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3,101 results for “Historical”
Farnos' costume from a historical engraving
The 3D model presents a digital look of a historical costume from a Russian lubok print "Farnos i Pigasia" dating to the 18th century. The costume was made for a theatrical performance «Indecent is decent» (Ivanovo State Museum of Local History named after D.G. Burylin, October 30, 2019 – March 1, 2020). The 3D reconstruction was made by using historical block patterns, 2D scans of textile materials and 3d scans of contemporary actors. Anthroscan, Clo3D, SubstancePainter and Unreal Engine 4 software programs were applied. The authors of the 3D model are: Victor Kuzmichev https://www.researchgate.net/profile/Viktor-Kuzmichev Aleksei Moskvin https://sketchfab.com/alekseimoskvin1 https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina https://sketchfab.com/mariia89 https://independent.academia.edu/MariiaMoskvina The authors thank Tatiana Ponomareva and Anastasia Kornilovich for their important contribution to the project. Source: Objaverse 1.0 / Sketchfab
Assembled RADseq Data: "Historical introgression among the American live oaks and the comparative nature of tests for introgression"
<p>Assembled sequence data for Eaton et al. (2015) "Historical introgression among the American live oaks and the comparative nature of tests for introgression". The unassembled sequence data are available on NCBI SRA. </p> <p>Data sets were assembled using pyrad v.2.13 as described in notebook 1 from the study.</p>
FIGURE 1 in Crozetia Davies (Diptera: Simuliidae): redescription of Cr. crozetensis, Cr. seguyi, number of larval instars, phylogenetic relationships and historical biogeography
FIGURE 1. Geographic position of the Crozet Archipelago. (Adapted from Carroll 2001)
Northern Hemisphere historical in-situ Snow Water Equivalent dataset (NorSWE, 1979-2021)
<p><strong>Description</strong> (in English, French follows)</p> <p>The Northern Hemisphere historical in situ snow water equivalent dataset (NorSWE) includes snow water equivalent (SWE, or water equivalent of snow cover by WMO, 2018) observations from manual snow surveys, snow pillows, automated passive gamma radiation sensors (GMON), and from airborne passive gamma radiation surveys for the period 1979-2021 compiled from nine different sources covering North America, Russia, Finland, Norway and Switzerland. Exceptionally, to expand coverage over Europe we also include single point manual SWE observations (type_mes=1) from eleven sites in Switzerland. SWE is the primary variable of interest. Snow depth (SD) is included when available and derived bulk snow density is calculated from SD and SWE. NorSWE is described in detail in Mortimer and Vionnet (in prep). Sites intersecting the Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) with a 25 km buffer or a 2° slope mask derived from the GETASSE30 DEM are assigned a mountain mask flag of 1.</p> <p>Processing and quality control generally follows that described in Vionnet et al. (2021). Quality control involved range thresholding: ranges for SD, SWE and bulk density are 0-3 m (0-8 m where mmask = 1), 0-3000 kg m-2 (0-8000 kg m-2 where mmask =1), and 25-700 kg m-3. Where station elevation was not included in the original station metadata or it was deemed to be erroneous, elevation was taken from the United States Geological Survey’s National Elevation Dataset (Gesh et al., 2022). NorSWE was originally compiled to support evaluation of gridded SWE products over the modern satellite era (1979-2021) and focused on observations from snow course and airborne gamma SWE. In v2, we expanded the dataset to include automated data over North America to support hydrological modelling applications. In v3, we added data from Norway and Switzerland (Marty, 2020).</p> <p>NorSWE is provided as a netCDF (NorSWE-NorEEN_1979-2021_v3.nc, compressed into zip file) and following the conventions of the Canadian Snow Water Equivalent Dataset (CanSWE) described in Vionnet et al. (2021) with the addition of a mountain mask variable. The final dataset includes 10 153 locations spanning the years 1979 to 2021.</p> <p> </p> <p><strong>Description</strong> (Francais)</p> <p>Cet ensemble de données de l’Équivalent en Eau de la couverture Neigeuse (EEN, OMM, 2018) comprend des observations manuelles des lignes de neige, des mesures automatiques des coussins à neige et des capteurs gamma passif (GMON), et des estimations de l’EEN issues de mesures de radiation gamma aéroportée pour la période 1979-2021. Cette base de données compile des données issues de neuf sources couvrant l’Amérique du Nord, la Russie et la Finlande. L’EEN est la quantité d’intérêt principal. L’information sur la hauteur de neige (HN) est incluse lorsqu’elle est disponible et la masse volumique moyenne du manteau neigeux est calculée à partir de l’HN et de l’EEN. NorEEN est décrit en détail dans Mortimer and Vionnet (en préparation). Les sites montagneux sont indiqués par le code mmask (valeur = 1). Le masque de montagne combine le Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) (plus une zone tampon de 25 km) et un masque de topographie complexe (2°) calculée selon le modèle numérique de terrain (MNT) GETASSE30.