Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,117
datasets available to search
ShareScore release 0.9.0
Dataset results
13,117 results for “Maps”
253k/250k Maps of South Asia--additional 1--20240421
<p>94 New map files to add to the set of 253k/250k Maps of South Asia. An updated Index Map is also included in this new set of maps.</p>
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F).
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D).
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D).
РИС. 5. ИЗвестные находки Monacha cartusiana на Западе Украины. A. Анатомически проверенные авторами статьи. B. Определенные только по раковинам или беЗ учета анатомических раЗличий между M. cartusiana и M. claustralis. ИЗЗа масШтаба картосхем находки в блиЗко расположенных населенных пунктах объединены в одну точку. FIG. 5. Known records of Monacha cartusiana in Western Ukraine. A. Anatomically examined by the authors of this paper. B. Identifed only by shell or without regard to anatomical differences between M. cartusiana and M. claustralis. Due to the scale of the schematic maps, the findings in closely located settlements are combined into one point. in Monacha claustralis и M. cartusiana (Gastropoda, Hygromiidae) - два криптических вида антропохорных наЗемных моллюсков на Западе Украины
РИС. 5. ИЗвестные находки Monacha cartusiana на Западе Украины. A. Анатомически проверенные авторами статьи. B. Определенные только по раковинам или беЗ учета анатомических раЗличий между M. cartusiana и M. claustralis. ИЗЗа масШтаба картосхем находки в блиЗко расположенных населенных пунктах объединены в одну точку. FIG. 5. Known records of Monacha cartusiana in Western Ukraine. A. Anatomically examined by the authors of this paper. B. Identifed only by shell or without regard to anatomical differences between M. cartusiana and M. claustralis. Due to the scale of the schematic maps, the findings in closely located settlements are combined into one point.
Context Map for eshopContainers Benchmark
<p>This is part of the outcomes from the paper titled "Deriving Microservice Architectural Perspectives Using Static Code Analysis For C# Platform"</p>
Рис. 1. Карты Приморского края (А) и юЖного Приморья (В) с укаЗанием располоЖения стоянки Теляковского 2 и фотография побереЖья б. Теляковского (С); оранЖевая стрелка укаЗывает на располоЖение стоянки). Fig. 1. Maps of Primorsky Krai (Territory) (A) and its southern area (south Primorye) (B) showing location of Telyakobskogo 2 site and a photograph of the coast of Telyakovskogo Bay (C); orange arrow shows location of the site). in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 1. Карты Приморского края (А) и юЖного Приморья (В) с укаЗанием располоЖения стоянки Теляковского 2 и фотография побереЖья б. Теляковского (С); оранЖевая стрелка укаЗывает на располоЖение стоянки). Fig. 1. Maps of Primorsky Krai (Territory) (A) and its southern area (south Primorye) (B) showing location of Telyakobskogo 2 site and a photograph of the coast of Telyakovskogo Bay (C); orange arrow shows location of the site).
GRASS GIS database for CASAS-PBDM (www.casasglobal.org) geospatial mapping and analysis
<p>GRASS GIS database for geospatial mapping and analysis of physiologically based demographic modeling (PBDM) implemented by the Center for the Analysis of Sustainable Agricultural Systems (CASAS, <a href="https://www.casasglobal.org/" target="_blank" rel="noopener">www.casasglobal.org</a>).</p> <p>The <code>casas_gis_grass8data.zip</code> archive includes data updated for use with GRASS GIS version 8.</p>
Rapid mapping inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023
<p>The dataset contains the spatial explicit inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023. Hybrid pixel-based and object-based image analysis approach was used to generate the rapid mapping products. The fully automated supervised procedure relies on change detection analysis, based on quantitative variation of vegetation cover in pre-event and post-event remote sensing imagery. Two separate inventories were generated using:<br>- Copernicus Sentinel-2 MSI satellite imagery<br>- PlanetScope SuperDove satellite imagery</p> <p>The dataset is a spatial representation vector polygons representing potential landslides, distributed in GeoPackage format, complemented with confidence measure. It contains modified Copernicus Sentinel data, available at no cost from Copernicus Open Access Hub. Use of PlanetScope images is under license: ©Planet Labs PBC, CC BY-NC-SA 2.0.</p>
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Maps of topsoil (0-30 cm) properties of Tuscany (Italy)
<p><span>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></p> <p><span>The topsoil (0-30 cm) properties maps are prepared to evaluate soil ecosystem services in SERENA/EJP-Soil and for applying SOC loss Cookbook and SOIL Loss Cookbook. In particular Soil Organic Carbon content map was directly considered as an application of SOC loss Cookbook (DOI: </span><a href="https://doi.org/10.5281/zenodo.13951265"><span>10.5281/zenodo.13951265 </span></a><span>Version 3).</span></p> <p><span>They are based on Tuscany Region soil database available at Geoscopio (https://www502.regione.toscana.it/geoscopio/pedologia.html) and on point soil data not freely available (Lamma Consortium). More information and requests to: </span><a href="mailto:info@lamma.toscana.it"><span>info@lamma.toscana.it</span></a><span>.</span></p> <p><span>In accordance with the methodology reported in the Soil Organic Carbon Mapping Cookbook (Yigini et al., 2018), the following soil properties were mapped for all Tuscany Region:</span></p> <ul> <li><span>soil organic carbon content (dag/kg),</span></li> <li><span>soil organic carbon stock (t/ha),</span></li> <li><span>textural fractions (sand, silt and clay, USDA limits, dag/kg),</span></li> <li>rock fragments (vol/vol),</li> <li>pH in water,</li> <li>bulk density (g/cm3).</li> </ul> <p><span>They were obtained through Digital Soil Mapping (DSM) approach, based on correlations with numerous environmental factors and using Random Forest algorithm.</span></p> <p><span>All the maps have a 100 m spatial resolution.</span></p>
