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.
12,237
datasets available to search
ShareScore release 0.9.0
Dataset results
12,237 results for “2019”
[2019 QSM Reconstruction Challenge] Submissions Stage 1
<p>This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
User requirements of Big Earth Data - Survey 2019
<p>The survey was conducted between November 2018 and May 2019 with the aim to find out how users working with large volumes of environmental data interact with data, what challenges they face and how they would like to use cloud-based data services in the future.</p> <p>The term Big Earth Data in this context refers to digital information about Earth, including observations, imagery, derived higher-level products, forecasts and analyses produced by computer models.</p> <p>The survey was conducted in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF) and as part of a PhD thesis on "Big Data technologies for environmental and climate data" at University of Marburg, Germany.</p> <p>The results are published in form of two articles:</p> <ul> <li>Wagemann, J., Siemen, S., Seeger, B. and J. Bendix (2021): Users of open Big Earth data - An analysis of the current state. Computers and Geosciences 2021. <a href="https://doi.org/10.1016/j.cageo.2021.104916">doi:10.1016/j.cageo.2021.104916</a></li> <li>Wagemann, J. Siemen, S., Seeger, B. and J. Bendix (2021): A user perspective on future cloud-based services for Big Earth data. International Journal of Digital Earth 2021. doi: <a href="http://doi.org/10.1080/17538947.2021.1982031">10.1080/17538947.2021.1982031</a></li> </ul> <p> </p>
Questionnaire data to research small-scale farmers' information sharing for adapting to climate change in Mozambique (2019-2020)
<p>Data collected from individual questionnaires with local communities of 4 districts of Mozambique in November 2019 and July 2020. It contains as well data from nine individual questionnaires to institutions (government and NGOs) working with local communities for their development.</p> <p>Data are replies from interviews containing open and closed questions about a) climate change adaptation options necessary for Mozambican small scale farmers, about b) the most used and preferred information sources of farmers, about c) the main barriers for a better exchange of information, and about d) proposals for improving it. The questionnaire can be consulted in Appendix A (in English and Portuguese). The open questions had the purpose to understand the causes and explanations about the themes presented. The closed questions followed a 0-5 likert scale approach, where 5 meant a very important factor and 0 non important one. This format was pursued for developing statistical analysis and comparison between the different types of participants. We used the same questions and format for interviewing farmers and stakeholders, although the questionnaire for farmers included also personal aspects like gender, age, and education.</p>
Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019
<p>An airborne mapping approach combining laser-guided imaging spectroscopy and deep learning models was used to quantify the geographic distribution of live corals to 16 m water depth throughout the eight main Hawaiian Islands. Full metadata and methods are provided in:</p> <p>Asner, G.P., N.R. Vaughn, J. Heckler, D.E. Knapp, C. Balzotti, E. Shafron, R.E. Martin, B.J. Neilson, J.M. Gove. 2020. Large-scale mapping of live corals to guide reef conservation. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2017628117.</p> <p>Asner, G.P., N.R. Vaughn, J. Heckler. 2020. Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019 (Version 3.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4292660</p> <p>Use of our data requires that you cite both of these sources together. In addition, we ask that these data be used to make the world a better place.</p> <p>The data are in standard GeoTIFF file format organized by island.</p> <p>These data files will be updated as further improvements are made.</p>
Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019)
<p>Argo-based mixed layer depth profiles derived from the CORA product as described in Whitt Nicholson Carranza. A binned 2-degree climatology was published previously:</p> <p>https://github.com/danielwhitt/globalimpacts_2019_whittetal/blob/master/MonthlyClimatology_ARGO_MLDbmax_TEOS10_Copernicus_PF_2000-2017_all_jun252019_nc.nc</p> <p>with:</p> <p>Whitt, D. B., Nicholson, S. A., & Carranza, M. M. (2019). Global Impacts of Subseasonal (< 60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth. <em>Journal of Geophysical Research: Oceans</em>, <em>124</em>(12), 8798-8831</p> <p>Contact the authors with questions. </p> <p>The chosen mixed layer depth definition is the same as "HMXL", a standard output of the Community Earth System Model (CESM) ocean component.</p>
