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3,101 results for “historic”
HANZE catalogue of modelled and historical floods in Europe, 1950-2020
<p>The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains:</p> <ul> <li>2521 historical floods with impact data (1870-2020);</li> <li>237 further historical floods with significant impacts, but without precise impact data (1950-2020)</li> <li>Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020).</li> </ul> <p>Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a>. </p> <p>The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders):</p> <p>HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020)</p> <p>HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020)</p> <p>HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020)</p> <p>HANZE_list_of_references.csv - List of all references used in the catalogues</p> <p>HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods</p> <p>Regions_v2010_simplified.zip - Map of subnational regions (v2010)</p> <p>Regions_v2021_simplified.zip - Map of subnational regions (regions v2021)</p> <p> </p> <p>v1.2: corrected NUTS regions v2021 for a few events, which were accidently coded with v2010 regions.</p> <p>v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected.</p>
Historical tone data for Tai languages
<p>Comma-separated values (CSV) of historical tone data for 300+ Tai doculects (languages and dialects). Gives historical categories (Gedney 1972) and tone numerals (Chao 1930). Contact author for source citations. I recommend getting in touch if you'd like to use this dataset There is a good chance I have a newer (bigger, cleaner, better) version of it you could use!</p>
Map. Historic map of the Vākāṭaka realm and neighbouring kingdoms
<p>Map. Historic map of the Vākāṭaka realm and neighbouring kingdoms and principalities of the Viṣṇukuṇḍins, Valkhās, Traikūṭakas, Nalas, Rāṣṭrakūṭas, Cāḷukyas, Sanakānikas, Śarabhapurīyas, Sālaṅkāyaṇas, Pitṛbhaktas and Uccakalpas.</p>
Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)
<p><strong>Gridded historical climate </strong><strong>data over China, spanning 1851 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by 20th century reanalysis (20CRv2c, NOAA/ESRL PSD 20th Century Reanalysis version 2c, ensemble member 37).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled ERA-Interim data set: <a href="https://zenodo.org/record/2600192#.XJj3uKD7RWE">Downscaled ERA-Interim gridded historical climate data over China (1980-2010)</a> doi: 10.5281/zenodo.2600192</em></p>
Historical heat wave temperature (Comune di Napoli)
<p>Hazard level of heat waves depending on different base temperatures for the city of Naples.</p>
Historical contingency shapes adaptive radiation in Antarctic fishes [Data set]
<p>Assembled reference contigs for protein-coding exons and conserved non-coding regions from targeted sequence enrichment of notothenioid fishes and outgroups. </p> <p>Published in : Daane, JM, Dornburg, A, Smits, P, MacGuigan, D, Hawkins, B, Near, TJ, Detrich, HW III*, Harris MP*. (2019). Historical contingency shapes adaptive radiation in Antarctic fishes. <em>Nature Ecology & Evolution.</em></p> <p> </p> <p>-contigs.zip contains the assembled contigs for each species. Each contig represents a targeted region with the addition of flanking DNA sequence</p> <p>-cnes.zip contains the targeted conserved non-coding regions isolated from the larger contigs in contigs.zip</p> <p>-exons.zip contains the targeted protein coding exons isolated from the larger contigs in contigs.zip</p> <p>-protein.zip contains the translated protein coding exons from exons.zip</p>
Historical distribution and current drivers of guppy occurrence in Brazil