Data for: Mapping the Limits of Passive Samplers in Water: Chemical Space Coverage Using Nontargeted LC-HRMS Analysis
<p>This dataset provides files for passive samplers nad blanks analyzed by LC-HRMS fullscan DIA MS2.</p> <p>Excel file provides information about passive samplers, sampling site and sample files.</p>
Public Procurement of Innovation stakeholders map
<p>This interactive map contains a database of Public Procurement of Innovation (POI) stakeholders, including public buyers, procures, SMEs, start-ups, spin-offs, universities, incubators, technology centres, projects & other relevant actors.</p> <p>Go through the<a href="https://procedin.eu/stakeholder-map/"><strong> interactive stakeholders map</strong></a>, find the best contact and set up a call to enhance your businesses together.</p> <p><br>You can also explore F6S Platform or Tenderio, your place for match making.</p> <p> </p> <p>Additionally, you can find valuable information at: <br>– <a href="https://procurement-forum.eu/">Procurement Forum</a><br>– <a href="https://eafip.eu/">European Assistance for Innovation Procurement (EAFIP)</a> <br>– <a href="https://sustainable-procurement.org/sustainable-public-procurement/">Sustainable Procurement Platform</a></p>
Datasets for: Integrative Mapping of Human CD8+ T Cell in Inflammation and Cancer
<p>Files for the integrated pan-disease CD8+ T cell atlas: </p> <ul> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.all_genes.h5ad.gz?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.all_genes.h5ad.gz</a> : 19957 genes raw matrix </li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.X_gex.npy?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.X_gex.npy</a> : scatlasavae model embedding</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.clone_subtype.csv?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.clone_subtype.csv</a> : clone type definition</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.supervised.model</a>. scatlasavae model checkpoint</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : Tex subset of 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model</a>. scatlasavae model checkpoint for the Tex subset</li> </ul> <p>Files for the TILs CD8+ T cell atlas</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_chu.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_chu.h5ad</a>. the Chu <em>et al.</em>, 2023 Dataset</li> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_zheng.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_zheng.h5ad.</a> The Zheng <em>et al.</em>, 2021 Dataset</li> </ul> <p>Files for the transfer (query) datasets:</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/Zhang_LC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Zhang_LC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_TIL.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_TIL.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_PBMC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_PBMC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Watson_MELA.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Watson_MELA.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bassez_BC.cohort1.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bassez_BC.cohort1.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bi_RCC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bi_RCC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Caushi_NSCLC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Caushi_NSCLC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Liu_TNBC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Liu_TNBC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Borra%CC%80s_2024_Colorectal_cancer.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Borràs_2024_Colorectal_cancer.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Garner_2023_MAIT.h5ad" target="_blank" rel="noopener">Garner_2023.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Vorkas_2022_MAIT.h5ad?download=1" target="_blank" rel="noopener">Vorkas_2022_MAIT.h5ad</a></li> </ul>
reference genome used for scRNA-seq mapping with CellRanger in the method spatial-scERA
<p>The modified <em>Drosophila </em>melanogaster (dm6) reference genome used for the mapping with CellRanger in the method paper about spatial-scERA</p> <p>The genome is composed of the original genome from EnsembleMetazo website (BDGP6.46.110). An addition of 26 chromosomes (one for the plasmid construct and 25 for the tested enhancer sequences) is also present to allow for the mapping of mRNAs comming from our constructs. </p>
Transformation Rate Maps of Dissolved Organic Carbon in the Contiguous U.S.
