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647 results for “historical data”
Historical IVT data of SPEAR-LE (ens03)
<p>The repository contains the historical scenario simulation (3rd ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
Historical IVT data of SPEAR-LE (ens05)
<p>The repository contains the historical scenario simulation (5th ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
Historical AR data of SPEAR-LE (ens03)
<p>The repository contains the historical scenario simulation (3rd ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
Historical AR data of SPEAR-LE (ens04)
<p>The repository contains the historical scenario simulation (4th ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
Historical AR data of SPEAR-LE (ens02)
<p>The repository contains the historical scenario simulation (2nd ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
Historical AR data of SPEAR-LE (ens05)
<p>The repository contains the historical scenario simulation (5th ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
The simulated monthly runoff data in the historical period and under future climate scenarios of the Yarlung Zangbo River Basin
<p>This data provides the simulated monthly runoff data under the historical period (1979-2014) and future (2049-2084) climate scenarios for four sub-basins of the Yarlung Zangbo River Basin, including Nugexia, Nuxia, Lasha, and Rikaze.<br> This runoff data is simulated based on the GR4J model coupled with a simple degree-day snow module. The GR4J_SNOW performs parameterization and calculates runoff on each grid cell, and the gridded simulated runoff then converges to the outlet of the sub-basin.<br> Time series of the daily records for meteorological forcing data (precipitation, air temperature, vapor pressure, wind speed, downward long-wave radiation, and downward short-wave radiation) from 1979-2014 was provided by China Meteorological Forcing Dataset (CMFD). <br> Future climate scenarios were generated using the combined climate forcing data together with scaling factors obtained from empirical downscaling of 30 available CMIP5 models (28 GCMs for RCP4.5 and 29 GCMs for RCP8.5). The simulated runoff under RCP4.5 and RCP8.4 are the ensemble averages of 28 and 29 simulated runoff results, respectively.</p>
Sensor data for "Influence of X-Ray Radiation on Historical Paper"
<p>This is the raw and processed data used in the paper "Influence of X-Ray Radiation on Historical Paper". It consists of the following:</p> <ol> <li>all images in the CR3 format, taken before, during, and after irradiation of the paper,</li> <li>the images converted to JPEG, as well as crops of the relevant parts stored as nupy arrays</li> </ol>
Figure 1 in Birds of the Pantanal floodplains, Brazil: historical data, diversity, and conservation
Figure 1. Localities with ornithological inventories in Pantanal wetland.The circles correspond to different sample sites, whose geographic coordinates can be found in Table S1. The dark gray spots correspond to burned areas in 2020 fire gray spots correspond to burned areas in 2020 fire according to ALARMES-HISTÓRICO (LASA/ UFRJ, 2021; Pinto et al., 2020).
Data from: Vrba was right: Historical climatic fragmentation, and not current climate, explains mammal biogeography
<p>Climate plays a crucial role in shaping species distribution and evolution over time. Dr. Elisabeth Vrba's Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These results provide empirical support for the role of historical climate fragmentation and physiography in shaping the distribution and evolution of life on Earth.</p>
Data from: Minimally destructive hDNA extraction method for retrospective genetics of pinned historical Lepidoptera specimens
<p>The millions of specimens stored in entomological collections provide a unique opportunity to study historical insect diversity. Current technologies allow to sequence entire genomes of historical specimens and estimate past genetic diversity of present-day endangered species, advancing our understanding of anthropogenic impact on genetic diversity and enabling the implementation of conservation strategies. A limiting challenge is the extraction of historical DNA (hDNA) of adequate quality for sequencing platforms. We tested four hDNA extraction protocols on five body parts of pinned false heath fritillary butterflies, <em>Melitaea diamina</em>, aiming to minimise specimen damage, preserve their scientific value to the collections, and maximise DNA quality and yield for whole-genome re-sequencing. We developed a very effective approach that successfully recovers hDNA appropriate for short-read sequencing from a single leg of pinned specimens using silica-based DNA extraction columns and an extraction buffer that includes SDS, Tris, Proteinase K, EDTA, NaCl, PTB, and DTT. We observed substantial variation in the ratio of nuclear to mitochondrial DNA in extractions from different tissues, indicating that optimal tissue choice depends on project aims and anticipated downstream analyses. We found that sufficient DNA for whole genome re-sequencing can reliably be extracted from a single leg, opening the possibility to monitor changes in genetic diversity maintaining the scientific value of specimens while supporting current and future conservation strategies.</p>
Historical data for feeding, growth and life history in Pieris rapae