<p>This data set was used in the paper "Historical distribution and current drivers of guppy occurrence in Brazil". The file consist of occurrence records of <em>Poecilia reticulata</em> in Brazil over different time periods. In order to evaluate the historical and current distribution of <em>P. reticulata,</em> we searched for occurrence records in two major data sources. We first compiled data from all the studies cited in the recent comprehensive review of studies of Brazilian stream fish assemblages (Dias et al., 2016). We performed this search by using electronic databases and search engines (i.e., Web of Knowledge, Google Scholar, and Scielo) to look for primary studies and published papers from Brazilian journals that provide occurrence records of <em>P. reticulata</em> in Brazil. We also used combinations of the following search terms, in English and Portuguese: “guppy fish”, “non-native”, “nonindigenous aquatic species”, and “<em>Poecilia reticulata”; </em>this second search provided additional published papers mentioning this species in Brazilian territory. Both the papers and supplementary information were screened in order to find the geographical coordinates of the sampling points where this species has been detected. As a third data source, we used all the available records of <em>P. reticulata</em> from the SpeciesLink website (http://splink.cria.org.br/, accessed 2016), which aggregates species occurrence data from major biological collections worldwide, including those from Brazilian institutions. We extracted from this platform all the associated informations (e.g., the location and the associated geographical coordinates; the sampling dates containing year, month, and day; and the names of researchers who composed the sampling teams). From these three sources, we created a database of the occurrence of <em>P. reticulata</em> in Brazil.</p> <p>Some records were excluded because the geographical coordinates and/or sampling dates were not provided in detail, or could not be determined directly or from information in the publication itself (Dias et al., 2016). Overall, incomplete and discarded records comprised only 0.7% (12 out of 1649 records) of our dataset. We further used the sampling dates and associated collector information to remove duplicate records from the database. By this means, multiple records of <em>P. reticulata</em> with the identical geographical coordinates were compared in terms of the sampling day, month, year, and collector(s). If all the information was identical, only one record was included. On the other hand, multiple records of <em>P. reticulata</em> from the same location but with different dates and collectors were retained in the final database. This final database was composed of 1402 records and was used to investigate the occurrence of <em>P. reticulata</em> over time.</p> <p>References</p> <p>Dias, M. S., J. Zuanon, T. B. A. Couto, M. Carvalho, L. N. Carvalho, H. M. V. Espírito-Santo, R. Frederico, R. P. Leitão, A. F. Mortati, T. H. S. Pires, G. Torrente-Vilara, J. do Vale, M. B. dos Anjos, F. P. Mendonça, & P. A. Tedesco, 2016. Trends in studies of Brazilian stream fish assemblages. Natureza & Conservação 14: 106–111.</p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
A Global Database of Historic and Real-time Flood Events based on Social Media
<p>Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on <a href="http://www.globalfloodmonitor.org">www.globalfloodmonitor.org</a>.</p>
EvoBib: A Bibliographic Database and Quote Collection for Historical Linguistics
<p>This databases offers 4564 references dealing with computer-assisted language comparison in a broad sense. In addition, the database offers 8364 distinct quotes collected from 5063 references. The majority of the references in the quote database overlaps with those in the bibliographic database. The quotes are organized by keywords and can browsed with a full text and a keyword search. </p> <p>The data (references and quotes) underlying each new release are provided here, the data can be browsed at <a href="https://evobib.digling.org/">https://evobib.digling.org/</a>.</p> <p>If you use the database, I would appreciate if you could this in your research:</p> <blockquote> <p>List, Johann-Mattis (2024): EvoBib: A bibliographic database and quote collection [Database, Version 1.8.0]. Passau: Chair for Multilingual Computational Linguistics. URL: <a href="https://evobib.digling.org/" rel="nofollow">https://evobib.digling.org/</a></p> </blockquote> <p> </p> <p> </p> <p> </p>
Temperature and Relative Humidity Data in Rooms of Historical Museum (Cabildo) in Salta, Argentina.