<p>We develop two new maps of the dissolved organic carbon (DOC) transformation rate (\(P_r\)) over the contiguous United States. Those maps are derived by combining the USGS riverine DOC observations, soil organic carbon (SOC) data from two sources—HWSD v1.2 and SoilGrids 2.0, and the watershed characteristics from two existing datasets medium-resolution NHDplus and ScienceBase, and state-of-the-art machine learning techniques. </p>
High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany
<p>Open access publication under review.</p> <p>Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels"><strong>this Earth Engine app</strong></a> to explore the data interactively.</p> <p> </p> <p>Abstract:</p> <p>Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.</p> <p><br>We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),<br>Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass<br>models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.<br>Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.</p> <p><br>Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation<br>with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)<br>rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of<br>model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.<br>The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.</p> <p> </p> <p>Data:</p> <p>This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.</p> <p> </p> <p>Disclaimer:<br>Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.</p>
Mapping the DeFi Crime Landscape: An Evidence-based Picture
<p><br><strong>README - Crime Events Dataset</strong></p> <p>This document provides a detailed overview of the structure of the dataset for the paper: "Mapping the DeFi crime landscape: An Evidence-based Picture", published in the Journal of Cybersecurity, and available here: https://academic.oup.com/cybersecurity/article/11/1/tyae029/7962044</p> <p>The following fields are included, each representing different aspects of the events collected.</p> <p><strong>Data Fields</strong></p> <p><strong>1. unique_key</strong><br> <em>Description: </em>A unique number assigned to identify each event in the dataset.</p> <p><strong>2. Agregators </strong><br> <em>Description: </em>The sources where the event is listed. Aggregators include:<br> - De.Fi REKT<br> - SlowMist<br> - CryptoSec (rebranded to ChainSec as of February 2023)</p> <p><strong>3. DeFi actor involved </strong><br> <em>Description: </em>The name of the DeFi actor involved in the event (target, perpetrator, or intermediary).<br> Sources: <br> - On De.Fi REKT: Found as the "Title" of the event’s listing.<br> - On SlowMist: Found under the “Hacked target” title.<br> - On CryptoSec: Found in the "Title" of the event’s listing with the date.</p> <p><strong>4. REKT URL </strong><br> <em> Description: </em>The URL to the event's listing on De.Fi REKT.<br> <em>Process: </em>Found by searching for the DeFi actor involved in the REKT Database: https://de.fi/rekt-database</p> <p><strong>5. SlowMist URL </strong><br> <em>Description: </em>The URL to the event's listing on SlowMist.<br> <em>Process: </em>Available via https://hacked.slowmist.io/search/. Note that searching the actor's name will lead to the event but without an individualized URL.</p> <p><strong>6. CryptoSec URL </strong><br> <em>Description: </em>The URL to the event's listing on CryptoSec.<br> <em>Process: </em>Found at https://chainsec.io/defi-hacks/. Events are listed on a single page; use traditional keyboard search to locate specific events.</p> <p><strong>7. Aggregator Summary </strong><br> <em> Description</em>: A summary of the event provided by the aggregator.<br> <em>Sources: </em><br> - On De.Fi REKT: Found under "Quick Summary" and "Details of the Exploit".<br> - On SlowMist: Under "Description of the event".<br> - On CryptoSec: Below the title in quotation marks.</p> <p><strong>8. Aggregator sources URL </strong><br> <em>Description: </em>The URLs of references linked by the aggregator in the event’s listing.<br> <em>Sources:</em><br> - On De.Fi REKT: Found at the bottom by clicking "Source" or "Archived link".<br> - On SlowMist: Found by clicking "View Reference Sources".<br> - On CryptoSec: Available by clicking the source’s name at the end of the summary.</p> <p><strong>9. Event date </strong><br> <em>Description: </em>The date the event occurred.<br> <em>Sources:</em><br> - On De.Fi REKT: Listed under the "Date" field.<br> - On SlowMist: At the top right of the listing.<br> - On CryptoSec: Listed in parentheses behind the actor’s name.</p> <p><strong>10. Event year </strong><br> <em> Description: </em>The year the event occurred, extracted from the Event date.</p> <p><strong>11. Stolen amount USD </strong><br> <em>Description:</em> The total amount stolen, converted to USD.<br> <em>Sources:</em><br> - On De.Fi REKT: Found under "Funds lost".