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”
Offres d'emploi en Suisse romande dans le domaine de l'information documentaire en 2019
<p>Les données ont été collectées sur Swiss-Lib en 2019. Voir l'onglet documentation du fichier Excel pour plus d'informations.</p>
Images_HistoricAntarcticFishDataset_2019
<p>Images of 225 Trematomus specimens from the Natural History Museum London. see <a href="http://ipt.biodiversity.aq/resource.do?r=historic_antarctic_fish_dataset_2019">http://ipt.biodiversity.aq/resource.do?r=historic_antarctic_fish_dataset_2019</a></p> <p> </p>
Data on frequency-wavenumber spectra of water waves from videos of the river surface: River Sheaf, UK, Feb-Jun 2019
<p>This data set contains sequences of orthorectified images of the free surface of the River Sheaf, Sheffield, United Kingdom (Latitude: 53.373056$^\circ$ Longitude: -1.463913$^\circ$ (WGS 84)), recorded between February and June 2019, as well as their 3D space-time Fourier power spectrum, and gauging survey data of the stage and flow discharge.</p>
FIGURES 8–9 in Two new genera of long clawed Isohypsibioidea Guil, Jørgensen & Kristensen, 2019
FIGURES 8–9. Weglarskobius altai comb. nov.: 8—claws of leg I (paratype); 9—claws of leg IV (paratype). All PCM. Scale bars in µm
FIGURES 2–5 in Two new genera of long clawed Isohypsibioidea Guil, Jørgensen & Kristensen, 2019
FIGURES 2–5. Vladimirobius irregibilis comb. nov.: 2—bucco-pharyngeal apparatus (paratype); 3—claws of leg IV, arrowhead indicates a gibbosity-like projection near the connection of primary and secondary branches of the internal claw (holotype); 4—general view of hind legs (holotype); 5—claws of leg IV with visible accessory point (paratype). All PCM. Scale bars in µm.
ENTSO-E PECD (European Climate Database) from MAF 2019 in CSV and Feather formats
<p>ENTSO-E has published the PECD dataset with the Mid-term Adequacy Forecast (MAF) 2019: https://www.entsoe.eu/outlooks/midterm/#download</p> <p>The downloadable archive contains 3 large Microsoft Excel (~350 MB each). Each file contains the hourly data for all the considered weather years (1982-2016) for all the MAF regions (grouped in tabs).</p> <p>Here we provide the same data but in a more open and user-friendly format.</p> <p><strong>Single files</strong></p> <p>Data saved in CSV and <a href="https://blog.rstudio.com/2016/03/29/feather/">Feather format</a> (using R package feather 0.35). Each file contains the hourly data in a wide tabular format with area, day, month, hour and year (1982-2016). The files are:</p> <ul> <li>PECD-MAF2019-wide-PV.csv (and .feather)</li> <li>PECD-MAF2019-wide-WindOffshore.csv (and .feather)</li> <li>PECD-MAF2019-wide-WindOnshore.csv (and .feather)</li> </ul> <p><strong>Split by year</strong></p> <p>We provide here three archives each one containing a file per year. The files are:</p> <ul> <li>PECD-MAF2019-wide-PV.single_years.tar.bz2</li> <li>PECD-MAF2019-wide-WindOffShore.single_years.tar.bz2</li> <li>PECD-MAF2019-wide-WindOnshore.single_years.tar.bz2</li> </ul> <p> </p> <p> </p>
Decision problem for renewable energy planning in Turkey (Dataset). June 2019
<p>This dataset compromises the decision problem for renewable energy planning in Turkey, adopted from the literature (Kahraman and Kaya 2010; Erdogan and Kaya 2015; Mousavi et al. 2017).</p> <p>The decision problem consists in evaluating and selecting the most appropriate renewable energy alternative for Turkey, and includes:</p> <ul> <li>Five alternatives (<strong>Hydro, Wind, Solar, Biomass, and Geothermal</strong>).</li> <li>Five dimensions fo criteria (<strong>Technological, Technical, Economical, Environmental, and Socio-politic</strong>), with 31 sub-criteria.</li> <li>Weights of criteria.</li> <li>Direction of criteria: "max" if the sub-criterion needs to be maximized ("bigger is better”) and "min" if it needs to be minimized (”smaller is better”).</li> </ul> <p>The dataset is used in the following paper:</p> <p><em>Ezbakhe, F. and Pérez-Foguet, A. (2019) Decision analysis for sustainable development: the case of renewable energy planning under uncertainty. European Journal of Operational Research. </em><a href="https://doi.org/10.1016/j.ejor.2020.02.037">https://doi.org/10.1016/j.ejor.2020.02.037</a></p>
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
History of coronavirus naming during the three zoonotic outbreaks in relation to virus taxonomy and diseases caused by these viruses. According to the current international classification of diseases49, MERS and SARS are classified as 1D64 and 1D65, respectively.