<p><strong>Temperature and Relative Humidity Data in Rooms of Historical Museum (Cabildo) in Salta, Argentina.</strong></p> <p>Data was monitored over 15 consecutive days, with readings taken at 15-minute intervals during two periods: the cold season and the warm season. HOBO data loggers (model U12-12) were used, featuring a temperature accuracy of ±0.35°C and a resolution of 0.03°C at 25°C, as well as a humidity accuracy of ±2.5% and a resolution of 0.03%. The sensors were specifically calibrated for the expected temperature range, with a calibration age of less than one year and a calibration error within ±0.5°C. Additionally, a sensor was installed in the building's galleries in all cases to record outdoor temperature and relative humidity data, allowing for a comparative analysis between indoor and outdoor conditions.</p>
Historical forest cover in the Kivu Rift (1958)
<p>If you use this dataset, please refer to our work:<strong> Depicker, A., Jacobs, L., Mboga, N. <em>et al.</em> Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift. <em>Nat Sustain</em> (2021). https://doi.org/10.1038/s41893-021-00757-9</strong></p> <p>This .tif file represents the forest cover in the Kivu Rift in 1958, covering parts of the eastern DRC, western Rwanda, and western Burundi. The forest data was derived from 1 m resolution panchromatic historical aerial photographs (conserved at the Royal Museum for Central Africa) and resampled to a 30 m resolution.</p> <p>The data range from 0 to 1, representing the % of forest cover. Data smaller than 0 can be considered NoData values.</p> <p>This work was funded by the Belgian Science Policy Office (BELSPO) through the PAStECA project (BR/165/A3/PASTECA) entitled `Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa' (http://pasteca.africamuseum.be/).</p>
HTRCatalogs: Dataset for historical catalogs HTR and Segmentation
<p>This release contains 465 xml files, and their corresponding images from a large corpus of 19th, 20th and 21th exhibition catalogs, manuscripts'fair catalogs and directories. The new catalogs added here were created using the HTR and segmentation models accessible in the repository. It includes a csv file describing the xml files and various tools to create a training dataset: differents bash scripts, a python programm to divide the xml files into testing, training and evaluation dataset and several fixed tests. A xsl transformation sheet is also accessible to delete the Entry and EntryEnd zones from the xml files in order to have a SegmOnto-like dataset. The xml files has been corrected since the 4.0 release thanks to the addition of a github action (SegmOntoKraken).</p>
Historical City Maps Semantic Segmentation Dataset
<p>This dataset includes a total of 635 annotated image patches from historical city maps. It is designed for the semantic segmentation of the maps into 5 semantic classes (building blocks, non-built, water, road network, background frame). 330 patches are taken from maps of the city of Paris, while the 305 others are taken from a balanced corpus of city maps from 90 countries all around the world.</p> <p>Please read the detailed informations about data collection methodology, associated metadata and annotation ontology in README.md hereunder :</p>
Supplementary material for 'Station to Station: Linking and Enriching Historical British Railway Data'
<p>Supplementary material for the <a href="https://github.com/Living-with-machines/station-to-station">station-to-station</a> Github repository, containing the underlying code and materials for the paper 'Station to Station: Linking and Enriching Historical British Railway Data', accepted to CHR2021 (Computational Humanities Research).</p> <p>Mariona Coll Ardanuy, Kaspar Beelen, Jon Lawrence, Katherine McDonough, Federico Nanni, Joshua Rhodes, Giorgia Tolfo, and Daniel C.S. Wilson. "Station to Station: Linking and Enriching Historical British Railway Data." In Computational Humanities Research (CHR2021). 2021.</p>
Dispersal of alien species in relation to the historic development of hydropower generation and navigation
<p>Dataset on dispersal of alien species in relation to the historic development of hydropower generation and Navigation along the River Danube.</p>
Layout Ground Truth for Historical Commentaries
<p>This release contains the public domain portion of the dataset used in the paper <em>Page Layout Analysis of Text-heavy Historical Documents: a Comparison of Textual and Visual Approaches</em>.</p>
Historical digital elevation models (DEMs) and orthoimage mosaics for North American Glacier Aerial Photography (NAGAP) program, version 1.0
