Find research datasets worth reusing

VulnMiner: A Comprehensive Framework for Vulnerability Collection from C/C++ Source Code Projects

In this repository, we present an initial release of the VulnMiner vulnerability dataset, curated from prevalent projects and annotated with vulnerable and benign instances. This dataset incorporates projects with vulnerabilities labeled as Common Weakness Enumeration (CWE) categories. The developed open-source extraction tool collects vulnerability data utilizing static security analyzers. The study also fosters the machine learning (ML) and natural language processing (NLP) model's effectiveness in accurately classifying vulnerabilities, evidenced by its identification of numerous weaknesses in open-source projects.

MODUL4R EU Founded Project Swarm Learning framework dataset

MODUL4R EU Founded Project Swarm Learning framework dataset v1.0.0 Contains values for: <ul> <li>Capacitor type </li> <li>Grab Pressure (Pa)</li> <li>Leg Cutting (mm)</li> <li>Polarity</li> <li>Quality Metric</li> </ul>

RNAseq analyses ANR MAORI project

Results of DEseq2 analyses from RNAseq data Results of enrichment analyses using GO or KEGG Results of WGCNA data

opencc-by-4.0Sep 2023View details →

Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.

<h2>Description (V2 - Latest):</h2> To enable easy integration in the workflows, we have provided the main datasets in the following formats:   <ul> <li>Vector dataset:<code> Folder - Vector</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>Geopackage (.gpkg)</code> file (Results_Vis.gpkg) with polygon geometries at 1/8-degree spatial resolution in an EPSG:4326 coordinate system. The attribute table of this file contains FN_ID column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul>   <ul> <li>Raster datasets: <code> Folder - Raster</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>geotiff (.tif)</code> files with LZW compression in an EPSG:4326 coordinate system. The assessed gross rooftop area datasets are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units.</li> </ul>   <ul> <li>Numerical dataset: <code> Folder - Numerical</code> The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code>parquet (.parquet)</code> file (Results.parquet). This file contains FN_ID column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as BF_X_Y with X having values as 1, 2, 3, 4, and 5 for SSP1, SSP2, SSP3, SSP4, SSP5 narratives with Y representing the assessment year having values as 20, 30, 40, and 50 for years 2020, 2030, 2040, and 2050 and with km2 units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul>   In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <code> Folder - Models</code> <ul> <li>M2_Model.json: This file contains the frozen parameters of the M2 model in <code>.json</code> format generated from <code>XGBoost version 2.0.3</code></li> <li>SSP_drivers.parquet: This file contains the driver data used for generating the main dataset in our study</li> <li>FN_MAP.parquet: This file contains the boundary information for each fishnet grid tile in a Well Known Text (WKT) format.</li> <li>Prediction.ipynb: This file provides a python notebook interface to generate inferencing from <code>M2_Model.json</code> using <code>SSP_drivers.parquet</code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code>FN_MAP.parquet</code><code> </code>file.</li> <li>environment.yaml: This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul>   <h2>Version history:</h2> This version corresponds to the revised journal submission (Round 1). The version will be updated upon the completion of the review of the main manuscript. <ul> <li>This version V2 is supersedes V1 to correspond with round 1 of review.</li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 ", submitted to Scientific Reports Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul>   <h2>Changelog:</h2> The following files from version V1 of this dataset are now archived based on the reviews (Round 1). <ol> <li> <blockquote>1_Geospatial_Dataset_V1.gpkg</blockquote> </li> <li> <blockquote>2_Countrylevel_gross_rooftop_area_V1.parquet</blockquote> </li> <li> <blockquote>3_Analytics_Scripts_V1.ipynb</blockquote> </li> </ol>

European Investment Bank Projects in ACP, OCT, Africa, Asia, and Latin America (1957-2024)

This dataset offers a comprehensive analysis of European Investment Bank (EIB) projects in Africa, the Caribbean, and the Pacific (ACP) regions, Overseas Countries and Territories (OCT), Asia, and Latin America, spanning from 1975 to 2023. The dataset includes information on 2,558 projects; each entry in the dataset includes key project details such as the project’s sector, date of signature, and financial commitments. All numbers are in 2015 euros.

opencc-by-4.0Feb 2024View details →

Model projections of North Sea cod under deep uncertainty

This is model output generated with code publicly available on Github (https://github.com/imf-uham/DMDU_North_Sea).

The BEV*ARV Project; the Preservation Conditions of Museum Collection Storages in Denmark.