Box 4 in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Box 4 | Classifying SARS-CoV-2 The species demarcation threshold (also known as demarcation limit) in the family Coronaviridae is defined by viruses whose PPD(s) may cross the inter-species demarcation PPD threshold (threshold 'violators'). Due to their minute share of ~10–4 of the to- tal number of all intra- and inter-species PPDs, these violators may not even be visually recognized in a conventional diagonal plot clus- tering viruses on a species basis (panel a of the figure in Box 4). Furthermore, they do not involve any virus of the species Severe acute respiratory syndrome-related coronavirus, as is evident from the analysis of maximal intraspecies PPDs of 2,505 viruses of all 49 coronavirus species (of which 39 are established and 10 are pending or tentative) (panel b of the figure in Box 4) and PDs of 256 viruses of this species (panel c of the figure in Box 4). Thus, the genomic variation of the known viruses of the species Severe acute respiratory syndrome-related coronavirus is smaller compared to that of other comparably well-sampled species—for example, those prototyped by MERS-CoV, human coronavirus OC43 (HCoV-OC43) and in- fectious bronchitis virus (IBV) (panel b of the figure in Box 4)—and this species is well separated from other known coronavirus species in the sequence space. Both of these characteristics facilitate the un- ambiguous assignment of SARS-CoV-2 to this species. Intra-species PDs of SARS-CoV-2 belong to the top 25% of this species and also include the largest PD between SARS-CoV-2 and an African bat virus isolate (SARSr-CoV_BtKY72)56 (panel c of the figure in Box 4), representing two basal lineages within the species Severe acute respiratory syndrome-related coronavirus that constitute very few known viruses (Fig. 2b,c). These relationships stand in contrast to the shallow branching of the most populous lineage of this species, which includes all the human SARS-CoV isolates collected during the 2002–2003 outbreak and the closely related bat viruses of Asian origin identified in the search for the potential zoonotic source of that epidemic57. This clade structure is susceptible to homologous recombination, which is common in this species44,58,59; to formalize clade definition, it must be revisited after the sampling of viruses representing the deep branches has improved sufficiently. The current sampling defines a very small median PD for human SARS-CoVs, which is approximately 15 times smaller than the median PD determined for SARS-CoV-2 (0.16% versus 2.6%; panel c of the figure in Box 4). This small median PD of human SARS-CoVs also dominates the species- wide PD distribution (0.25%; panel c of the figure in Box 4). Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Fig. 2 in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Fig. 2 | Phylogeny of coronaviruses. a, Concatenated multiple sequence alignments (MSAs) of the protein domain combination44 used for phylogenetic and DEmARC analyses of the family Coronaviridae. Shown are the locations of the replicative domains conserved in the ordert Nidovirales in relation to several other ORF1a/b-encoded domains and other major ORFs in the SARS-CoV genome. 