<p>These datasets provide the original data for a series of laboratory and field studies, and associated research publications, with Pieris rapae from multiple populations in North America, between 2000 and 2013. These studies focus on thermal sensitivity of feeding, growth and life history traits in these populations. Details of the study site, methodology, and associated publications for each study are described in the associated document file.</p>
Data for "Uncertainties too large to predict tipping times of major Earth system components from historical data"
<p>All data needed to reproduce the figures from the Science Advances manuscript "Uncertainties too large to predict tipping times of major Earth system components from historical data" by Ben-Yami et al.</p> <p>Figs12SamplePathData.zip and Fig3SamplePathData.zip includes the generated synthetic timeseries of the conceptual models in Figs 1-3.</p> <p>The .txt files are the different AMOC observational time series, with the file names structured as dataset_fingerprint.txt. C18 and C18_2GMT are the subpolar gyre SSTs minus one times and twice the global mean SSTs, respectively. The dipole fingerprint is as defined in the text. The first column is the date, and the second column the fingeprint values. The values are monthly means and thus the value for the day of the month in the date is not significant.</p>
Bulk SIA of historical and contemporary squid beaks: raw data and primary analyses
<p>Raw data obtained from stable isotope analysis of <em>δ</em>13C and <em>δ</em>15N in beaks of the squids <em>Gonatus fabricii</em> (Lichtenstein, 1818) and <em>Todarodes sagittatus</em> (Lamarck, 1798) (Cephalopoda: Oegopsida), and primary analyses of these data. Squids sampled in the Baffin Bay, Davis Strait and Nordic Seas (1882-2010) and Iceland, Faroe Islands and Ireland (1844-2023), respectively. Raw data for the open access peer-reviewed paper '<strong>Insights on long-term ecosystem changes from stable isotopes in historical squid beaks</strong>'</p> <p>Paper can be accessed at: https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-024-02274-7</p> <p>Paper's doi: 10.1186/s12862-024-02274-7</p>
Bureaux d'affaires à Montréal - données historiques / Montreal office buildings - historical data
<p>Les données présentées ici ont servi à l’écriture de la monographie : <em>Les bureaux d’affaires à Montréal – historique et état des lieux</em> publiée en juin 2022. La monographie est aussi disponible ici en format PDF.</p> <p>Renseignements concernant l’auteur :</p> <p>Michel Hudon, Saint-Pacôme (Québec), Canada, <a href="mailto:m.hudon51@videotron.ca">m.hudon51@videotron.ca</a></p> <p>Description sommaire :</p> <p>La <strong>monographie</strong> retrace l’historique de la localisation des bureaux d’affaires à Montréal et cherche à en expliquer les moteurs. Afin d’établir un portrait quantitatif autant que qualitatif du phénomène, elle fait un suivi détaillé de l’inventaire des espaces de bureaux, immeuble par immeuble, et estime la progression annuelle de l’espace occupé par les bureaux dans chacun des secteurs géographiques définis.</p> <p>Dans les chapitres 1 à 8, elle se concentre sur le centre-ville, puis dans les chapitres 9 à 11, elle analyse la construction de bureaux et leur décentralisation dans les différentes couronnes de développement commercial qui l’entourent.</p> <p>Chaque chapitre fait d’abord un rappel de l’évolution économique durant la période considérée, puis l’analyse des variables urbanistiques (croissance urbaine et répartition spatiale des diverses fonctions). Vient ensuite l’étude du développement des bureaux proprement dit, secteur par secteur.</p> <p>Cette étude s’inscrit dans les recherches traditionnelles sur la géographie des bureaux d’affaires et vise à une meilleure compréhension de la spécificité montréalaise dans ce domaine tout en fournissant une base statistique à des études plus poussées sur la question.</p> <p>Les <strong>données historiques</strong> compilées touchent toute la région métropolitaine de Montréal sur toute la période historique des débuts de la ville jusqu’au printemps 2022. Elles traitent tous les types d’immeubles accueillant des bureaux mobiles.</p> <p>La banque de données classe les immeubles de bureaux par secteur et sous-secteurs suivant les zones d’analyse habituellement reconnues par le marché et elle les catégorise suivant leurs principales caractéristiques physiques.</p> <p>L’objectif premier de cette banque de données est de préciser pour chaque année et chaque sous-secteur le volume d’espace disponible à l’usage de bureaux d’affaires, qu’il soit ou non occupé. Un tel relevé exhaustif devrait permettre des analyses en relation avec diverses variables économiques.</p> <p>Le fichier principal des immeubles s’accompagne d’un fichier de photos et d’un fichier d’articles de journaux.</p> <p>Date de la collecte de données : de 1984 à 2022</p> <p>Date de production des données : 2022-06-01</p> <p>Date de publication des données : 2022-06-01</p> <p>Localisation géographique de la collecte de données : Montréal (région métropolitaine) détaillée en 14 grands secteurs et 72 sous-secteurs</p> <p>Renseignements concernant les organismes subventionnaires ou commanditaires de cette collecte de données : Les données ont été colligées auprès des différentes firmes immobilières, des propriétaires et gestionnaires d’immeubles, des municipalités, notamment aux Archives de la Ville de Montréal ainsi que dans la presse spécialisée. Elles ont été comparées, vérifiées et normalisées par l’auteur.</p>
Fig. 1 in Unpublished population data of Dendrobates azureus Hoogmoed 1969 obtained in 1968 and 1970, and its historical and current taxonomic status
Fig. 1. Dendrobates "azureus" (= tinctorius).
Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility testing and human health risks - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Hiller E., Faragó T., Kolesár M., Filová L., Mihaljevič M., Jurovič L., Demko R., Mchlica A., Štefánek J., Vítková M. (2024): Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility and human health risks. <em>Chemosphere</em> <strong>362</strong>, 142677. DOI: 10.1016/j.chemosphere.2024.142677</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
DWUG DE Sense: A data set of historical word sense annotations in German
<p>This data collection contains a subset of <a href="https://zenodo.org/record/5543723">DWUG DE</a> word usage data annotated with classical word sense definitions (<em>DWUG DE Sense</em>, see <code>data/*/judgments_senses.csv</code>). From these annotations aggregated and cleaned sense labels were derived (<code>labels/*/labels_senses.csv</code>). From these labels we derived additional binary semantic proximity labels between use pairs ('0' for different sense, '1' for same sense, <code>labels/*/labels_proximity.csv</code>) and change labels reflecting sense changes between the two time periods from which word usages were sampled (<code>stats/*/stats_groupings.csv</code>).</p> <p>The sense labels were derived from the sense annotation by removing instances where not at least 2/3 annotators agree on the label (<code>maj_2</code>/<code>maj_3</code>). Note that the binary proximity labels were <em>derived</em> from the sense annotation, and not directly judged by humans (in contrast to other <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUG data sets</a>). Note that consequently also the change scores EARLIER, LATER and COMPARE were not calculated directly from human judgments, but from the inferred binary proximity labels. Please find the code aggregating and cleaning the data, deriving proximity labels and deriving change labels in the <a href="https://github.com/Garrafao/WUGs">WUG repository</a>.</p> <p>Please find more information on the provided data in the paper referenced below.</p> <p>Version: 1.0.1, 01.11.2024. Correct or remove some normalization and lemmatization errors in the uses. Updated references.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Frank D. Zamora-Reina, Felipe Bravo-Marquez, Nikolay Arefyev. 2024. <a href="https://doi.org/10.1007/s10579-024-09771-7">Sense Through Time: Diachronic Word Sense Annotations for Word Sense Induction and Lexical Semantic Change Detection</a>. Language Resources and Evaluation.</p> <p>Dominik Schlechtweg. 2023. <a href="http://dx.doi.org/10.18419/opus-12833">Human and Computational Measurement of Lexical Semantic Change</a>. PhD thesis. University of Stuttgart.</p>
Data from: Species tree estimation using ddRADseq data from historical specimens confirms the monophyly of highly disjunct species of Chloropyron (Orobanchaceae)
Sequence data exist for only about 1/5 of plant species; therefore we are at risk of losing many branches of the tree of life even before they are placed into a molecular evolutionary context. This necessitates methods for phylogeny estimation of understudied, rare, and threatened taxa, which often forces researchers to utilize historical collections. The restriction site-associated DNA sequencing (RADseq) family of reduced representation sequence generation has provided a flexible and efficient method for the rapid generation of hundreds to tens of thousands of loci, and has recently seen adoption for phylogeny estimation. However, these methods have been primarily utilized with freshly collected or well preserved tissue. Here we sample all taxa of a genus of rare flowering plants, Chloropyron (Orobanchaceae), from herbarium sheets dating up to 25 yr and use double digest restriction site-associated DNA sequencing (ddRADseq) to resolve intraspecific relationships. We find all species in Chloropyron to be monophyletic, with the inland taxon C. maritimum ssp. canescens sister to the rest of the coastal C. maritimum (ssp. maritimum + ssp. palustre), and the two distinct subspecies of C. molle to be each other's closest relative with strong support. In addition, we demonstrate the utility of reduced representation libraries to address phylogenomic problems in a group of rare species and address pitfalls of accurately inferring relationships when the amount of missing data is large, as is often the case when using historical specimens and rare taxa.
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.