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3,101 results for “Historical”
FIGURE 20 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 20. Depiction of Anchieta's laboratory in Caconda (source Arquivo Histórico Museu Bocage, artist and date unknown).
FIGURE 35 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 35. Live specimens of Kinixys belliana, among FIGURE 36. A Nile Crocodile killed by locals in the Laúca other bushmeat, confiscated by Cangandala National Parks Dam area and destined for local consumption (Photo by Luis rangers from local poachers in September 2015 (Photo by da Costa). Luis Ceríaco).
FIGURE 16 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 16. Angolan Mopane Woodlands in southern Cunene Province and adjacent Namibia (Photo by Aaron M. Bauer).
which has priority over all other available names (Lanza and Broadley 2014). More recently, Broadley et al. (2018) split Gonionotophis into four genera, with brussauxi being the only Angolan species remaining in the genus. MAP 291. Distribution of Gonionotophis brussauxi in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
which has priority over all other available names (Lanza and Broadley 2014). More recently, Broadley et al. (2018) split Gonionotophis into four genera, with brussauxi being the only Angolan species remaining in the genus. MAP 291. Distribution of Gonionotophis brussauxi in Angola.
et al. 2016a:57); "Rio Curoca in the Pediva Hot Springs area" [-16.28359, 12.56106] (Ceríaco et al. 2016a:32); "Namibe Regional-naNatural Park" [-15.77386, 12.33306] (Ceríaco et al. 2016a:32). Taxonomic and distributional notes: None. MAP 222. Distribution of Trachylepis hoeschi in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
et al. 2016a:57); "Rio Curoca in the Pediva Hot Springs area" [-16.28359, 12.56106] (Ceríaco et al. 2016a:32); "Namibe Regional-naNatural Park" [-15.77386, 12.33306] (Ceríaco et al. 2016a:32). Taxonomic and distributional notes: None. MAP 222. Distribution of Trachylepis hoeschi in Angola.
FIGURE 13 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 13. Zambezian Baikiaea Woodlands behind Kavango River shore in Cuando Cubango Province (Photo by Aaron M. Bauer).
FIGURE 11 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 11. Central Zambezian Miombo Woodlands near Ikelenge, on the Angola-Zambia border (Photo by Philipp Wagner).
FIGURE 8 in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
FIGURE 8. Wetland in Atlantic Equatorial Coastal Forest in Maiombe Forest, northeastern Cabinda Province (Photo by Cândido Carneiro).
Historical NCI Genomic Data Commons data (09-14-2017)
<p>Historical NCI Genomic Data Commons data (v09-14-2017). Clinical ('phenotype') and gene expression (HTSeq FPKM-UQ).</p> <p> </p> <p><strong><a href="https://xenabrowser.net/datapages/?dataset=TCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv&host=https://gdc.xenahubs.net">TCGA-COAD.GDC_phenotype.tsv</a></strong></p> <blockquote> <p>dataset: phenotype - Phenotype</p> <p>cohort<a href="https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Colon%20Cancer%20(COAD)">GDC TCGA Colon Cancer (COAD)</a><br> dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv<br> download<a href="https://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv.gz">https://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv.gz</a>; <a href="https://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv.json">Full metadata</a><br> samples570<br> version11-27-2017<br> hubhttps://gdc.xenahubs.net<br> type of dataphenotype<br> authorGenomic Data Commons<br> raw data<a href="https://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-90">https://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-90</a><br> raw data<a href="https://api.gdc.cancer.gov/data/">https://api.gdc.cancer.gov/data/</a><br> input data formatROWs (samples) x COLUMNs (identifiers) (i.e. clinicalMatrix)<br> 570 samples X 151 identifiers<a href="https://xenabrowser.net/datapages/?host=https%3A%2F%2Fgdc.xenahubs.net&dataset=TCGA-COAD%2FXena_Matrices%2FTCGA-COAD.GDC_phenotype.tsv&allIdentifiers=true">All Identifiers</a><a href="https://xenabrowser.net/datapages/?host=https%3A%2F%2Fgdc.xenahubs.net&dataset=TCGA-COAD%2FXena_Matrices%2FTCGA-COAD.GDC_phenotype.tsv&allSamples=true">All Samples</a></p> </blockquote> <p> </p> <p><strong><a