5d, 5 domains: nsp5A-3CLpro, two beta-barrel domains of the 3C-like protease; nsp12-NiRAN, nidovirus RdRp-associated nucleotidyltransferase; nsp12-RdRp, RNA-dependent RNA polymerase; nsp13-HEL1 core, superfamily 1 helicase with upstream Zn-binding domain (nsp13-ZBD); nt, nucleotide. b, The maximum-likelihood tree of SARS-CoV was reconstructed by IQ-TREE v.1.6.1 (ref. 45) using 83 sequences with the best fitting evolutionary model. Subsequently, the tree was purged from the most similar sequences and midpoint-rooted. Branch support was estimated using the Shimodaira–Hasegawa (SH)-like approximate likelihood ratio test with 1,000 replicates. GenBank IDs for all viruses except four are shown; SARS-CoV, AY274119.3; SARS-CoV-2, MN908947.3; SARSr-CoV_BtKY72, KY352407.1; SARS-CoV_PC4-227, AY613950.1. c, Shown is an IQ-TREE maximum-likelihood tree of single virus representatives of thirteen species and five representatives of the species Severe acute respiratory syndrome-related coronavirus of the genus Betacoronavirus. The tree is rooted with HCoV-NL63 and HCoV-229E, representing two species of the genus Alphacoronavirus. Purple text highlights zoonotic viruses with varying pathogenicity in humans; orange text highlights common respiratory viruses that circulate in humans. Asterisks indicate two coronavirus species whose demarcations and names are pending approval from the ICTV and, thus, these names are not italicized.
Datasets from Water 11(12)(2019)2579
<p>The ODT file contains the metadata, the excel file contains the input data of the published article “Reductive/ Oxidative Sequential Bioelectrochemical Process for Perchloroethylene Removal”, Water 11(12)(2019) 2579</p>
Resultats eleccions municipals 2015 i 2019 a Catalunya
<p>El conjunt de dades presenta els resultats de les eleccions municipals de 2015 i 2019 en cada municipi de Catalunya. Primerament es presenten els resultats globals en cada municipi (total participació, vots en blanc, abstencions...) i posteriorment els resultats obtinguts per cada una de les candidatures, en vots i en regidors.</p>
The world economic growth 1980 2019
<p>A simple display of IFM' data that spams the real GDP evolution from 1980 to 2019 of 194 countries. </p>
Effectiveness of Open Science Policies at ESEC/FSE 2019
<p>This report summarizes how the ESEC/FSE 2019 open science policies came to life and how they resulted in openness for the conference. In particular, I provide how authors of 277 papers provided data at peer review time, and how authors of 74 published papers provided their papers and data openly. Finally, I discuss on the obtained results, and provide lessons learned for the development of SIGSOFT open science policies.</p> <p>See: <a href="https://ineed.coffee/5836/effectiveness-of-open-science-policies-at-esec-fse-2019/">https://ineed.coffee/5836/effectiveness-of-open-science-policies-at-esec-fse-2019/</a></p>
Data and code for changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China
<p>Data and code for the paper "changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China" published in Science.</p>
Proceedings of 1st International Conference: Modern Information, Measurement and Control Systems: Problems and Perspectives (MIMCS'2019)
<p>International conference</p> <p>July 01-02 2019</p>
Admiralty Bay monitoring 2019
<p>Data are described and supplemented the manuscript submitted to review to Journal of Geophysical Research: Oceans entitled: Variability and interrelation of quality parameters of surface waters in five tidewater glacier coves of King George Island, Antarctica by: Osińska, Bialik and Wójcik.</p>
Dataset: Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 – 2019)
<p>A data set of evaluations of mixed and augmented reality in 2009–2019.</p>
FIGURES 37–40 in A review of the genus Barsaurea Volynkin & Huang, 2019, with a description of new species (Lepidoptera, Erebidae, Arctiinae, Lithosiini)
FIGURES 37–40. Barsaurea ketiga sp. n., female genitalia. Depositories of specimens: 37, 38—MWM/ZSM; 39—SCAU; 40—CAV.
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.