Global Airborne Observatory: Hawaiian Islands Reef Rugosity 2019+2020
<p><strong>Summary</strong></p> <p>Coral reef rugosity maps were developed by the Global Airborne Observatory (GAO) team at the Center for Global Discovery and Conservation Science at Arizona State University. The maps show high-resolution seafloor rugosity derived from airborne imaging spectroscopy data collected by the GAO in January 2019 and January 2020.</p> <p><strong>Data Use Requirements</strong></p> <p>Use of these data must acknowledge the source its funders as:</p> <p>“The bathymetry and rugosity data maps were created by the Global Airborne Observatory, Center for Global Discovery and Conservation Science, Arizona State University. The project received financial support from the Lenfest Ocean Program, The Battery Foundation, John D. and Catherine T. MacArthur Foundation, Avatar Alliance Foundation, State of Hawaiʻi Division of Aquatic Resources, State of Hawaiʻi Department of Planning, National Oceanic and Atmospheric Administration.”</p> <p>In addition, provide citations to the following two publications on any materials or presentations utilizing the data or products and results derived from the data:</p> <p>Asner, G.P., N.R. Vaughn, C. Balzotti, P.G. Brodrick, and J. Heckler. 2020. High-resolution reef bathymetry and coral habitat complexity from airborne imaging spectroscopy. <em>Remote Sensing</em> 12:310 (doi:10.3390/rs12020310)</p> <p>Asner, G.P., N.R. Vaughn, S.A. Foo, J. Heckler, and R.E. Martin. 2021. Drivers of reef habitat complexity throughout the Main Hawaiian Islands. <em>Frontiers in Marine Science </em>8:631842. (doi: 10.3389/fmars.2021.631842)</p> <p><strong>Map Properties</strong></p> <p>There are two types of map products available as part of this collection: fine rugosity, and coarse rugosity. Except for Hawaii Island, there are three separate map files for each of the Main Hawaiian Islands (Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau). Hawaii Island was large enough that it needed to be split into quarters for manageability, and each of the three maps are available for all quarters (12 maps total). Coordinates for all maps refer to the UTM Coordinate System, Zone 4 North using datum WGS-84, with the exception of those for Hawaii Island which refer to Zone 5 North.</p> <p>The rugosity maps in meters at a 2-meter spatial resolution up to approximately 22 meters in depth, where data quality allowed. To minimize the effect of water properties, these maps are built using a blend of data from both the 2019 and 2020 collection periods. Fine-scale rugosity seeks high frequency changes on the seafloor arising from coral colonies, rocks, and other bottom features that generate local habitat variability, and coarse-scale rugosity is more responsive to variations in larger terrain features resulting from geologic, reef-scale accretion, and subsidence processes. Fine-scale rugosity maps are produced at 2-meter horizontal resolution, but each pixel represents the conditions of a 6-meter square window centered at the given pixel. Similarly, coarse-scale rugosity maps are produced at 6-meter resolution, but each pixel represents the conditions of a 54-meter square window centered at the given pixel. Rugosity values in the maps are unitless and range from 0.0 (low rugosity) to 100.0 (high rugosity).</p> <p><strong>Methods</strong></p> <p>We computed island-wide maps of rugosity at two resolution using a standard planar image rugosity metric on the GAO blended bathymetry maps (Asner, Gregory. P., Vaughn, Nicholas, & Heckler, Joseph. (2020). Global Airborne Observatory: Hawaiian Islands Live Coral Cover in 2019. Zenodo. <a href="http://doi.org/10.5281/zenodo.4292660">http://doi.org/10.5281/zenodo.4292660</a>). Prior to running the algorithm, missing data of less than two pixels in width were filled using an inverse-distance weighted average of the three nearest neighboring pixels. Fine-scale rugosity was computed using a 3 x 3 pixel (6 x 6 meters) moving window on the original 2-meter resolution bathymetric maps. Coarse-scale rugosity was computed by first down-sampling the 2-meter depth maps to 6-meter resolution using a mean filter. The rugosity metric was then computed using a 9 x 9 pixel (54.0 x 54.0 m) moving window on the 6-meter depth maps. The distribution of raw rugosity algorithm output values is extremely skewed and difficult to interpret. Thus, the rugosity maps contain rugosity values that are transformed in such a way that they have an approximate uniform [0,1] distribution. This both reduces the influence of noisy depth pixels and gives a more meaningful scale upon which to interpret the maps.</p>