<p>This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io). </p> <p>The scanned images were preprocessed using the <a href="https://github.com/friedrichknuth/hipp">Historical Image Pre-Processing</a> v0.1 software. Photogrammetric processing was performed with the <a href="https://github.com/friedrichknuth/hsfm">Historical Structure from Motion</a> v0.1 software. </p> <p>All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid. </p> <p>See <a href="https://www.sciencedirect.com/science/article/pii/S0034425722004850">manuscript</a> and <a href="https://ars.els-cdn.com/content/image/1-s2.0-S0034425722004850-mmc1.pdf">supplement</a> for processing details and further dataset description.</p> <p>This release contains data products for two study sites in Washington state, USA:</p> <p><strong>Mount Baker</strong><br> 1970-09-09<br> 1970-09-29<br> 1974-08-10<br> 1977-09-27<br> 1979-10-06<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-09-15<br> 1992-09-18</p> <p><strong>South Cascade</strong><br> 1967-09-21<br> 1970-09-29<br> 1974-08-10<br> 1977-10-03<br> 1979-08-20<br> 1979-10-06<br> 1984-08-14<br> 1986-09-05<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-07-28<br> 1992-09-15<br> 1992-09-18<br> 1992-10-06<br> 1994-09-06<br> 1996-09-10<br> 1997-09-23</p> <p>The 00_thumbnails.jpg provides a quicklook overview at both sites.</p> <p><strong>The DEM and ortho file names are structured as follows:</strong><br> hsfm_NAGAP_[site-name]_[date]_[type].tif</p> <p><strong>For example:</strong><br> hsfm_NAGAP_south-cascade_19670921_ortho.tif</p> <p><strong>Where:</strong><br> [site-name] = Either mount-baker or south-cascade<br> [date] = Image acquisition date in YYYYMMDD format<br> [type] = File type</p> <p><strong>For each DEM and ortho pair, we provide the following:</strong><br> _1m_dem.tif = Digital elevation model posted at 1 m resolution <br> _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place.<br> _metadata.tar.gz = Metadata tarball containing:<br> _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326)<br> _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326)<br> _cameras.csv = Image file names, positions, and orientations</p>
High frequency (tick data) of historical FOREX prices
<p>Price tick data for the most liquid Forex assets (AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, USDJPY). The period covered 09 March 2020 to 07, September 2022. </p>
Historical GIS and Guidebooks: Czechoslovak Tourist Attractions
<p>The following dataset of geolocated travel guide toponyms was compiled in the process of a research project <em>Historical GIS and Guidebooks: A Scalable Reading of Czechoslovak Tourist Attractions </em>(Bechmann Pedersen & Johansson, forthcoming) in which we performed a scalable reading of three travel guides of the former Czechoslovak lands as they were cirka 1959. We used a specialized, open-sourced tool <a href="https://github.com/MatJohaDH/citadel">CITADEL</a> created for this project, which in itself relies on open-sourced data from GeoNames and WikiData.</p> <p> </p> <p> </p> <p>Our primary sources were the following three travel guides:</p> <ul> <li>Čedok/Nagel, 1959, <em>Czechoslovakia</em></li> <li>Čedok, 1928 <em>Guide to the Czechoslovak Republic</em></li> <li>Baedker, 1905 <em>Austria–Hungary including Dalmatia and Bosnia</em></li> </ul> <p>All files are tab separated values (.tsv) files with utf-8 encoding.</p> <ol> <li>Three lists of positions with associated toponyms</li> <li>Two lists of clusters</li> </ol> <p>There are three files of the first type:</p> <p>CITADEL_1905Baedeker_toponyms.tsv<br> CITADEL_1928Cedok_toponyms.tsv<br> CITADEL_1959Nagel_toponyms.tsv</p> Columns for toponym files <table><tbody><tr> <th>Column</th> <th>Explanation</th> </tr> </tbody><tbody> <tr> <td>Toponym_first_used</td> <td>First toponym recorded in the database that is linked to the position</td> </tr> <tr> <td>Toponym_added</td> <td>The toponym as spellled in the travel guide</td> </tr> <tr> <td>Source</td> <td>A shorthand used to identify the travel guide</td> </tr> <tr> <td>PositionID</td> <td>The unique identifier of a position, using the GeoNames or WikiData identifiers where applicable.</td> </tr> <tr> <td>Longitude</td> <td>-</td> </tr> <tr> <td>Latitude</td> <td>-</td> </tr> <tr> <td>Year</td> <td>The year of the travel guide</td> </tr> <tr> <td>Weight</td> <td>The weight reflects the number of entries the toponym has in the index. Nagel1928 does not use this convention, and the weight is instead based on the relative page count.</td> </tr> </tbody> </table> <p> </p> <p>There are two files of the second type:</p> <p>CITADEL_1905,1928,1959_4km_clusters.tsv<br> CITADEL_1928,1959_4km_clusters.tsv</p> Columns for the cluster files <table><tbody><tr> <th>Column</th> <th>Explanation</th> </tr> </tbody><tbody> <tr> <td>years</td> <td>The years represented in the cluster</td> </tr> <tr> <td>Latitude</td> <td>-</td> </tr> <tr> <td>Longitude</td> <td>-</td> </tr> <tr> <td>cnt_points</td> <td> <p>The number of points represented in the cluster</p> </td> </tr> <tr> <td>cnt_toponyms</td> <td>The number of toponyms represented in the cluster</td> </tr> <tr> <td>cnt_toponyms_<source></td> <td>The number of toponyms from <source> represented in the cluster</td> </tr> </tbody> </table>
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.