Find research datasets worth reusing

EUNIS habitat suitability maps at 100m resolution

Habitat suitability maps of 203 <a href="https://eunis.eea.europa.eu/">EUNIS</a> level 3 classes have been modeled at 100m resolution with <a href="https://biodiversityinformatics.amnh.org/open_source/maxent/">Maxent</a>. For the modeling plot observation from the <a href="http://euroveg.org/eva-database">European Vegetation Archive</a> have been used as observations for training and testing. As predictors various climate layers, soil layers, topographic layers and RS-enables EBVs have been used. Detailed information on the predictors can be found <a href="https://www.synbiosys.alterra.nl/nextgeoss/docs/Description_Abiotic_and_RSEBVs.pdf">here</a>. The 203 EUNIS habitat types comprise 8 groups according to the revised typology: <ul> <li>Salt marsh (MA)</li> <li>Coastal habitat (N)</li> <li>Wetland (Q)</li> <li>Grassland (R)</li> <li>Shrub (S)</li> <li>Forest (T)</li> <li>Sparsely vegetated habitat (U)</li> <li>Man-made habitat (V)</li> </ul>

Political map of the Arctic

Political map of the Arctic -used as a source for the model of the Regional Security Complex in the Arctic The map was drafted as a part of research supported by Poland’s National Centre for Science under the grant entitled: The adaptation of the regional security complex in the face of climate change: the example of the Arctic, with number: UMO-2019/35/N/HS5/00578.

Impact of Schistosomiasis, Soil-Transmitted Helminthiasis and Anaemia on preschool and school-age children's health condition: post treatment predictive mapping in Benin Republic.

This dataset provides information about the epidemiology of schistosomiasis, soil transmitted helminthiasis and anemia alongside malnutrition among preschool and school age children in Ouake and Bembereke districts of donga and borgou departments in Benin republic.

opencc-by-4.0Feb 2023View details →

Mapping an Impact Network for Transformational Change in CLEVER - Stakeholder Database

How does international trade in agricultural and forest products affect biodiversity? CLEVER (Creating leverage to enhance biodiversity outcomes of global biomass trade) investigates this question, particularly for animal feed, energy crops, tropical timber and aquacultures. This stakeholder database has been created in the CLEVER project to provide a comprehensive but not exhaustive list of stakeholders who may have an interest in CLEVER. The database includes information such as names and types of organisations and data about their role in the value chains concerned (timber and soy in Brazil, timber in Cameroon, fishmeal and oil globally). Particular attention has been paid to the involvement of local communities and vulnerable groups directly or indirectly affected by trade.

Texas Ecological Mapping Systems (EMS) for Southern Texas Coastal Counties

We mapped 66 Ecological Mapping Systems (EMS) for eight coastal counties in south Texas, from Refugio and Aransas County south to the Mexican border. Land cover (LC), geophysical setting information, and woody vegetation height were all attributed to image objects derived from 10 m Sentinel-2 satellite imagery to model EMS type. A supervised process with training data collected from aerial photographs, aided by quantitative, species-specific, ground-collected virtual plot data, was used to classify LC in a RandomForest framework. Out of bag (OOB) error for LC was 15.24%. Recently collected LiDAR point cloud information was used to map height for woody vegetation, and the height was, in turn, used to distinguish between herbaceous, shrubland, and woodland/forest types via modification of LC results, and to define several canopy >10 m versions of forested EMS types.  Geophysical settings were mapped based primarily on the distribution of soil Map Units (MUs) from the national digital soil survey (gSSURGO). Elevation and potential ponding information were derived from analysis of LiDAR-derived digital elevation models (DEMs) as an aid in mapping several EMS types. Heads-up modification of both LC and EMS modeling results using aerial photograph interpretation improved results. The agreement between EMS mapped type and field-collected data (most 10 years old or more) was >75%. The most abundant EMS types included Coastal and Sandsheet: Deep Sand Grassland (10.7% of the region), Native Invasive: Mesquite/Mixed Shrubland (5.0%), Gulf Coast: Coastal Prairie (4.6%), and South Texas: Sandy Mesquite Savanna Grassland (4.4%). The improved land cover, geophysical settings data, vegetation height data, and the use of finer-resolution image objects for modeling enabled mapping of all EMS types more accurately than previous datasets. The new EMS dataset will facilitate analysis and conservation of important habitats and modeling of species of concern that are tied to those habitats.