A national survey on the preservation condition in Danish state subsidised museums’ storages was conducted in 2022-23. The collected data has been anonymized and is open for further study and research. The survey consisted of 25 questions (BEV.ARV_Spørgeskema) responded during physical inspections of the storages. 103 museums participated in the survey, 350 buildings and more than 850 storage rooms were physically inspected, and the results recorded. Upon inspection, the preservation/degradation risks addressed in each question were rated according to an A-B-C-D scale. Character A is the best (no degradation risk), D is the lowest (high degradation risk). A guideline (BEV.ARV_Svarvejledning) was used to assist uniform evaluations of the storage conditions. The survey covered museums with art collections, cultural history collections and natural history collections. The indoor climate over one calendar year was recorded in around five hundred of the storage rooms. The collected and uploaded data contain information on the condition of the buildings used for storages, the condition of the storage rooms and the objects stored therein, and how the museums manage and control their collection storage rooms. The survey method has been developed for future inspections and comparative reports of the storage conditions of museum collections. The files included in the datasets have been used in the report to the Ministry of Culture. Furthermore, the data has been applied for making individual museum storage reports with scores and characters for each storage facility. The data is available in Danish only. Content of the folder Klimadata: <ul> <li>The file BEV.ARV_2024.04_Dataoversigt provides information regarding type of museum and whether climate data from the storages have been collected or not.  </li> <li>The Excel files (M00X-files), one per museum, provides the climate data (Relative Humidity, RH % and Temperature, T °C) from the individual storages, naming corresponding to those applied in the file BEV.ARV_2024.04_Anonymiseret_Raadata.csv</li> </ul> Content of the folder Rapporter_Supp.Info: <ul> <li>The file BEV.ARV_2024.04_Anonymiseret_Raadata.csv holds the complete inspection results for all museum storages (the buildings and their rooms).</li> <li>The file BEV.ARV_2024.04_Karaktermodel+Analyse contains analyses carried out and used in the overall report to the Ministry of Culture.</li> <li>For completeness, the report to the Ministry of Culture (BEV.ARV_Slutrapport), an example of an individual museum storage report ( BEV.ARV_Magasinrapport_Museum_X) the 25 survey questions (BEV.ARV_Spoergeskema) and the response guidelines (BEV.ARV_Svarvejledning), all in Danish, are included. </li> </ul>

opencc-by-4.0Jul 2022View details →

Scenario emissions and temperature data for PROVIDE project

Data for tier 1 and tier 2 PROVIDE scenarios.  Tier 1 scenarios are mostly from integrated assessment models. Tier 2 scenarios are much more numerous and are kept in a separately zipped folder for temperatures and csv file for emissions data. The temperature folders contains the full set of FaIR runs for scenarios entirely defined by emissions. Summaries are much smaller files containing quantile info for each scenario, including the scenarios defined by combinations of emissions and temperature trends.  <ul> <li>10 Tier 1 scenarios until 2100</li> <li>15 Tier 1 scenarios defined until 2300, all of which are variations of the original 10</li> <li>Many Tier 2 scenarios, aiming to completely tile reasonable emissions space parameterised with 4 variables</li> </ul> Several objectives of the PROVIDE project depend on a set of scenarios that can be modelled through either a ‘classical’ forward-looking approach or by a novel approach that ‘reverses the impact chain’. These scenarios are also key elements for the integration of PROVIDE findings in the outward-looking stakeholder Dashboard of the project. Here we describe the set of scenarios that has been developed and will be used within PROVIDE. In total, PROVIDE explores three complementary approaches: <ol> <li>10 distinct tier 1 scenarios extending until 2100, mostly based on the existing literature, used for short-term assessments of impacts</li> <li>15 distinct tier 1 scenarios extending until 2300, based on different extensions of the 10 literature scenarios, used for assessing longer-run impacts and the geophysical impact of significant temperature overshoot</li> <li>~1350 distinct tier 2 scenarios, exploring several dimensions of emissions space systematically, such as CO2 net zero date and relative methane intensity. This is used to explore which scenarios are compatible with given climate outcomes. These scenarios can be used to reverse the traditional impact chain, going from acceptable climate risks to descriptions of acceptable emissions. </li> </ol>

opencc-by-4.0May 2024View details →

Scripts and datas for "Climate-driven projections of future global wetlands extent"

Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study: L. Hardouin, B. Decharme, J. Colin, C. Delire: Climate-driven projections of future global wetlands extent. The calculation and input scripts include: 1_var_comput : Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl. 2_TOPMODEL : The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included. 3_alpha_and_beta : Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version. The outputs datasets contain: 4_fwtl_model_period : The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work. 5_not_calibrated_fwtl_model_period : The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.   Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.

opencc-by-4.0Sep 2023View details →

Glacier runoff projections and their multiple sources of uncertainty in the Patagonian Andes (40-56°S)