href="https://xenabrowser.net/datapages/?dataset=TCGA-COAD%2FXena_Matrices%2FTCGA-COAD.htseq_fpkm-uq.tsv&host=https%3A%2F%2Fgdc.xenahubs.net">TCGA-COAD.htseq_fpkm-uq.tsv</a></strong></p> <blockquote> <p>dataset: gene expression RNAseq - HTSeq - FPKM-UQ</p> <p>cohort<a href="https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Colon%20Cancer%20(COAD)">GDC TCGA Colon Cancer (COAD)</a><br> dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv<br> download<a href="https://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv.gz">https://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv.gz</a>; <a href="https://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv.json">Full metadata</a><br> samples512<br> version09-14-2017<br> hubhttps://gdc.xenahubs.net<br> type of datagene expression RNAseq<br> unitlog2(fpkm-uq+1)<br> platformIllumina<br> ID/Gene Mapping<a href="https://gdc.xenahubs.net/download/probeMaps/gencode.v22.annotation.gene.probeMap.gz">https://gdc.xenahubs.net/download/probeMaps/gencode.v22.annotation.gene.probeMap.gz</a>; <a href="https://gdc.xenahubs.net/download/probeMaps/gencode.v22.annotation.gene.probeMap.json">Full metadata</a><br> authorGenomic Data Commons<br> raw data<a href="https://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-80">https://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-80</a><br> raw data<a href="https://api.gdc.cancer.gov/data/">https://api.gdc.cancer.gov/data/</a><br> wranglingData from the same sample but from different vials/portions/analytes/aliquotes is averaged; data from different samples is combined into genomicMatrix; all data is then log2(x+1) transformed.<br> input data formatROWs (identifiers) x COLUMNs (samples) (i.e. genomicMatrix)<br> 60,484 identifiers X 512 samples</p> </blockquote>
Historical climate model output of ECHAM5-wiso from 1871-2011 at T106 resolution
<p>Historical climate model simulation of the isotope-enabled ECHAM5-wiso model from the years 1871 to 2011 at T106 (1 degree) resolution. The model code was provided by Martin Werner of AWI. The simulations were designed and run by Nathan Steiger on the Yellowstone supercomputer. The boundary conditions were interpolated HadISST fields. The simulations also included updated fractionation factors (an option within the ECHAM5-wiso Fortran code). All variables here are at monthly resolution in netcdf format. Please <a href="http://www.ldeo.columbia.edu/~nsteiger/contact.html">contact</a> Nathan Steiger if you have any questions about the simulation. In addition to the data citation, please also cite the following reference for where the data were first published: Steiger, N.J., E.J. Steig, S.G. Dee, G.H. Roe, and G.J. Hakim, (2017): <em>Climate reconstruction using data assimilation of water-isotope ratios from ice cores.</em> Journal of Geophysical Research: Atmospheres, doi:10.1002/2016JD026011.</p> <p>Standard variables include: ECHAM5 T106 orography, 2 m temperature, surface pressure, mean sea level pressure, vertically integrated water vapor, total precipitation, evaporation, soil moisture, relative humidity, specific humidity, atmospheric stream function at 200 hPa, geopotential height at 500 hPa, windspeed at 10 m, and u-velocity wind at 200 hPa. Moisture variables are given at the surface (lowest atmospheric level).</p> <p>Isotope variables include: d18O and dD of total precipitation, d18O and dD of evaporation, d18O and dD of snow fall, d18O and dD of seasonal snow cover, d18O and dD of snow on glaciers, d18O and dD of soil moisture, and specific humidity of water isotopes.</p>
ScriptNet: ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI)
<p>This dataset contains the test set for the ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI).</p> <p>The dataset used in this competition consists of 3600 handwritten pages originating from 13th to 20th century. It contains manuscripts from 720 different writers where each writer contributed five pages.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/6/</p> <p>Changes August 1st, 2018: uploaded trainings set in color and binarized</p> <p>if you use the dataset please cite the ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI) paper: https://doi.org/10.1109/ICDAR.2017.225</p> <p> </p>
Oficio de Hipotecas de Girona. A dataset of Spanish notarial deeds (18th Century) for Handwritten Text Recognition and Layout Analysis of historical documents.