Global Airborne Observatory: Hawaiian Islands Bathymetry 2019+2020
<p><strong>Summary</strong></p> <p>Bathymetry maps were developed by the Global Airborne Observatory (GAO) team at the Center for Global Discovery and Conservation Science at Arizona State University. The maps show high-resolution benthic depth, derived from airborne imaging spectroscopy data collected by the GAO in January 2019 and January 2020.</p> <p><strong>Data Use Requirements</strong></p> <p>Use of these data must acknowledge the source its funders as:</p> <p>“The bathymetry and rugosity data maps were created by the Global Airborne Observatory, Center for Global Discovery and Conservation Science, Arizona State University. The project received financial support from the Lenfest Ocean Program, The Battery Foundation, John D. and Catherine T. MacArthur Foundation, Avatar Alliance Foundation, State of Hawaiʻi Division of Aquatic Resources, State of Hawaiʻi Department of Planning, National Oceanic and Atmospheric Administration.”</p> <p>In addition, provide citations to the following two publications on any materials or presentations utilizing the data or products and results derived from the data:</p> <p>Asner, G.P., N.R. Vaughn, C. Balzotti, P.G. Brodrick, and J. Heckler. 2020. High-resolution reef bathymetry and coral habitat complexity from airborne imaging spectroscopy. <em>Remote Sensing</em> 12:310 (doi:10.3390/rs12020310)</p> <p>Asner, G.P., N.R. Vaughn, S.A. Foo, J. Heckler, and R.E. Martin. 2021. Drivers of reef habitat complexity throughout the Main Hawaiian Islands. <em>Frontiers in Marine Science </em>8:631842. (doi: 10.3389/fmars.2021.631842)</p> <p><strong>Map Properties</strong></p> <p>There are multiple map products available as part of this collection. Except for Hawaii Island, there are three separate map files for each of the Main Hawaiian Islands (Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau). Hawaii Island was large enough that it needed to be split into quarters for manageability, and each of the three maps are available for all quarters. Coordinates for all maps refer to the UTM Coordinate System, Zone 4 North using datum WGS-84, with the exception of those for Hawaii Island which refer to Zone 5 North.</p> <p>The blended bathymetry maps give modeled depth as a floating-point values in meters at a 2-meter spatial resolution up to approximately 22 meters in depth, where data quality allowed. To minimize the effect of water properties, these maps are built using a blend of data from both the 2019 and 2020 collection periods.</p> <p><strong>Methods</strong></p> <p>GAO spectrometer data for Hawaii were collected in 1.3 km wide flight line strips and the flights were planned such that individual flight lines overlap each other by about 50%, giving at least two passes of coverage per year of collection. Thus, we have two or more passes of data over most of the Hawaiian coastlines. Details of data collection protocols can be found in <strong><em>Asner et al. (2020) and Asner et al. (2021)</em></strong>. To build the blended bathymetry maps, we identified areas with sufficient sunlight and low surface glint for each flight line, and then applied GAO-created a neural network model to derive estimated depth of each pixel in such areas.</p>
Supporting data to M. Cavallaro, et al., 3'-5' crosstalk contributes to transcriptional bursting, 2019
<p>This repository contains supporting data to reference [1]. Please cite [1] if you find this repository useful. The data include:</p> <ul> <li>Flow cytometry data of HBB and HIV transgenes' expression in `.fcs` format.</li> <li>NanoString data for HIV expression.</li> <li>smFISH data for HBB and Akt1 gene expression.</li> </ul> <p>[1] M. Cavallaro, <em>et al.</em>, 3'-5' interactions contribute to transcriptional bursting, bioR$\chi$iv 514174. <a href="https://doi.org/10.1101/514174">https://doi.org/10.1101/514174</a></p>
Virtually Real eyetracking data (beeswarm) for the short film "Lux" (2019) by Wendy Pillonel
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These quicktime movie represent the Eye-Tracking Data for Scenes 1,2 and 3 of "Lux" (2019) by Wendy Pillonel in beeswarm form for both the real and the virtual version of the film.</p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
Twitter analysis of the five main political leaders during the 2019 UK electoral campaign: from 12 October to 16 December 2019
<p>The analysis was conducted from 12 October to 16 December 2019 on Twitter through the study of the five main political leaders— Boris Johnson, Jeremy Corbyn, Jo Swinson, Nicola Sturgeon, and Nigel Farage — during the 2019 UK electoral campaign.</p>
LDdata 2019 L5&A4