</p> <p>Le traitement des données et le contrôle de qualité (CQ) suivent la méthodologie proposée par Vionnet et al. (2021). Pour le CQ, les observations en dehors de plages de valeurs prédéterminées ont été exclues : HN 0-3 m (0-8 m mmask = 1), EEN 0-3000 kg m-2 (0-8000 kg m-2 mmask = 1), et masse volumique moyenne du manteau neigeux 25-700 kg m-3. Si l’altitude de la station manquait ou était erronée, l’altitude du site a été extraite du fichier national d’élévation de la Commission Géologique des USA (Gesh et al., 2022). Originalement, la base de données décrite dans ce document a été mise en place pour évaluer de produits d’EEN sur grille d’échelle moyennes à large (4-50 km) couvrant la période moderne de la télédétection satellitaire (1979-2021). Pour cette raison, seules les observations des lignes de neige et les estimations de EEN dérivées de mesures de radiation gamma aéroportées avaient été incluses dans la version 1. Pour la version 2, les données historiques des stations automatiques couvrants l’Amérique du Nord ont été incluses en support des applications hydrologiques. Pour la version 3, les données historiques des stations de stations en Norvège et la Swisse ont été incluses (Marty, 2020).</p> <p>La base de données est distribuée au format NetCDF (NorSWE-NorEEN_1979-2021_v3.nc, comprimée dans une archive zip) selon les conventions de la base de données historiques canadiennes d’Équivalent en Eau de la Neige (CanEEN, Vionnet et al., 2021) et comprenant une variable supplémentaire indiquant les sites montagneux. La base de données inclut des mesures issues de 10,153 sites uniques durant la période de 1979 à 2021.</p> <p><strong>References/Références</strong></p> <p>Beaudette, D., Skovlin, J., Roecker, S., and Brown, A.: soilDB: Soil Database Interface. R package version 2.8.5, [codebase] https://CRAN.R-project.org/package=soilDB, 2024.</p> <p>Carroll, T.R. Airborne Gamma Radiation Snow Survey Program: A user's guide, Version 5.0. National Operational Hydrologic Remote Sensing Center (NOHRSC), Chanhassen, 14, 2001. https://www.nohrsc.noaa.gov/special/tom/gamma50.pdf</p> <p>Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D.: The National Elevation Dataset, Photogramm. Eng. Rem. S., 68, 5–32, 2002.</p> <p>Marty, C.: GCOS SWE data from 11 stations in Switzerland, EnviDat, [data Set], https://www.doi.org/10.16904/15, last updated 2024 (last access: February 2025), 2020.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: A hierarchical inventory of the world’s mountains for global comparative mountain science, Sci. Data, 9, 149, https://doi.org/10.1038/s41597-022-01256-y, 2022.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: GMBA Mountain Inventory v2 [data set], GMBA-EarthEnv., https://doi.org/10.48601/earthenv-t9k2-1407, 2022, accessed June 2023.</p> <p>Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.</p> <p>Vionnet, V., Mortimer C., Brady, M., Arnal, L., and Brown R.: Canadian historical Snow Water Equivalent dataset (CanSWE 1928-2022), Version 5, Zenodo, https://zenodo.org/records/7734616 , 2021, updated 13 March 2023.</p> <p>WMO (Ed.): Guide to instruments and methods of observation: Volume II - Measurement of Cryospheric Variables, 2018th ed., World Meteorological Organization, Geneva, WMO-No. 8, 52 pp., 2018. <strong><br></strong></p>
Growth Rates of Technologies in the Historical Adoption of Technologies (HATCH) Dataset
<p>Table of growth rate measurements for each technology measured from the Historical Adoption of Technologies dataset. The methods to calculate these growth rates is described in the paper "Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures" </p>
Applying genomic approaches to identify historic population declines in European forest bats
<ol> <li>Anthropogenically-driven environmental changes over the past two centuries have led to severe biodiversity loss, most prominently in the form of loss of populations and individuals. Better tools are needed to assess the magnitude of these wildlife population declines. Anecdotal evidence suggests European bat populations have suffered substantial declines in the past few centuries. However, there is little empirical evidence of these declines that can be used to put more recent population changes into historic context and set appropriate targets for species recovery. </li> <li>This study is a collaboration between academics and conservation practitioners to develop molecular approaches capable of providing quantitative evidence of historic population changes and their drivers that can inform the assessment of conservation status and conservation management. We generated a genomic dataset for the Western barbastelle, <em>Barbastella barbastellus</em>, a globally Near Threatened and regionally Vulnerable bat