Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs

Supplemental data and analysis files for Perron et al.: "Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs" (<a href="https://www.nature.com/articles/s42003-022-03796-w">https://www.nature.com/articles/s42003-022-03796-w</a>). The .tar.gz files contain read counts associated with various RNA-seq analyses. The .rds files are single R object files that contain various analysis results tables. The .csv files also contain analysis results or sample metadata tables. See <a href="http://csg.lab.mcgill.ca/sup/pancancer_stability/">http://csg.lab.mcgill.ca/sup/pancancer_stability/</a> for a full description of the files.

openmit-licenseNov 2023View details →

BASE-9 binarity and stellar masses from Gaia DR3, 2MASS, and Pan-STARRS data for six open clusters: NGC 2168, NGC 7789, NGC 6819, NGC 2682, NGC 188, NGC 6791

<h2>Data sets as described in "Goodbye to Chi-by-Eye: A Bayesian Analysis of Photometric Binaries in Six Open Clusters", Childs et al. 2023 <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract">https://ui.adsabs.harvard.edu/abs/2023arXiv230816282C/abstract</a></h2>

opencc-by-4.0May 2022View details →

Modelling pan-Arctic peatland carbon dynamics under alternative warming scenarios

The purpose of this study is to simulate peatland carbon dynamics in the future climate conditions for four major future warming scenarios. The study examines whether less pronounced warming could further enhance the peatland carbon sink capacity and buffer the effects of climate change. It will also determine which trajectory peatland carbon balance will follow, what the main drivers are and which one will dominate in the future. In this study, LPJGUESS Peatland has been employed across the pan-Arctic and we carried out four sets of simulations. The data files contain the information about carbon accumulation, NEE, NPP and ice fraction.

opencc-by-4.0Oct 2022View details →

The pan-genome of Aspergillus fumigatus provides a high-resolution view of its population structure revealing high-levels of lineage-specific diversity driven by recombination

Aspergillus fumigatus is a deadly agent of human fungal disease, where virulence heterogeneity is thought to be at least partially structured by genetic variation between strains. While population genomic analyses based on reference genome alignments offer valuable insights into how gene variants are distributed across populations, these approaches fail to capture intraspecific variation in genes absent from the reference genome. Pan-genomic analyses based on de novo assemblies offer a promising alternative to reference-based genomics, with the potential to address the full genetic repertoire of a species. Here, we use a combination of population genomics, phylogenomics, and pan-genomics to assess population structure and recombination frequency, phylogenetically structured gene presence-absence variation, evidence for metabolic specificity, and the distribution of putative antifungal resistance genes in A. fumigatus.  We provide evidence for three distinct populations of A. fumigatus, structured by both gene variation (SNPs and indels) and distinct gene presence-absence variation with unique suites of accessory genes present exclusively in each clade. Accessory genes displayed functional enrichment for nitrogen and carbohydrate metabolism, hinting that populations may be stratified by environmental niche specialization. Similarly, the distribution of antifungal resistance genes and resistance alleles were often structured by phylogeny. Despite low levels of outcrossing, A. fumigatus demonstrated a large pan-genome including many genes unrepresented in the Af293 reference genome. These results highlight the inadequacy of relying on a single-reference based approach for evaluating intraspecific variation, and the power of combined genomic approaches to elucidate population structure, genetic diversity, and the putative ecological drivers of clinically relevant fungi. Accompanying manuscript is available as preprint at <a href="https://dx.doi.org/10.1101/2021.12.12.472145">https://dx.doi.org/10.1101/2021.12.12.472145</a>  Lotus A. Lofgren, Brandon S. Ross, Robert A. Cramer, Jason E. Stajich. Combined Pan-, Population-, and Phylo-Genomic Analysis of Aspergillus fumigatus Reveals Population Structure and Lineage-Specific Diversity bioRxiv 2021.12.12.472145; doi: https://doi.org/10.1101/2021.12.12.472145

opencc-by-4.0Jun 2024View details →

Near Pan-Svalbard cryospheric hazards inventory (SvalCryo)