This dataset contains the catchment scale results of the study: "Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S)". The results are disaggregated in the following files (for more details, please read the README file): - basins_boundaries.zip: Contains the polygons (in .shp format) of the studied catchments. Each catchment is identified by its "basin_id". - dataset_historical.csv: Summarises the historical conditions of each glacier at the catchment scale (area, volume and reference climate). - dataset_future.csv: Summarises the future glacier climate drivers and their impacts at the catchment scale.  - dataset_signatures.csv: Summarises the  glacio-hydrological signatures of each glacier at the catchment scale. Citation (preprint under review):  - Aguayo, R., Maussion, F., Schuster, L., Schaefer, M., Caro, A., Schmitt, P., Mackay, J., Ultee, L., Leon-Muñoz, J., and Aguayo, M.: Assessing the glacier projection uncertainties in the Patagonian Andes (40–56° S) from a catchment perspective, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2325, 2023.

opencc-by-4.0Jun 2024View details →

Input data files for the CPR-DINCAE project

This dataset contains the netCDF files used as the input for [DINCAE](https://github.com/gher-uliege/DINCAE.jl)

opencc-by-4.0Jun 2024View details →

Forest-related landscape metrics in LandKlif project

We calculated forest-related landscape metrics which have influence on insect diversity. Based on the detailed Landklif map (dataset 11560 at LandKlif database, https://www.landklif.biozentrum.uni-wuerzburg.de), we classify coniferous forest, decideous forest, mixed forest, small wood, and transitional woodland-shrub as forest features. Sub land use class and origical classification was kept as well. This dataset includes the area percentage (landscape composition) of these classes as well as edge length between forest features and non-forest features, in a scale of 100, 200, 500, 1000, 1500 meter radius around the study plots, as well as in TK 25 quadrant scale. TK 25 quadrant is common name in Germany for the topographical map unit at a scale of 1:25000 designated by four-digit numbers, which has a long history (from 1875) and is been used as unit for geographical survey and biodiversity mapping (http://maps.snsb.info/TK25/). Detailed Landklif map was created by combining 3 different land cover maps to create a detailed land cover map for 6 km buffer area around landklif study plots. We used ATKIS 2019 land cover as basis, added details from Invekos 2019 and Corine 2018. We categorized the land cover into 6 classes, further subcategorized them into sub land use classes. The original classification from different sources are kept. In case of overlapping, the priority goes (from high to low): natural > forest > grassland > arable > urban > water. In case of overlapping between data source: transitional woodland-shrub from Corine > Invekos > ATKIS. Areas outside of Bayern are filled with only Corine data. The coordinate system of the shapefile is ETRS89 / UTM zone 32N (EPSG:25832). This dataset is not open access due to its sensitivity but can be reached (https://www.landklif.biozentrum.uni-wuerzburg.de/Download/ShowXml.aspx?DatasetId=11560) and requested via the LandKlif database. LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif).  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

opencc-by-4.0Jun 2024View details →

World Bank Digitalization Database ODDEA Project

Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381). It consists of 8 ICT indicators for 266 countries and country groups for 2012 – 2022 period. Excel files with metadata compacted in zip file.

Data gathered during the first and second stage of carrying out the NCN research project "Odmieńcy. Performances of otherness in the Polish transition culture" (year 2022 and 2023) - PI

Data gathered during the first and secon phase of the project "Odmieńcy. Performances of otherness in Polish transition culture" by Dorota Sosnowska used as a basis for two papers: <a href="https://open.icm.edu.pl/items/9117fe29-528d-43ff-99c7-2e1fe0a8a93a">Blasted 1999. Sarah Kane’s Body Against the Archive (icm.edu.pl)</a> and <a href="https://open.icm.edu.pl/items/0aa7964c-b690-42af-8f7c-3d4fc62084b5">Brzydkie uczucia. O nudzie w sztuce i teatrze lat’ 90 (icm.edu.pl)</a>

Data gathered during the first and second stage of carrying out the NCN research project "Odmieńcy. Performances of otherness in the Polish transition culture" (year 2022 and 2023)

Data gathered during the first and secon phase of the project "Odmieńcy. Performances of otherness in Polish transition culture" by Łukasz Kiełpiński used as a basis for two papers: <a href="../records/10625768">Zarządzanie ambiwalencją. Polski dyskurs ekspercki wokół HIV/AIDS na przełomie lat osiemdziesiątych i dziewięćdziesiątych XX wieku (zenodo.org)</a> and <a href="../records/10625805">Gra o sumie zerowej. Ekonomia wstydu w filmie "Pora na czarownice" (zenodo.org)</a>

The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life. Data file underpinning Figure 2A and Figure 2B