<br> - On SlowMist: Under the title “Amount of loss”.<br> - On CryptoSec: Behind the title "Amount stolen".<br> <em>Note: </em>If needed, conversions were manually performed using CoinMarketCap’s historical data as explained in the paper. </p> <p><strong>12. Implication of actor </strong><br> <em>Description: </em>Indicates whether the DeFi actor was a target, perpetrator, or intermediary in the event. Manually coded after reviewing the aggregator’s summary and linked sources.</p> <p><strong>13. Strategy </strong><br> <em>Description: </em>The main approach used to steal funds. Six categories are possible: Technical vulnerability, Human risks, Undetermined, Malicious use of contract, Misappropriation of funds, and Imitation. This was manually coded from the event summary and sources.</p> <p><strong>14. General tactic </strong><br> <em> Description: </em>The common techniques or methods used by malicious actors. Eleven categories are possible, defined in the appendix. Manually coded after reviewing the summary and linked sources.</p> <p><strong>15. Specific tactic </strong><br> <em> Description:</em> The precise technique used to commit the crime. Thirty-seven categories are possible, defined in the appendix. This was manually coded based on the event summary and sources.</p> <p><strong>16. Paper category </strong><br> <em>Description: </em>The main area of operation of the involved DeFi actor. Twelve categories are possible: Blockchain, Bridge, DApp, Derivatives, Exchange, Fungible Token (FT), Non-Fungible Token (NFT), Oracle, Yield, Staking, and Others. This was determined by the event summary and research on the actor.</p> <p><strong>17. Stack category </strong><br> <em>Description: </em>The technical layer of the DeFi Stack Reference (DSR) model corresponding to the paper category. Five categories are possible: DeFi Compositions (CP), DeFi Protocols (P), Cryptoassets (CA), Distributed Ledger Technology (DLT), and Interfaces (INT).</p> <p>---</p> <p>For more detailed information on the tactics, strategies, or categories used, please refer to the appendix of the dataset or the associated documentation.</p> <p>The previous version included duplicates. They have been removed in the second version.</p>
Improving 30-meter global impervious surface area (GISA) mapping: New method and dataset
<p>Timely and accurate monitoring of impervious surface areas (ISA) is crucial for effective urban planning and sustainable development. Recent advances in remote sensing technologies have enabled global ISA mapping at fine spatial resolution (<30 m) over long time spans (>30 years), offering the opportunity to track global ISA dynamics. However, existing 30 m global long-term ISA datasets suffer from omission and commission issues, affecting their accuracy in practical applications. To address these challenges, we proposed a novel global longterm ISA mapping method and generated a new 30 m global ISA dataset from 1985 to 2021, namely GISA-new. Specifically, to reduce ISA omissions, a multi-temporal Continuous Change Detection and Classification (CCDC) algorithm that accounts for newly added ISA regions (NA-CCDC) was proposed to enhance the diversity and representativeness of the training samples. Meanwhile, a multi-scale iterative (MIA) method was proposed to automatically remove global commissions of various sizes and types. Finally, we collected two independent test datasets with over 100,000 test samples globally for accuracy assessment. Results showed that GISA-new out performed other existing global ISA datasets, such as GISA, WSF-evo, GAIA, and GAUD, achieving the highest overall accuracy (93.12 %), the lowest omission errors (10.50 %), and the lowest commission errors (3.52 %). Furthermore, the spatial distribution of global ISA omissions and commissions was analyzed, revealing more mapping uncertainties in the Northern Hemisphere. In general, the proposed method in this study effectively addressed global ISA omissions and removed commissions at different scales. The generated high-quality GISAnew can serve as a fundamental parameter for a more comprehensive understanding of global urbanization.</p>
Dataset for ´´A New Detailed Global Map of Lunar Light Plains´´ research article
<p>The shapefiles (.shp) provided in this repository are the datasets for the paper ´A new detailed global map of lunar light plains´ published in PSJ journal Special Issue. </p> <p>These shapefiles can be directly imported in ArcMap/ArcPRO. The third dataset is a .tif or image of the global map for a fast and easy overview.</p> <p>Two geomorphologic maps of lunar light plains are provided as described in the article: one with an FeO wt% cut off of about 12 wt% (Area_lightplains), and the other around 8 wt% (Area_LPFeOLow). </p> <p> </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.