Mapping of solar panels and Fukushima Daiichi Nuclear Power Plant Accident-associated radioactive waste storage in 2022 and 2023, Fukushima, Japan

The policy of reconstruction after the Fukushima Daiichi Nuclear Plant accident has led to a radical transformation of the landscapes of Fukushima Prefecture especially in two aspects (Asanuma-Brice et al., 2023). The first is related to an extensive decontamination policy which resulted in the removal of more than 13 million m3 of contaminated soil (MOEJ, 2021). The second is the widespread installation of solar panels, which demonstrate the transition decided by the Prefecture and the inhabitants in terms of energy policy. A systematic mapping of these features was carried out from the satellite imagery of Google Map (2023) within the boundaries of Fukushima Prefecture. The objective was to highlight the evolution of specific land use features that are captured imprecisely by automatic detection mapping. We focused on the main visible change in the landscape in terms of land use since Fukushima Daiichi nuclear accident:  contaminated waste disposal areas and solar panel fields. These zones were delineated allowing a calculation of the corresponding surface areas (m2). The dataset is composed of 4 shapefile layers: contaminated waste deposits in 2022 and 2023, solar panels in 2022 and 2023. For the year 2022 the last update was conducted in July 2022 and for the year 2023 the last update took place in March 2023. As the land use is in constant and rapid transition (Asanuma-Brice, 2021), we considered as contaminated waste deposits, the permanent storage centers as well as the sites where there are still bags of contaminated waste in varying numbers, knowing that they will be removed and stored on other dedicated sites (Evrard et al., 2019). This choice was made to potentially identify, when the map was updated, the future uses of the land where this waste was stored temporarily. This dataset is part of a larger project that aims to provide the community with an interactive tool (https://mitatelab.cnrs.fr/mitate-labs-map-of-solar-panel-and-contaminated-wasted-land/) that makes available various types of information essential to the analysis of the reconstruction, such as: the delineation of the evacuated zone (which evolved throughout time), the delineation of the municipality boundaries affected by the reconstruction policy, the main services found in these localities, the location of the memorials of the disaster in the zone, as well as the geo-localization of the soil/sediment samples collected by other Mitate lab researchers in order to investigate the redistribution of radionuclides in the environment (Evrard et al., 2021).

opencc-by-4.0Dec 2022View details →

Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group

Example datasets and code for the recommended implementation of Quantitative Susceptibility Mapping (QSM) in "Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain:  A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group".

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Data from: Latent generative landscapes as maps of functional diversity in protein sequence space

Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.

opencc-zeroApr 2023View details →

opencc-by-4.0Nov 2022View details →

Global dataset for "Global leaf-trait mapping based on optimality theory "

This repository contains Global data used for “Global leaf-trait mapping based on optimality theory” published in GEB. <ol> <li>Global_Maps_SLA represents climatology of published Global SLA used for comparison (details products see table 1 and figure 4).</li> <li>Global_Maps_Na represents climatology of published Global Narea used for  comparison  (details see table 1 and figure 4).</li> <li>Global_Maps_Nmass represents climatology of published Global Nmass used for comparison (details see table 1 and figure 4).</li> <li>TS_SLA is simulated time-series of SLA based on optimality theories from 1992 to 2015</li> <li>TS_Na is simulated time-series of Narea based on optimality theories from 1992 to 2015</li> <li>TS_Nmass is simulated  time-series of  Nmass based on optimality theories  from 1992 to 2015</li> <li>TS_LMA_decidudous  is simulated time-series of  deciduous LMA  based on optimality theories from 1982 to 2016</li> <li>TS_LMA_evergreen is simulated time-series of evergreen LMA  based on optimality theories from 1982 to 2016</li> <li>TS_Vcmax25 is simulated time-series of Vcmax25  based on optimality theories from 1982 to 2016</li> </ol>