species, including colonies from across the species' British and Iberian ranges. We used a combination of landscape genetics and approximate Bayesian computation model-based inference of demographic history to identify both evidence of population size changes and possible drivers of these changes. </li> <li>We found that levels of genetic diversity and inbreeding were related to broadleaf woodland cover around the colony location. Genetic connectivity was impeded by artificial lights and facilitated by the combination of rivers and broadleaf woodland cover. </li> <li>The demographic history analysis showed that both the northern and southern British barbastelle populations have declined by 99% over the past 330–548 years. These declines may have been triggered by loss of large oak trees and native woodlands due to shipbuilding during the early colonial period.</li> <li> <em>Synthesis and applications</em>. Genomic approaches can be applied to provide a better understanding of the conservation status of threatened species, within historic and contemporary context, and inform their conservation management. This study shows how we can bridge the implementation gap and promote the application of genomics in conservation management through co-designing studies with conservation practitioners and co-developing applied management targets and recommendations.</li> </ol>
Opening the museum's vault: Historical field records preserve reliable ecological data
<p><span>Museum specimens have long served as foundational data sources for ecological, evolutionary, and environmental research. Continued reimagining of museum collections is now also generating new types of data associated with, but beyond physical specimens, a concept known as "extended specimens". Field notes penned by generations of naturalists contain first-hand ecological observations associated with museum collections and comprise a form of extended specimens with the potential to provide novel ecological data spanning broad geographic and temporal scales. Despite their data-yielding potential, however, field notes remain underutilized in research due to their heterogeneous, unstandardized, and qualitative nature. We introduce an approach for transforming descriptive ecological notes into quantitative data suitable for statistical analysis. Tests with simulated and real-world published data show that field notes and our transformation approach retain reliable quantitative ecological information under a range of sample sizes and evolutionary scenarios. Unlocking the wealth of data contained within field records could facilitate investigations into the ecology of clades whose diversity, distribution, or other demographic features present challenges to traditional ecological studies, improve our understanding of long-term environmental and evolutionary change, and enhance predictions of future change.</span></p>
Data for "Modular Bibliographical Profiling of Historic Book Reviews"
<p>This dataset supports the research paper, ""Modular Bibliographical Profiling of Historic Book Reviews." The paper examines different methods of predicting bibliographical details (e.g. author, title, and publisher) of books under review in a corpus of approximately 1,100 historical book reviews. The dataset is comprised of book reviews from ProQuest's American Periodicals Series (APS). This kind of bibliographical profiling is often characterized as a Natural Language Processing (NLP) or Named Entity Recognition (NER) task, but it can be more specifically described as a two-part Named Entity Linking (NEL) task, beginning with a feature extraction stage followed by one of several available matching or classification methods. An attempt has been made to formalize constraints for modular bibliographical profiling (MBP) and shed light on some important choices that are often glossed over or obscured by digital humanities (DH) practitioners. Applying these constraints, the paper evaluates combinations of feature selection (naive bag-of-words [BOW], rule-based feature extraction, and NER using a pre-trained model) with a standardized similarity-based matching strategy (cosine similarity). All tasks are performed on derived text data (term frequency tables), so that data can be shared and all methods can be used on materials available only in non-consumptive formats. These comparisons suggest that naive BOW can perform quite robustly, and that using even a basic pretrained NER model in conjunction with a BOW approach may reduce false positives. </p>
FIG. 4 in Historical perspective: 140 years of Mesozoic radiolarian taxonomy
FIG. 4. — Pie diagram showing distribution of new radiolarian species across the Mesozoic Periods.
FIG. 1 in Historical perspective: 140 years of Mesozoic radiolarian taxonomy
FIG. 1. — New species of Mesozoic radiolarians published between 1867 and 2008.
FIG. 9 in Historical perspective: 140 years of Mesozoic radiolarian taxonomy
FIG. 9. — Pie diagram showing the distribution of nomina dubia.