<p>Files with .csv are the data in csv format used for a paper entitled "Differences between two coniferous species in the seasonal fluctuation of the pressure potential of sapwood in mature trees" by Ryogo Nakada.</p> <p>"readme.txt" are explanation of the data.</p> <p>R scripts are also included.</p>
Semantic Annotation for Tabular Data with DBpedia: Adapted SemTab 2019 with DBpedia 2016-10
<p>Semantic Annotation for Tabular Data with DBpedia: Adapted SemTab 2019 with DBpedia 2016-10</p> <p>Github: https://github.com/phucty/mtab4dbpedia<br> ---------------------------------------------------------------------------------------------------------------------------------------</p> <p>CEA: </p> <ul> <li> <p>Keep only valid entities in DBpedia 2016-10</p> </li> <li> <p>Resolve percentage encoding</p> </li> <li> <p>Add missing redirect entities</p> </li> </ul> <p>CTA: </p> <ul> <li> <p>Keep only valid types</p> </li> <li> <p>Resolve transitive types (parents and equivalent types of the specific type) with DBpedia ontology 2016-10</p> </li> </ul> <p>CPA:</p> <ul> <li> <p>Add equivalent properties</p> </li> </ul> <p>Statistic of Adapted Tabular data SemTab 2019</p> <pre><code>| | CEA | | | CPA | | | CTA | | | |---------|:--------:|:-------:|:------:|:--------:|:-------:|:------:|:--------:|---------|--------| | | Orginal | Adapted | Change | Orginal | Adapted | Change | Orginal | Adapted | Change | | Round 1 | 8418 | 8406 | -0.14% | 116 | 116 | 0.00% | 120 | 120 | 0.00% | | Round 2 | 463796 | 457567 | -1.34% | 6762 | 6762 | 0.00% | 14780 | 14333 | -3.02% | | Round 3 | 406827 | 406820 | 0.00% | 7575 | 7575 | 0.00% | 5762 | 5673 | -1.54% | | Round 4 | 107352 | 107351 | 0.00% | 2747 | 2747 | 0.00% | 1732 | 1717 | -0.87% |</code></pre> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------------------<br> DBpedia 2016-10 extra resources: (Original dataset http://downloads.dbpedia.org/2016-10/)</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_classes_2016-10.csv</p> <p>Information: DBpedia classes and parents: (We remove the abstract types: Agent, Thing)</p> <p>Total: 759 classes</p> <p>Structure: [class, parents (separate with space)] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "City","Location Place PopulatedPlace Settlement"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_properties_2016-10.csv</p> <p>Information: DBpedia properties and these equivalents</p> <p>Total: 2865 properties</p> <p>Structure: [property, it’s equivalent properties] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "restingDate","deathDate"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_domains_2016-10.csv</p> <p>Information: DBpedia properties and these domain types</p> <p>Total: 2421 properties (have types as their domain)</p> <p>Structure: [property, type (domain)] (without prefix dbo: or http://dbpedia.org/ontology/)</p> <p>Example: "deathDate","Person"</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>File: _dbpedia_entities_2016-10.jsonl.bz2 </p> <p>Information: DBpedia entity dump</p> <p>Format: json list bz2 (bz2 Compressed json list)</p> <p>Source: DBpedia dump 2016-10 core</p> <p>Total: 5,289,577 entities (No disambiguation entities)</p> <p>Structure:</p> <p>An entity: for example “Tokyo”: (datatype: dictionary),</p> <p>{</p> <p>'wd': 'Q1322032', (Wikidata ID, datatype: string)</p> <p>'wp': 'Tokyo', (Wikipedia ID, add prefix <a href="https://en.wikipedia.org/wiki/">https://en.wikipedia.org/wiki/</a> + wp to get the Wikipedia URL, datatype: string)</p> <p>'dp': 'Tokyo', (DBpedia ID, add prefix <a href="http://dbpedia.org/resource/">http://dbpedia.org/resource/</a> + dp to get the DBpedia URL, datatype: string)</p> <p>'label': 'Tokyo', (Entity label, datatype: string)</p> <p>'aliases': ['To-kyo', 'Tôkyô Prefecture', ..], (Other entity names, datatype: list) </p> <p>'aliases_multilingual': ['东京小子', 'طوكيو', ...], (Other entity names in multilingual, datatype: list)</p> <p>'types_specific': 'City', (Entity direct type, datatype: string) </p> <p>'types_transitive': ['Human settlement', 'City', 'PopulatedPlace', 'Location', 'Place', 'Settlement'], (Entity transitive types, datatype: list)</p> <p>'claims_entity': { (entity statements, datatype: dictionary. Keys: properties, Values: list of tail entities)</p> <p>'governingBody': ['Tokyo Metropolitan Government'], </p> <p> 'subdivision': ['Honshu', 'Kantō region'],</p> <p>...</p> <p>},</p> <p>'claims_literal': {</p> <p>'string': { (String literal: datatype: dictionary. Keys: properties, Values: list of values</p> <p>'postalCode': ['JP-13'], </p> <p>'utcOffset': ['+09:00', '+9'],</p> <p>…</p> <p>}</p> <p>'time': { (Time literal: datatype: dictionary. Keys: properties, Values: list of date time</p> <p>'populationAsOf': ['2016-07-31'], </p> <p>...</p> <p>}), </p> <p>'quantity': { (Numerical literal: datatype: dictionary. Keys: properties, Values: list of values</p> <p>populationDesity: [6224.66, 6349.0], </p> <p>'maximumElevation': [2017], </p> <p>...</p> <p>},</p> <p>'pagerank': 2.2167366040153352e-06 (Entity page rank score calculated on DBpedia Graph)</p> <p>}</p> <p>---------------------------------------------------------------------------------------------------------------------------------------</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
