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”
Interpolated maps of bird density and flight vector over Europe
<p>This dataset contains the interpolated values of bird density and bird flight speed (N-S and E-W) resulting from the methodology presented in [<em>reference</em>].The methodology is explained in less detail at <a href="https://rafnuss-postdoc.github.io/BMM/">rafnuss-postdoc.github.io/BMM</a>. The resulting interpolation is a probability distribution (define the probability of each value to occurs). Only the median, quantile 10 and 90 are given in this file. </p> <p>The spatio-temporal grid has a resolution of 0.2° in latitude (43°-68°) and longitude (-5°-30°) and 15 minutes in time (19 September to 10 October 2016), resulting in 127x176x2017 nodes. Over this large data cube, the estimation are only computed at the nodes located (1) over land, (2) within 200km of the nearest radar and (3) during nighttime.</p> <p>The same dataset can be visualised on a dedicated web interface: <a href="https://bmm.raphaelnussbaumer.com/">www.bmm.raphaelnussbaumer.com</a> and data can be queried on a API (<a href="https://github.com/Rafnuss-PostDoc/BMM-web#how-to-use-the-api">documentation</a>).</p> <p>The csv file is structured as a table with the following columns:</p> <ul> <li>density_estimation: Median bird density [bird/km^2] (quantile 50)</li> <li>density_quantile10: Quantile 10 of bird density [bird/km^2]</li> <li>density_quantile90: Quantile 90 of bird density [bird/km^2]</li> <li>speedu_estimation: Mean bird speed east(+)/west(-) [m/s]</li> <li>speedu_std: Standard deviation of bird speed east(+)/west(-) [m/s]</li> <li>speedv_estimation: Mean bird speed north(+)/south(-) [m/s]</li> <li>speedv_std: Standard deviation of bird speed north(+)/south(-) [m/s]</li> <li>latitude</li> <li>longitude</li> <li>time</li> </ul> <p> </p>
Software Variability Tools: Industrial Survey and Systematic Mapping Study
<p>Software Variability Tools: Industrial Survey and Systematic Mapping Study</p>
Hydroperiod maps of Donana for 2015/2016, 2016/2017 and their accompanying INSPIRE metadata XML files
<p>The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands.</p> <p>The hydroperiod maps for Donana are named: "Donana_Hydroperiod_from_1st_Sept_2015_to_31st_Aug_2016_using_Sentinel_2_and_Landsat_inundation_maps.tif" and "Donana_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". Their pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. They were generated by interpolating satellite-derived inundation maps falling within the period indicated in their filenames.</p> <p>The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.</p>
Hydroperiod map of Camargue for 2016/2017 and its accompanying INSPIRE metadata XML file
<p>The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands.</p> <p>The hydroperiod map of Camargue is named: "Camargue_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename.</p> <p>The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.</p>
Hydroperiod map of Danube Delta for 2016/2017 and its accompanying INSPIRE metadata XML file
<p>The annual hydroperiod of wetlands, which is affected by global trends and human activities, is a critical<br> ecological parameter that shapes aquatic plants’ and animals’ distribution and determines available habitat<br> for many of the living organisms. Thus, its estimation is useful for the sustainable management of wetlands.</p> <p>The hydroperiod map of Danube Delta is named: "Danube_Delta_Hydroperiod_from_1st_Sept_2016_to_31st_Aug_2017_using_Sentinel_2_inundation_maps.tif". The pixel values range from 0 to 365 (or 366 for leap years) and denote the number of days a pixel is inundated within a year. The map is generated by interpolating satellite-derived inundation maps falling within the period indicated in its filename.</p> <p>The interpolation approach is the following: For two dates separated by n days, the occurrence of water is compared. If a pixel is inundated on both dates, then it is assumed inundated for n-days. If a pixel is not inundated on both dates, then it is assumed inundated for n/2 days. In the hydroperiod map, the total number of inundation days per pixel is determined by accumulating the inundated days throughout the desired time period.</p>
Pre-eruption ground deformation maps at Kilauea (Hawai`i, USA): 2014-2017 and 2018.