<div>These datasheets accompany the article "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" in Frontiers in Science</div> <div>This file contains data processed from Catalog of Life on 31 December 2023. The catalog was downloaded and post-processed to</div> <div>remove prokaryotic taxa</div> <div>remove extinct and fossil taxa</div> <div>remove taxon names that were listed as junior synonyms</div> <div>remove taxon names listed as "invalid"</div> <div>Total living, valid eukaryotic genera 167,085</div> <div>The taxa were sorted by the nomenclatorial Code under which they were declared (to avoid namespace clashes)</div> <div>International Code for Algae, Fungi and Plants https://www.iapt-taxon.org/nomen/main.php</div> <div>Algal, Fungal, Plant code genera 31,076</div> <div>International Code of Zoological Nomenclature https://www.iczn.org/the-code/the-code-online/</div> <div>Zoological code genera 136,009</div> <div>The Code-sorted taxa were aggregated by the generic portion of their names, and two plots were generated:</div> <div>a plot aggregating the cumulative number of species in genera sorted by species number (Figure 2A)</div> <div>a plot illustrating the distribution of the size of genera (Figure 2B)</div> <div>This data file gives access to these processed data for</div> <div>Figure 2 A Data</div> <div>Figure 2 B Data</div> <div>The original data including the intermediate calculations of values, and the plotted graphs, are available as a GoogleDoc at https://docs.google.com/spreadsheets/d/1V-bTtWjIRasC3AgID0jGlyToKqI-H9h1aPeSxUpNrjk/edit?usp=sharing</div>

Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing

The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing". OGGM_area_projections.nc contains data for Figure 3. OGGM_volume_projections contains data for Figure 4. OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5. OGGM_solid_ice_discharge_regions.nc contains data for Figure 6. OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7. OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.

opencc-by-sa-4.0Jul 2024View details →

EFSA Project on the use of NAMs to explore interspecies metabolic differences on essential oils as feed additives (Annexes A, B, C, D)

Raw data of phase I and II experiments and PBK model input data and simulation of the EFSA Project on the use of NAMs to explore interspecies metabolic differences on essential oils as feed additives (OC/EFSA/SCER/2021/14).

METAS VNA Tools project of a niobium CPW in different conduction states at 4 Kelvin, 10 Kelvin and at 25 Kelvin

A METAS VNA Tools project which contains S-parameter data of a plain CPW line on a 1cmx1cm silicon chip made from intrinsic silicon. The chip is operated at 4 Kelvin, 10 Kelvin and 25 Kelvin with currents up to 200 mA for further heating injected. The temperature, current and DC resistance are noted in each filename.

gEneSys Project - Systematic Literature on the Nexus between Gender and Energy Transition Database

The present Dataset containes the data collected for the gEneSys Systematic Literature Review on the nexus between gender and energy transition. Data have been collected from 152 papers published between 2000 and 2023. The publications have been identified through an hoc research query and retrieved from the Web of Science Database. The dataset inscludes the following variables: <ol> <li>Title of the publication, category of the categorization of Bell et al., 2020 (Political, Economic, Socio-Ecological, Technological).</li> <li>Cluster in which the publication has been included.</li> <li>Parts of the publication’s results about the nexus between gender and energy.</li> <li>Parts of the publication’s text about the gender gap assessed by the publication.</li> <li>Parts of the publication’s text about the gender gap identified to be bridged by future research.</li> <li>The type of the gender issue/s addressed by the publication. </li> <li>The type of the gender issue/s addressed by the publication. </li> <li>Technology/ies mentioned in the publication.</li> <li>The name of the country or countries studied by the publication.</li> <li>World Bank classification of the level of income of the country or countries studied by the publication.</li> <li>World Bank classification of the region of the country or countries studied by the publication.</li> <li>Spatial Context (e.g. international, national, inner-country, peri-urban, rural) of the country or countries studied by the publication.</li> <li>Research method employed in the publication (qualitative, quantitative, mixed).</li> <li>Specific qualitative, quantitative or mixed method or methods employed in the publication.</li> <li>Number of observations for the methods used.</li> <li>Parts of the publication’s text about the policy recommendations elaborated in the publication.</li> <li>If the publication mentions a pathway.</li> <li>Year of publication.</li> <li>Author/s surname and name initial. </li> <li>Author/s full surnames and names. </li> <li>Keywords chosen by the author/s. </li> <li>Abstract of the publication. </li> <li>Name of the source or journal. </li> <li>Type of publication. </li> <li>Category/ies identified by Web of Science. </li> <li>Publication’s language. </li> <li>Keywords identified by Web of Science. </li> <li>Number of references cited by the publication. </li> <li>Number of times the publication has been cited in Web of Science Core Database. </li> <li>Number of times the publication has been cited in Web of Science All Databases. </li> <li>Name of the publisher. </li> <li>Digital Object Identifier. </li> <li>Digital Object Identifier link. </li> <li>Publication’s number of pages. </li> <li>Web of Science citation index. </li> <li>Research area or areas of the publication. </li> <li>Web of Science Unique Identifier.</li> </ol>