Dataset for: A revised and expanded deep radiostratigraphy of the Greenland Ice Sheet from airborne radar sounding surveys between 1993–2019
<p>Version 5 changes the variable names in the NetCDF file so that are more readable by xarray.</p> <p>The NetCDF v4 file (Greenland_radiostratigraphy_v2.nc) contains both the gridded depths of synthetic isochrones (i.e., at ages of interest that were not necessarily directly observed) and the age at regular normalized depths (10–80%) in the ice sheet.</p> <p>For each campaign that was traced, there is an HDF5-compliant .mat MATLAB file (Greenland_radiostratigraphy_v2_X_Y, where X is the year the campaign was flown and Y is the aircraft that was used) that contains the original traced and dated reflections for each traced segment in that campaign. If using Python instead of MATLAB, the mat73 package and its loadmat function can be used to load these files. An example Jupyter notebook is included that illustrates how to access the full contents of .mat file using Python and this package. There is also a zipped (.zip) archive for each campaign that contains GeoPackage (.gpkg) files for each traced segment of that campaign. Because of format restrictions, these GeoPackages only include the traced reflections' depths, with the ages in the metadata. </p> <p> </p>
CLDF dataset derived from Chan's "Numeral Systems of the World's Languages." from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chan, Eugene (eds.) 2019. Numeral Systems of the World's Languages. Accessed: 2019-09-30</p> </blockquote>
Xiaoxing Rma/Qiang forms from Huang et al. 2019
<p>This is a small subset of the published data on Xiaoxing Qiang/Rma language data from Huang et al. 2019. </p><p>Huang, Chenglong, Baofeng Wang, Mingjun Mao & Xi Zhang. 2019. Sichuan Songpan Qiangyu Zhongguo binwei yuyan zhi. Shaoshu minzu yuyan xilie. Beijing: Shangwu yinshuguan. </p><p>These forms were gathered together in order to perform basic measurements of pitch in a study of word-prosody in Rma varieties. Huang et al. 2019 made the recordings and the IPA transcriptions. The text grids and the R code are original. </p><p> </p>
Phlorest phylogeny derived from Sagart et al. 2019 'Dated language phylogenies shed light on the ancestry of Sino-Tibetan'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sagart L, Jacques G, Lai Y, Ryder RJ, Thouzeau V, Greenhill SJ, List J- M. 2019 Dated language phylogenies shed light on the ancestry of Sino-Tibetan. Proceedings of the National Academy of Sciences, 201817972.</p> </blockquote>
Phlorest phylogeny derived from Zhang et al 2019 'Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zhang M, Yan S, Pan W, & Jin L. 2019. Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic. Nature, 569, 112–115.</p> </blockquote>
Customers experiencing a power outage in counties of California, 2019
<p>This dataset includes the long form of the time series from 2019/01/01 to 2019/12/31, showing the number of customers who are experiencing a power outage at the city and county level in the State of California, USA. The time series is divided into 10-min intervals. The original data source is <a href="https://poweroutage.us/">poweroutage.us</a>, a data vendor of power outage data across the United States.</p><p>Power outage long-form time series at the county level are provided in the form of CSV files. Each file contains data for the entire county.</p>
GlobalHighPM₂.₅ (2019)
<p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM<sub>2.5</sub> dataset over land for the year <strong>2019</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.91 and a root-mean-square error (RMSE) of 9.20 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the GlobalHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. <a href="https://weijing-rs.github.io/publications/Wei_et_al-NC-2023.pdf" target="_blank" rel="noopener">First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact</a>. <em>Nature Communications</em>, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3</p> </li> </ul>
Výkazy města Sedlčany 2019-2023
<p>Přiložené výkazy z města Sedlčany 2019-2023 sloužily jako materiály ke zpracování finanční analýzy této obce v rámci bakalářské práce. </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.