<p>This dataset is a subset of 596 documents from the <em>Registre d'Hipoteques de Girona</em> of 1769 collection, guarded by the <a href="http://xac.gencat.cat/ca/llista_arxius_comarcals/girones/"><em>Arxiu Històric de Girona</em></a>. This collection, is composed by hundreds of thousands of notarial deeds from the XVIII-XIX century (1768-1862). Sales, redemption of censuses, inheritance and matrimonial chapters are among the most common documentary typologies in the collection.</p> <p>This dataset is composed of more than 23700 text lines written by a single hand, covering more that 50 different topics (documentary typologies) and a vocabulary of more than 2400 different words. The documents are transcribed using the so-called diplomatic criteria. Additionally, transcripts were tagged with <br> extra enriching/complementary information (e.g. expansion of the abbreviations, hyphen marks, etc.). Along with the transcripts the layout of the document is detected and recorded. Pages have been labeled using six different layout regions.</p> <p>The images along with their respective ground-truth was compiled in PAGE compliant XML format<br> by the <a href="http://www2.udg.edu/tabid/11296/Default.aspx"><em>Centre de Recerca d'Història Rural</em></a> and the HTR group of the <a href="https://www.prhlt.upv.es">Pattern Recognition and Human Language Technologies Research Center</a>.</p>
GT4HistOCR: Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin
<p><strong>GT4HistOCR</strong> contains ground truth for research in Optical Character Recognition (OCR) technology applied to historical printings in German Fraktur and Early Modern Latin.</p> <p>The ground truth comes in pairs of images of single printed lines as they appear in book pages (*.png) and their corresponding diplomatic transcriptions (*.gt.txt), which are UTF-8 strings preserving the character forms (glyphs) as much as possible within the UNICODE standard. These pairs of line images and their transcriptions can be directly used to train recognition models with, e.g., the open source OCR engines <em>OCRopy</em> or <em>Tesseract</em>. A total of 313,173 ground truth lines are provided.</p> <p><strong>Please note that the subcorpora making up this collection used different transcription guidelines, so it is a bad idea to train a recognition model on the total collection! Rather train individual models for each subcorpus.</strong> Fur further information about the subcorpora, please see the README file and the accompanying publication.</p> <p>If these data are useful for you, please cite the accompanying publication:</p> <pre>@article{<a href="http://springmann.net/publications.html#springmann2018gt4hist">springmann2018gt4hist</a>, author = {Uwe Springmann and Christian Reul and Stefanie Dipper and Johannes Baiter}, title = {{Ground Truth for training {OCR} engines on historical documents in German Fraktur and Early Modern Latin}}, journal = {J. Lang. Technol. Comput. Linguistics}, volume = {33}, number = {1}, pages = {97--114}, year = {2018}, url = {https://jlcl.org/content/2-allissues/1-heft1-2018/jlcl_2018-1_5.pdf} }</pre>
Downscaled ERA-Interim gridded historical climate data over China (1980-2010)
<p><strong>Gridded historical climate data over China, spanning 1981 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF).</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. Data created as part of the Met Office Climate Science for Service Partnership China (<a href="https://www.metoffice.gov.uk/research/collaboration/cssp-china">CSSP China</a>), work package 1 output, supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) <a href="https://www.gov.uk/government/publications/newton-fund-building-science-and-innovation-capacity-in-developing-countries/newton-fund-building-science-and-innovation-capacity-in-developing-countries">UK-China Research Innovation Partnership Fund</a>.</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 20CRv2c data set: <a href="https://zenodo.org/record/2558135#.XJj2uaD7RWE">Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)</a> doi: 1</em>0.5281/zenodo.2558135</p>
Historical PM2.5 dataset across North America
<p>This dataset is the estimated long-term (1981-2016) concentrations of ambient fine particulate matter across North America, which combines information from chemical transport modeling, satellite remote sensing, and ground-based monitoring. The estimates included information from updated historical emissions inventories and meteorological data, fine resolution satellite-based estimates of PM<sub>2.5</sub>, and ground-based measurements of PM<sub>2.5</sub>, PM<sub>10</sub> and total suspended particles (TSP) measurements.</p>
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents [HisIR19] Dataset
<p>This dataset contains the training and test set used in the ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents.</p> <p>This competition investigates the performance of large-scale retrieval of historical document images based on<br> writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing<br> a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval.</p> <p>The training data set encompasses images from (i) Letters A, where each writer contributed one or three images; (ii) Manuscripts, where each writer was represented by five consecutive images from a single book.<br> In total, it contains 300 writers contributing one page, 100 writers contributing three pages, and 120 writers contributing five pages resulting in 1200 images of 520 writers.</p> <p>The test data set contains 20 000 images: About 7 500 pages stem from isolated documents (partially anonymous writers, contributing one page each), and about 12 500 pages are from writers that contributed three or five pages.</p> <p> </p> <p>If you use this dataset, please cite:</p> <p>V. Christlein, A. Nicolaou, M. Seuret, D. Stutzmann, A. Maier: "ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents", in 15th International Conference on Document Analysis and Recognition, 2019, Sydney, Australia</p> <p> </p>
Map. Historic map of the Vidarbha region showing find spots of Vākāṭaka inscriptions
<p>Map. Historic map of the Vidarbha region showing find spots of Vākāṭaka inscriptions</p>
CESM input data for running CESM historic with CAM6 and CLM5 (no ocean) in docker container
<p>CESM docker container for HIST_CAM60_CLM50%BGC_CICE%PRES_DOCN%DOM_MOSART_CISM2%NOEVOLVE_SWAV compset and resolution f19_g17 using <a href="https://bioconda.github.io/recipes/cesm/README.html">bioconda cesm docker</a> as a base image.</p>
Figure 14 in The Tabanidae (Diptera) in Brazil: Historical aspects, diversity and distribution
Figure 14. Number of Brazilian species of Tabanidae recorded for terrestrial biomes. The first number represents the total of species and second the number of endemic species.
Figures 6–7 in The Tabanidae (Diptera) in Brazil: Historical aspects, diversity and distribution
Figures 6–7. Accumulation curve of Tabanidae species recorded from Brazil by year: (6) based on the year of the description of each species; (7) based on the first record of each species for the country or continent (Europe) and categorized by nationality of the first authors.
ScienceDex guides
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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.