<p>Vertical displacement (velocity) maps of Kilauea (Hawai`i, USA) derived from InSAR, for the time periods 2014-2017 and 2018. Vertical velocity data were obtained by processing Sentinel-1 ascending and descending SAR data (tracks 124 and 87, respectively). Data were processed using the JPL-developed InSAR Scientific Computing Environment (ISCE) open-source software package, and further time-series analysis was performed using the MintPy software toolbox (Miami INsar Time-series software in PYthon), developed at the University of Miami. An SRTM-derived Digital Elevation Model is also provided. </p> <p>These data were generated for figures in: Farquharson, J. I. and Amelung, F. [2020], "<em>Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano.</em>" <a href="https://doi.org/10.1038/s41586-020-2172-5">https://doi.org/10.1038/s41586-020-2172-5</a></p> <p> </p>
Identification of novel genes involved in phosphate accumulation in Lotus japonicus through Genome Wide Association mapping of root system architecture and anion content
<p>130 Lotus japonicus accessions were used. The names and accession numbers are<br> listed in S6 Table. Seeds were scarified with sandpaper and then sterilized 14 minutes in 0.05%<br> sodium hypochlorite. Subsequently, seeds were rinsed and washed 5 times in sterile distilled<br> water. For the germination, seeds were positioned in imbibed filter paper, in sterile Petri dishes,<br> and wrapped in aluminium foil. After 3 days at 21°C, young seedling were transferred to square<br> plates (12 x 12 cm) containing growth medium. Both media used in this<br> study were based on Long-Ashton solution (with two levels of phosphate concentration -20 or<br> 750 μM, LP or HP, respectively) with 0.8% MES buffer (Duchefa Biochemie,<br> Haarlem, The Netherlands), 0.8% agarose (to minimize phosphate contamination), and adjusted<br> to pH 5.7 with 1M KOH. After adding the medium, plates were dried, closed, overnight in a<br> sterile laminar flow hood. Two accessions, with four replicates per each accession, were placed<br> on each plate. Each plate was replicated, with mirrored position of each accession to minimize<br> any positional growth effects. Plates were placed vertically, and plants grown under long-day<br> conditions (21°C, 16 h light/8 h dark cycle) with white light bulbs emitting 50 μmol/m 2 /s and<br> roots were exposed to light. Every day at the same time, the racks were transported to the image<br> acquisition room where images of each plate were acquired with eight Epson V600 CCD flatbed<br> color image scanners (Seiko Epson) and then immediately returned to the growth chamber.</p>
GTEx v8 fine mapping on eQTL and sQTL
<p># Data usage policy</p> <p>When using this data, you must acknowledge the source by citing the publication "Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits" (https://doi.org/10.1101/814350).</p> <p># GTEx-GWAS integration: Finemapping</p> <p>This package contains DAP-G results on GTEx v8 eQTL and sQTL data.<br> See ([DAP-G software](https://github.com/xqwen/dap)) for details.<br> We used only European individuals and variants with MAF>0.01, on genes that are annotated as `protein_coding` or `lncRNA`. <br> DAP-G `ld_control` parameter was 0.75.</p> <p>The results were analyzed in [this preprint](https://www.biorxiv.org/content/10.1101/814350v1)</p> <p>## Contents</p> <p>```<br> finemapping/<br> |-- README_finemapping.md<br> |-- dapg_eqtl.tar<br> `-- dapg_sqtl.tar<br> ```<br> Unpack each tarball with a command like `tar -xvpf dapg_sqtl.tar`</p> <p>For every tissue:</p> <p>* `{tissue}.variants_pip.txt.gz` contains the variants' posterior inclusion probabilities at being causal for every gene.<br> * gene: gene id (or intron id)<br> * rank: ranking of the variant according to its PIP (see below)<br> * variant_id: gtex variant id<br> * pip: posterior inclusion probability of the variant in the causal models<br> * log10_abf: approximate Bayes factor (-log10)<br> * cluster_id: id of cluster to which the variant belongs <br> * `{tissue}.models_variants.txt.gz` contains, for every model contemplated by DAPG, the list of variants involved. Most of them have single variant.