Dragoș Niculescu, & Elena Simona Lohan. (2021). Open Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4643668</p>
Open data and power dynamics
<p>Podcast with an expert -Mor Rubenstein- about the management of open data and the power dynamics that are at play in society. In particular we are referring mainly to western Europe as the societal context.</p>
Open Science Team 7 Data Set and Bibliography
<p>As a small exercise before delving into a group research project we generated a small data set based on the review of 7 websites ranging in subject matter. We provide the Data set and bibliography here. </p>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain: open data and code
<p>Quality controlled temperature data at daily resolution at CTS, HRR, HYS, NVC, RSI and SGV and the most relevant codes for data processing used in:</p> <p>García-Pereira, F., González-Rouco, J. F., Schmid, T., Melo-Aguilar, C, Vegas-Cañas, C., Steinert, N. J., Roldán-Gómez, P. J., Cuesta-Valero, F. J., García-García, A., Beltrami, H., and de Vrese, H.: "Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain". Earth Surf. Dynam., submitted, 2023.</p> <p>All data can be also freely obtained for research from the original data sources, GuMNet (https://www.ucm.es/gumnet/) and AEMET (https://www.aemet.es/en/datos_abiertos). Further details of the code are available upon request to the corresponding author (Félix García-Pereira, felgar03@ucm.es).</p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </p>
Data on the actual use of open data/ open source on pre-prints at arXiv/bioRxiv
<p>Articles submitted (1st edition) to the preprint server arXiv/bioRxiv were converted to text and analysed as follows :</p> <ul> <li>For arXiv articles, nationality was assigned to the manuscript using the first occurrence of the email address in the manuscript.</li> <li>For bioRxiv articles, we assigned nationality using the country tag information in the metadata about the first author.</li> <li>We listed the URLs that appeared in each manuscript.</li> <li>We checked how many articles contained a particular URL (e.g. github; https://github.com ) by year, month and nationality.</li> </ul> <p>This dataset describes the results of the above work.</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.