We present a comprehensive inventory of thaw slumps (TS) and thermo-erosion gullies (TEG) on the Svalbard Archipelago. We used the most recent orthophotos (0.5 x 0.5 m pixel size) acquired in 2009-2011 from the Web Map Services (WMS) of the Norwegian Polar Institute. TS and TEG were identified and digitised on-screen as polygons in the ETRS_1989_UTM_Zone_33N coordinate reference system. TS and TEG were identified based on their morphology, digitised on-screen (maximum zoom was 1:1000) as polygons, and then individually quality checked in the GIS environment. This process was repeated twice, to avoid any bias in feature(s) mapping, first by a geomorphologist (first author) and then by an Arctic geologist (second author). The cryospheric inventory of the 14 regions (Andreé Land, Dickson Land, James I Land, Nordenskiöld Land, Bünsow Land, Olav V Land, Sabine Land, Nathorst Land, Heer Land, Wedel Jarlsberg Land, Torell Land, Sørkapp Land, Barentsøya and Edgeøya) totalises 8491 polygons, out of which 3679 are TS and 4812 are TEG. Within the attribute tables, there are eight columns comprising details about each polygon/feature, as follows: FID (ID showing the total number of polygons), Shape (Polygon), ID (each polygon from each region has associated an ID for both TS and TEG), Area (sq. m), Perimeter (m), MaxDistanc (calculated between two points along the polygon perimeter), Elongation (calculated as the maximum distance divided by the square root of the area), Region (the name of the region that the feature belongs to).

openmit-licenseMar 2019View details →

The pan-genome of Saccharomyces cerevisiae

These datasets are related to 'The pan-genome of Saccharomyces cerevisiae' (Li G., Ji B., and Nielsen J.). This deposition contains following datasets: (1) Genomes.tar.gz: a compressed file containing 1392 Saccharomyces cerevisiae genome assembles analyzed (2) genome_information_2.0.tsv: a tab-separated text file that contains the basic information of above genomes, including genomeSize, contigNums, N50, busco_C(%), busco_S(%), busco_D(%), busco_F(%), busco_M(%), busco_n, number_of_genes, number_of_partial_genes, download_from, Eco_Source, Ploidy, Aneuploidies. (3) ClusterFasta.tar.gz: a compressed file that contains a list of fasta files. Each fasta file contains protein sequences in a cluster. The name of the fasta file is the name of the representative sequence of that cluster.   (4) sc_gene_cluster_info_0.7_v4.tsv: a tab-separated text file that contains the properties of gene clusters. (5) gene_presence_absence_v4.tsv: a tab-separated text file that contains the gene-presence/absence information. Each columns is a gene cluster. Each row is a genome. Y/N is used to present presence/absence. (6) gene_num_in_clusters_of_each_strain_v4.tsv: a tab-sparated text file that contains the gene number of each genome in each cluster (copy number). Each columns is a gene cluster. Each row is a genome.  (7) feature_importances_cv5_pa_cnv.tsv: a tab-separated file that contains the feature importance from a random forest classifier in a 5-fold cross-validation approach. The classifier was trained on gene presence/absence table (PA) or copy number table (CNV). The columns 'pa_x' indicate the feature importance in each fold of cross-validation on PA dataset. The columns 'cnv_x' indicate the feature importance in each fold of cross-validation on CNV dataset.

opencc-by-4.0Sep 2024View details →

Pan-European dataset of subsurface temperature isolines at 1000 m and 2000 m depth

This dataset consists of two seperate geopackages, which are both digitisations of isotherms in the 1000 meters and 2000 meters below ground maps, displayed in plates 2 and 3 of the 2002 <a href="https://op.europa.eu/publication-detail/-/publication/9003d463-03ed-4b0e-87e8-61325a2d4456" target="_blank" rel="noopener">Atlas of geothermal resources in Europe</a>.

opencc-by-4.0Jun 2022View details →

Pan-European exposure maps and uncertainty estimates from HANZE v2.0 model, 1870-2020

This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files: - CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP) - Pop: population - GDP: gross domestic product (2020 euros) - FA: fixed asset value (2020 euros) - imp: imperviousness density (%) Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods. Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables. For detailed description of the files, see the documentation provided with the code. This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.