Additional Data: Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023

The following is a selection of figures and tables from a bibliometric study that will be released later. The title of this study is Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023. In the online listing of the appendix, we will find three figures (Figure 5, Figure 6, and Figure 12) and three tables (Table 3, Table 4, and Table 4), also several references related to this research. It was important to us that the core of the study that is now being carried out not be diminished in any way, which is why we chose the photos and tables we did. This study was conceived and supported by the Indonesia Endowment Fund for Education (LPDP), which the Ministry of Finance administers in the Republic of Indonesia, to evaluate current trends and research problems in computational thinking for education. The Scopus database was used, and its range of coverage was from 1987 to 2023.

IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization

IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization.  This data is split up into two folders.  "stationary_magnetometer_data" 0.1Hz samples of a RM3100 magnetometer that was kept in one position from July 2022 through February 2023. The data is partitioned into separate files for analytical convenience of our research. Each file here is a CSV with a timestamp (synchronized with chrony to a central computer) and the three components of the measured magnetic field. We did not calibrate this stationary magnetometer. "UAV_and_mocap_data" has many subfolders. Each subfolder is labeled by a date and a small description of the goals for that test segment. There is a single "EXPLANATION" file in each subfolder that gives more detail on the provided data. The data here includes the trajectory flown by the UAV, outdoor calibration data to adjust the raw magnetometer measurements, and IMU/motion capture data for each listed flight test. The EXPLANATION file should explain what trajectory was flown for each individual flight test.

Data for: Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging

Magnetic Resonance Imaging measurement data used in the PhD thesis "Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).   Further information about the individual datasets: data_invivo_b0map     Type:       B0 Map     Object:     Single-slice of volunteers brain     Sequence:   Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence     TR|TE1|TE2 [ms]: 400|4.92|7.38     FA [deg]:   60     FOV [mm]:   240 data_invivo_b1map     Type:       B1 Map     Object:     Single-slice of volunteers brain     Sequence:   Preconditioned RF pulse with TurboFLASH Readout     TR|TE [ms]: 6830|2.19     FA [deg]:   8     FOV [mm]:   240 data_invivo_irflash     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR FLASH     TR|TE [ms]: 3.8|2.26     FA [deg]:   8     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   7 data_invivo_irbssfp     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   45     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   7 data_invivo_irbssfp_shim     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   45     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   7     missing shim break data_vfa_b0map     Type:       B0 Map     Object:     Single-slice of volunteers brain     Sequence:   Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence     TR|TE1|TE2 [ms]: 400|4.92|7.38     FA [deg]:   60     FOV [mm]:   240 data_vfa_b1map     Type:       B1 Map     Object:     Single-slice of volunteers brain     Sequence:   Preconditioned RF pulse with TurboFLASH Readout     TR|TE [ms]: 6830|2.19     FA [deg]:   8     FOV [mm]:   240 data_vfa_irflash     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR FLASH     TR|TE [ms]: 3.8|2.26     FA [deg]:   8     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13 data_vfa_irbssfp_20     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   20     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13 data_vfa_irbssfp_40     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   40     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13 data_vfa_irbssfp_45     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   45     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13 data_vfa_irbssfp_50     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   50     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13 data_vfa_irbssfp_60     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   60     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13 data_vfa_irbssfp_70     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   70     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13 data_vfa_irbssfp_77     Type:       Radial Single-Shot Dataset     Object:     Single-slice of volunteers brain     Sequence:   IR bSSFP     TR|TE [ms]: 4.5|2.25     FA [deg]:   77     T_RF [ms]:  1     BWTP:       4     FOV [mm]:   240     #Tiny GA:   13

XRF maps and line scans project files belonging to XRF instrument report. Identification of ink components through XRF analysis of Azzolino documents.