Lost in translation: A historical-comparative reconstruction of Proto-Khoe-Kwadi based on archival data - Supplementary Material
<p>Supplementary Materials 1-4 for the following article: </p><p>Fehn, Anne-Maria & Jorge Rocha. 2023. Lost in translation: A historical-comparative reconstruction of Proto-Khoe-Kwadi based on archival data. Diachronica. https://doi.org/10.1075/dia.23022.feh.</p>
INTERACT arctic research stations: Historic weather data fetched and plotted from Copernicus Climate Data Store
<p>Plots with historic weather data for arctic research stations covered by International Network for Terrestrial Research and Monitoring in the Arctic (INTERACT). Comparison with WMO climate normal periods 1961-1990 and 1991-2020, averages and stations deviations from the normal periods are calculated and plotted.</p><p>The data source for historic data of temperature and precipitation is: </p><p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7 </p><p>See the linked github repository https://zenodo.org/doi/10.5281/zenodo.10214962 for how data was fetched from Copernicus Climate Data Store, prepared and plotted. The github repository also contains CSVs with source data for the plots.</p>
Ocean biogeochemical reconstructions to estimate historical ocean CO2 uptake
<p>The dataset presented here was employed for the visual representations and analysis in the submitted research article "Ocean Biogeochemical Reconstructions to Estimate Historical Ocean CO_2 Uptake" by Bernardello R. et al., which was a requirement established by the Earth System Journal. The presented data are post-processed results obtained through simulations of the EC-Earth-CC Earth System Model. In adherence to the journal's policy, we have provided access to this dataset.</p><p>In the files where the name does not specify otherwise, the dataset comprises every case that was examined in the paper.</p><p>In the case of Chlorophyll and Mixed Layer Depth, each file comprises a single case, which is denoted by its name.</p>
Original FASTQ files of: Global genetic diversity and historical demography of the Bull Shark
<p><strong>Aim</strong></p> <p>Biogeographic boundaries and genetic structuring have important effects on the inferences and interpretation of effective population size (N<sub>e</sub>) temporal variations, a key genetics parameter. We reconstructed the historical demography and divergence history of a vulnerable coastal high-trophic shark using population genomics and assessed our ability to detect recent bottlenecks events.</p> <p><strong>Location</strong></p> <p>Western and Central Indo-Pacific (IPA), Western Tropical Atlantic (WTA), Eastern Tropical Pacific (EPA)</p> <p><strong>Taxon</strong></p> <p>Carcharhinus leucas (Müller & Henle, 1839)</p> <p><strong>Methods</strong></p> <p>A <a>DArTcap</a><sup>TM</sup> approach was used to sequence 475 samples and assess global genetic structuring. Three demographic models were tested on each population, using an ABC-RF framework coupled with coalescent simulations, to investigate within-cluster structure. Divergence times between clusters were computed, testing multiple scenarios, with <em>fastsimcoal</em>. N<sub>e</sub> temporal variations were reconstructed with STAIRWAYPLOT. Coalescent simulations were performed to determine the detectability of recent bottleneck under the estimated historical trend for datasets of this size.</p> <p><strong>Results</strong></p> <p>Three genetic clusters corresponding to the IPA, WTA and EPA regions were identified, agreeing with previous studies. The IPA presented the highest genetic diversity and was consistently identified as the oldest. No significant within-cluster structuring was detected. N<sub>e </sub>increased globally, with an earlier onset in the IPA, during the last glacial period. Coalescent simulations showed that weak and recent bottlenecks could not be detected with our dataset, while old and/or strong bottlenecks would erase the observed ancestral expansion.</p> <p><strong>Main conclusions</strong></p> <p>This study further confirms the role of marine biogeographic breaks in shaping the genetic history of large mobile marine predator. N<sub>e </sub>Historical increases of N<sub>e</sub> are potentially linked to extended coastal habitat availability. The limited within-cluster population structuring suggests that Ne can be monitored over ocean basins. Due to insufficient amount of available genetic data, it cannot be concluded whether overfishing is impacting Bull Shark genetic diversity, calling for whole genome sequencing.</p>
Historic Assynt - Achnacarnin Circle (2)
Model of the possible circle at Achnacarnin, Assynt, NW Scotland and the area to its SW. A standing stone, built into a wall at the far left of the model, and a large block, situated on an oucrop further right, are on a line which can be extended through the circle's centre. A pair of smaller blocks lie just off this line . The model was created from images taken on February 8th 2016 by a DJI Inspire UAV using DroneDeploy software. An orthomosaic and elevation map are also available. For more information on Historic Assynt, go to http://assyntfireandwater.aocarchaeology.com/about-historic-assynt/ www.historicassynt.co.uk Source: Objaverse 1.0 / Sketchfab
Pre-historic Petroglyph in Mustang, Nepal
These rocks are carved with images of animals, tools and figurines dating back to pre-historic days. Source: Objaverse 1.0 / Sketchfab
The John Rylands Library - Historic Entrance
This is a temporary test of a 360 panoramic Jpeg from a Ricoh Theta camera. Image displayed on a Spherical object from Blender. Thanks to @LOUIS for the spherical template and instructions Source: Objaverse 1.0 / Sketchfab
Historically Tablet
This tablet is from Izmir archaeological museum. Last week I visited there and I wanted to try convert one of the tablets to 3d model for that I reason I did this. Firstly I create displacement map from [This website](https://cpetry.github.io/NormalMap-Online/) after that I created low poly and high poly model with displacement modifier. Finally I created sculpted version in zbrush. In the end it was looking good it is not looking definetely accurate but I think I made a good job in that =) [Child Dionysus with Nymphs - Roman Period](https://imgur.com/gallery/8paZAVP) Source: Objaverse 1.0 / Sketchfab
Historical Cannon
This historical cannon is located in front of Siola Building (Museum of Surabaya) Source: Objaverse 1.0 / Sketchfab
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.