<br> * `{tissue}.model_summary.txt.gz` contains, for every analized gene, a summary of the modes such as expected number of causal variants<br> * gene: gene id (or intron id)<br> * pes: posterior expected model size (i.e. number of causal variants)<br> * pse_se: standard error of the above<br> * log_nc: dapg undocumented statistic<br> * log10_nc: dapg undocumented statistic<br> * `{tissue}.models.txt.gz` for every analyzed gene:<br> * gene: gene id (or intron id)<br> * model: number (serving as a model name)<br> * n: number of variants (0 for null model)<br> * pp: posterior inclusion probability of the model<br> * ps: posterior score<br> * `{tissue}.clusters.txt.gz` for every analyzed gene:<br> * gene: gene id (or intron id)<br> * cluster: number (serving as cluster name)<br> * n_snps: number of variants in the cluster<br> * pip: posterior inclusion probability<br> * average_r2: average correlation within the cluster<br> * `{tissue}.cluster_correlations.txt.gz`: upper triangular matrix of correlations among clusters </p> <p> </p> <p> </p> <p># Disclaimer</p> <p>The data is provided "as is", and the authors assume no responsibility for errors or omissions. <br> The User assumes the entire risk associated with its use of these data. <br> The authors shall not be held liable for any use or misuse of the data described and/or contained herein. <br> The User bears all responsibility in determining whether these data are fit for the User's intended use. </p> <p>The information contained in these data is not better than the original sources from which they were derived,<br> and both scale and accuracy may vary across the data set. <br> These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics<br> appropriate for applications that potential users of the data may contemplate. <br> <br> The user is responsible to comply with any data usage policy from the original GWAS studies;<br> refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1)<br> to identify their respective Consortia's requirements.</p> <p><br> THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND,<br> EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br> FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.<br> IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,<br> WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br> OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.</p>
Cutting through the Jungle: Disambiguating Model-based Traceability Terminology — Terminology Mapping Table
<p>This dataset contains the mapping of model-based traceability terminology developed in Holtmann et al. to the secondary sources that have been used as part of the validation of the terminology.</p>
18X7 Human brain field maps for shim coil designs
<p>128 B0 field maps of head brains of 18 different healthy adult volunteers in seven different head positions.</p>
Pretty Deep Maps: a detailed all-sky astronomical atlas
<p>The <em>Pretty Deep Maps</em> astronomical atlas is a large hyperlinked collection of charts and indices in PDF format covering the entire sky, in the main to a magnitude of 18, with star-dense sections at a lower magnitude, and selected regions to a deeper magnitude. The atlas is intended as a convenient, portable, deep visual representation of all-sky astronomical datasets, and can be used for tasks such as identification of objects in photographs and observation planning. The charts plot approximately 117k bright stars, 600 million field stars, 2.4 million galaxies, 277k variable stars, 110k multiple stars, 168k quasars, 8955 galaxy groups, 5250 galaxy clusters, 5004 dark nebulae, 2411 planetary nebulae, 1249 bright nebulae, 2167 open clusters, 159 reflection nebulae and 157 globular clusters. The celestial sphere is divided into 6787 charts, each covering 3 degrees of declination at a map scale of 11.74" per mm. Charts employ a gnomonic projection. The charts are accompanied by a set of sortable indices. The atlas can be used on any device with a PDF reader capable of supporting within- and cross-document links. </p> <p>The dataset is described in a detailed technical document (guide.pdf). <br> </p>
РИС. 1. Карта района исследований с укаЗанием станций отбора проб (А) и плотности поселениЯ R. venosa (ЭкЗ./м²). FIG. 1. Map of the study area with indication the sampling sites and the population density (A) of R. venosa (specimens/m²). in Rapana venosa (Valenciennes, 1846) Залива ДонуЗлав и прилегаюЩей акватории Чёрного морЯ
РИС. 1. Карта района исследований с укаЗанием станций отбора проб (А) и плотности поселениЯ R. venosa (ЭкЗ./м²). FIG. 1. Map of the study area with indication the sampling sites and the population density (A) of R. venosa (specimens/m²).
Fig. 2 in (Hem.: Pentatomidae) in flooded rice crop in Southern Brazil Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Fig. 2. Interpolation maps by multiquadric equations of spatiotemporal distribution of occurrence categories of Tibraca limbativentris [I = no insect (green), II = adult (red), III = nymphs (pink), IV = adult + nymphs (blue)] in flooded rice crop in Southern Brazil, 2011/2012 crop season. *Thematic maps: (A) 11/19/11 [V4]; (B) 12/03/11 [V6]; (C) 12/17/11 [V8/V9]; (D) 01/07/12 [V11]; (E) 01/21/12 [R1]; (F) 02/02/12 [R5]; (G) 02/15/12 [R9]; (H) 02/29/12 [post-harvest = crop residues destroyed]. Phenological stage according to Counce et al. (2000).