RTX Project files. Details given in supporting information  <a href="https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0283539.s002">S2 File. </a>XRF instrument report. Identification of ink components through XRF analysis of Azzolino documents. <a href="https://doi.org/10.1371/journal.pone.0283539.s002">https://doi.org/10.1371/journal.pone.0283539.s002</a> (DOCX) Belonging to publication  Lagerqvist Alidoost A, Hacke M, Winther T, Sandström T (2023) A closer look at the Azzolino collection. PLOS ONE 18(4): e0283539. <a href="https://doi.org/10.1371/journal.pone.0283539">https://doi.org/10.1371/journal.pone.0283539</a>

opencc-by-4.0Mar 2022View details →

UKCP18 RCM precipitation and temperature bias corrected using ISIMIP3BA change-preserving quantile mapping.

We present bias-corrected UK Climate Projections 2018 (UKCP18; Met Office Hadley Centre, 2018) regional datasets for temperature, precipitation, and potential evapotranspiration (1981-2080). All 12 members of the 12 km ensemble were corrected using quantile mapping and a change-preserving variant (Lange, 2019; Lange, 2020). Both methods effectively reduce biases in multiple statistics, while maintaining projected climatic changes. We provide guidance on using the bias-corrected datasets for climate change impact assessment. Please find a detailed description and evaluation in the metadata and accompanying data paper (Reyniers et al., 2025). --- Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, 8 March 2022. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a> Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). GMD, 12(7), 3055-3070. Lange, S. (2020). ISIMIP3BASD (2.4.1). Zenodo. https://doi.org/10.5281/zenodo.3898426 Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., & He, Y. (2025). Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain. Earth System Science Data, 2025, 17(5), 2113–2133.

Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets

The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. The original data is from 1k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor. Due to time constraints during training, the datasets were subsampled to reads that map to chromosome 21 only. The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license. There is an additional count matrix in Anndata format created from full datasets.

TSlam-marking-mapping-fabrication-recording

The dataset contains the camera recordings with timestamps for evaluating the TSlam system developed at IBOIS. The camera recordings cover 3 phases of TSlam evaluation: - marking by hand - application of tslam tags - mapping of tslam tags

opencc-by-4.0Jun 2022View details →

Data for "Rapid mapping of alloy surface phase diagrams via Bayesian evolutionary multitasking"

For the ORR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pd-Ag(111) surface are stored in ads_PdAg_111_dft.db. For the SMR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pt-Ni(111), (100) and (311) surfaces are stored in ads_PtNi_111_dft.db, ads_PtNi_100_dft.db and ads_PtNi_311_dft.db, respectively. The 76,265 tasks (combining 15,253 SMR conditions with 5 exploration parameters) used for the BEM runs in the SMR study can be found in bem_smr_tasks.csv. All the input files and scripts for BEM high-throughput screening (for both ORR and SMR studies), DFT calculations, EMT benchmarks, SGCMC simulations, structure generation and plotting (e.g. surface free energy diagrams and 2D phase diagrams) are all provided in inputs_and_scripts.zip.

79 Block--63k or 50k scale Maps of South Asia

189 maps of the Survey of India covering block 79. In the time since this first batch of maps (see the list below) was posted additional maps of Block 79 have become available.  These additional maps are posted on a separate page, and can be reached by clicking the link(s) below.  Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 79. <table> <tbody> <tr> <td><a href="../records/10460656">63k/50k Maps of South Asia--Block 79--additional 1--20240105</a></td> </tr> </tbody> </table>

opencc-by-4.0May 2023View details →

Coastal flood maps and extreme sea levels for the German Baltic Sea coast

The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H). The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will also be accessible via the preprint given below): Kiesel, J., Lorenz, M., König, M., Gräwe, U., and Vafeidis, A. T.: A new modelling framework for regional assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-275, in review, 2023. The dataset contains: - the location and names of flood boundary stations - the boundary conditions provided by the coastal ocean model at each of the flood boundary stations for all storm surge events simulated in the study cited above - the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model - the spatially explicit results of the extreme value analysis for every grid cell in the coastal ocean model - the modelled monthly peak water levels between 1961 and 2018 for every grid cell of the coastal ocean model - the modelled timeseries of water levels during the storm surge from January 2nd 2019 and the entire hindcast period (1961-2018) for all tide gauges along the German Baltic Sea coast For further information, we refer the reader to the readme file in this dataset or the publication itself.