opencc-by-4.0Jul 2023View details →

Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level

Pan-EU Land Mask Summary Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>. There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>. The masks are: <ol> <li> Landmask </li> <li> ISO-code country mask </li> <li> NUTS3 mask </li> </ol> Name convention To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include: <ol> <li> Generic variable name: country.code </li> <li> Variable procedure combination i.e. method standard (standard abbreviation): iso.3166 </li> <li> Position in the probability distribution / variable type: c </li> <li> Spatial support (usually horizontal block) in m or km: 30m </li> <li> Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s </li> <li> Time reference begin time (YYYYMMDD): 20210101 </li> <li> Time reference end time: 20211231 </li> <li> Bounding box (2 letters max): eu  </li> <li> EPSG code: epsg.3035 </li> <li> Version code i.e. creation date: v20230722 </li> </ol> An example of a file-name based on the description above: country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722 Landmask The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies. Two reference datasets are used,  <ol> <li> <a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution. </li> <li> <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask. </li> </ol> When generating the land mask, the two reference datasets in a way that: <ul> <li> If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask.  </li> <li> Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water. </li> </ul> The landmask consists of 4 values: <ul> <li> 10: not in the pan-EU area, i.e. out of mapping scope </li> <li> 1: land </li> <li> 2: inland water </li> <li> 3: ocean </li> </ul> This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible. ISO-3166 country code mask This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country. The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL. NUTS-3 mask The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions. The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL. It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.

opencc-by-4.0Mar 2023View details →

A Pan-European, Quantile Machine learning (QML) based, Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset (QML AOD))

The V 1.1.0 product is an improved Aerosol Optical Depth (AOD) product based on Gap-filled MAIAC AOD, which provide first full-coverage, high-resolution monitoring of fine-mode and coarse-mode aerosols in Europe from 2003-20. This dataset has successfully rectified the previously identified issue of weak associations between satellite AOD and PM2.5 in Europe, which was primarily attributable to current limitations of AOD data. Our innovative approach has yielded stronger correlations with PM10, PM2.5, and PMcoarse than previous AOD product, laying a critical groundwork for improving PM10, PM2.5, and PMcoarse predictions in further epidemiological studies or environmental monitoring. We have uploaded three QML AOD datasets in Geotiff format, covering the region from -27° to 72° latitude and from -25° to 45° longitude. These datasets will be useful for researchers and policymakers to better understand the impacts of aerosols on the environment and human health.  Note: v1.0.0 product do not include MAIAC AOD in their models. Please read more details in our paper  <h1>Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments</h1> <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.170593" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.170593</a>

opencc-by-4.0Dec 2019View details →

PanTaGruEl - a pan-European transmission grid and electricity generation model

If you have any questions or comments, please write to <a href="mailto:laurent.vincent.pagnier@gmail.com">laurent.vincent.pagnier@gmail.com</a>. When publishing results based on this data set, please cite: L. Pagnier, P. Jacquod, “Inertia location and slow network modes determine disturbance propagation in large-scale power grids”, PLOS ONE 14(3): e0213550, 2019. <a href="https://doi.org/10.1371/journal.pone.0213550">PLOS ONE 14(3): e0213550</a>, 2019. and M. Tyloo, L. Pagnier, P. Jacquod, “The Key Player Problem in Complex Oscillator Networks and Electric Power Grids: Resistance Centralities Identify Local Vulnerabilities”, <a href="https://doi.org/10.1126/sciadv.aaw8359">Science Advances 5(11): eaaw8359</a>, 2019. Description: PanTaGruEl is a dynamical grid model designed to investigate the propagation of disturbances in the continental European transmission grid. The construction of the model is detailed <a href="https://doi.org/10.1371/journal.pone.0213550.s002">here</a>. Features: <ul> <li>Precise distribution of national demands to network buses.</li> <li>Realistic electrical parameters of transmission lines.</li> <li>Merit-Order based economic dispatch of generators.</li> <li>Dynamical parameters of generators and loads for transient stability investigations.</li> </ul> Files: Data files: Our model is provided in an extended Matpower format and as csv raw data. For more information on Matpower format, see Appendix B of its <a href="https://matpower.org/docs/MATPOWER-manual.pdf">manual</a>. Script files: opf_ex.m performs optimal power flow computations for two load configurations. spectral_ex.m presents a basic spectral analysis. dynamics_ex.m give a minimal example of dynamical simulations. Requirements: Our model has been developed for use with <a href="https://matpower.org/">Matpower</a>. If you are interested in a port to another language, please <a href="mailto:laurent.vincent.pagnier@gmail.com?subject=Info%20on%20PanTaGruEl">contact us</a>. Acknowledgement: The authors thank M. Tyloo and K. Van Walstijn for their useful comments and remarks on the model. Sources: B. Wiegmans, <a href="https://doi.org/10.5281/zenodo.55853">“GridKit extract of ENTSO-E interactive map”</a> Global Energy Observatory, <a href="http://globalenergyobservatory.org">“GEO Power plants database”</a> Siemens, <a href="http://siemens.com/power-engineering-guide">“Power Engineering Guide”</a>