Fig. 46. Partial truncated map for Central and South America with species distributions for the M in Taxonomic notes on social wasps of the groups of Mischocyttarus wagneri (Buysson 1908) and M. barbatus Richards 1945 (Hymenoptera, Vespidae, Polistinae)
Fig. 46. Partial truncated map for Central and South America with species distributions for the M. barbatus group, and with the pooled distribution of the group of M. wagneri (see next figure for detailed representation of distributions of species of this group).
Fig. 1. Map showing sites where P in Wing shape is influenced by environmental variability in Polietina orbitalis (Stein) (Diptera: Muscidae)
Fig. 1. Map showing sites where P. orbitalis populations were collected: 1. Mbaracayú (25◦17Ɩ S, 54◦49Ɩ W) in Paraguay and 2. Fênix (23◦54Ɩ S, 51◦58Ɩ W), 3. Jundiaí do Sul (23◦26Ɩ S, 50◦14Ɩ W), 4. Guarapuava (25◦23Ɩ S, 51◦ 27Ɩ W), 5. Ponta Grossa (25◦ 05Ɩ S, 50◦ 09Ɩ W) and 6. Colombo (25◦17Ɩ S, 49◦13Ɩ W) in Brazil.
Figure 1. Map showing 6 in Distribution and ecology of Ostracoda (Crustacea) from troughs in Turkey
Figure 1. Map showing 6 provinces (Bolu, Ordu, Erzincan, Kahramanmaraş, Gaziantep, Van) with 105 sampling sites.
Text-fig. 4. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAP. For legend see Text-fig. 3. in Palaeoenvironmental Analysis Of The Messinian Macrofossil Floras Of Tossignano And Monte Tondo (Vena Del Gesso Basin, Romagna Apennines, Northern Italy)
Text-fig. 4. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAP. For legend see Text-fig. 3.
Text-fig. 2. Map showing the distribution of localities included in the treatment of Brown (1962), based on the coordinates presented in Appendix Table 1. Localities are distributed across New Mexico (NM), Colorado (CO), Wyoming (WY), Montana (MT), South Dakota (SD) and North Dakota (ND). Also plotted are cited Canadian localities: Joffre Bridge, in Alberta (AB), and Ravenscrag, in Saskatchewan (SK). Map generated with Map-It (2014) website. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. 2. Map showing the distribution of localities included in the treatment of Brown (1962), based on the coordinates presented in Appendix Table 1. Localities are distributed across New Mexico (NM), Colorado (CO), Wyoming (WY), Montana (MT), South Dakota (SD) and North Dakota (ND). Also plotted are cited Canadian localities: Joffre Bridge, in Alberta (AB), and Ravenscrag, in Saskatchewan (SK). Map generated with Map-It (2014) website.
Text-fig. 1. Simplified map of the Bohemian Cretaceous Basin (BCB, the Czech Republic), showing the occurrence of Filogranula cincta (GOLDFUSS) in the Bohemian Cretaceous Basin: Předboj, Plaňany, Velim-Skalka, Kutná Hora-Karlov, Chrtníky, Novosedlice ("Weisskirchlitz"), Želenice nad Bílinou ("Schillinge") and Klokočské Loučky. in Filogranula Cincta (G , 1831), A Serpulid Worm (Polychaeta, Sedentaria, Serpulidae) From The Bohemian Cretaceous Basin
Text-fig. 1. Simplified map of the Bohemian Cretaceous Basin (BCB, the Czech Republic), showing the occurrence of Filogranula cincta (GOLDFUSS) in the Bohemian Cretaceous Basin: Předboj, Plaňany, Velim-Skalka, Kutná Hora-Karlov, Chrtníky, Novosedlice ("Weisskirchlitz"), Želenice nad Bílinou ("Schillinge") and Klokočské Loučky.
Text-fig. 1. A – outline map of the Slovak Republic, Gombasek Quarry marked; B – Gombasek needle – Gombasecká ih a in the Gombasek Quarry, the sample was collected at the right side of the pillar; C – typical lithology, gray coloured clay. in Tracing Of Palynomorphs In The Eastern Slovakian Karst
Text-fig. 1. A – outline map of the Slovak Republic, Gombasek Quarry marked; B – Gombasek needle – Gombasecká ih a in the Gombasek Quarry, the sample was collected at the right side of the pillar; C – typical lithology, gray coloured clay.
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.