opencc-by-4.0Feb 2020View details →

Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"

<a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>

Level A Pan Europe Natural river discharge, E-HYPE 3.0

Simulated river discharge at each subbasin from E-HYPEv3.0 in m3/s. Daily data for 1961 to 2001 as time-series. Dam regulation and irrigation are NOT simulated in the natural runs. However, dams will still function as lakes with some amount of attenuation. Original data source: See information about the E-HYPE setup. Tools for repurposing: HYPE. Temporal resolution: Daily. Spatial resolution: 215 km2. Unit: m3/s. Data format: Zipped text file. On the first row there is a brief description and the rest of the file contains one row per time step, with the sub basin id as each column header on the second row. The first item on each line is the time step using the format YYYY-MM-DD, so 1961-01-01 for the first of January 1961. The dataset, Subbasin(EHYPEv3pt0).zip (shapefile with subbasin polygons) can be linked with the data.

Level A Pan Europe Precipitation, E-HYPE 2.5

Time-series for 1961 to 2001 describing the input forcing precipitation for each E-HYPE subbasin. The data describes daily values for each subbasin (mean area 215 km2). Unit of measurement: mm. The precipitation is derived from the WFD gridded data set which consists of the ERA-40 reanalysis data set daily precipitation output (Weedon et al. 2011)corrected for wet days mean monthly precipitation and undercatch (for example to CRU and GPCC). For each subbasin, the gridpoint nearest the subbasin centroid is used. Within E-HYPE a correction is made to increase precipitation above 700 m elevation. Original data source was WFD Forcing data and HYDE was used for repurposing. The data format is a zipped text file. On the first row there is a brief description and the rest of the file contains one row per time step, with the sub basin id as each column header on the second row. The first item on each line is the time step using the format YYYY-MM-DD, so 1961-01-01 for the first of January 1961.

Level A Pan Europe Subbasins, E-HYPE 2.5

This file is a shapefile with polygons defining hydrological watersheds (subbasins) with an average size of 215 km2 and the hydrological links (direction of flow) between them. The subbasins were automatically derived using the WHIST tool to delineate watersheds using the hydrologically corrected DEMs Hydrosheds (and Hydro1K north of 60 degrees). Some manual adjustments were made. The data has been quality checked against published values of catchment areas for European gauging stations. Original data source was Hydrosheds Hydro1k and WHIST was used for repurposing. The shapefile contains polygons with the following attributes: SUBID (unique ID of the subbasin), HAROID (ID of main river catchment, DOWN (ID of the downstream SUBID) , AREA (Area of subbasin in m2).

Level A Pan Europe Modelled river discharge, E-HYPE 3.0

Simulated river discharge at each subbasin from E-HYPEv3.0 in m3/s. Daily data for 1961 to 2001 as time-series. Original data source: See information about the E-HYPE setup. Tools for repurposing: HYPE. Temporal resolution: Daily. Spatial resolution: 215 km2. Unit: m3/s. Data format: Zipped text file. On the first row there is a brief description and the rest of the file contains one row per time step, with the sub basin id as each column header on the second row. The first item on each line is the time step using the format YYYY-MM-DD, so 1961-01-01 for the first of January 1961. The dataset, Subbasin(EHYPEv3pt0).zip (shapefile with subbasin polygons) can be linked with the data.