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FIGURE 1 in Long-term trends of fishery landings and target fish populations in the lower La Plata basin
FIGURE 1 | A. Map of the southern region of La Plata basin showing the location of landing sites where information was collected for historical statistics of the fishery production of Argentina between 1934 and 1986. The landing sites of each river are indicated by colors; Violet: Uruguay River; Blue: Río de la Plata; Light green: Lower Paraná River; Dark green: Middle Paraná River; Red: Higher Paraná; Maroon: Paraguay; Pink: Bermejo River. B. Map of the southern section of Paraná River, showing landing sites and sampling localities of recent commercial fishery monitoring programs and surveys of fish populations, respectively. Filled blue circles indicate landing sites of commercial fishery monitoring of Santa Fe; the violet filled circle indicate the landing site of recente commercial fishery monitoring of Chaco; green empty circles indicate sampling sites of EBIPES survey of fish populations, and red empty circles indicate sampling sites of Corrientes survey of fish populations. The middle Paraná River floodplain and delta is shaded in bright green. Ar: Argentina; Br: Brazil; Py: Paraguay; Uy: Uruguay.
FIGURE 2 in Long-term trends of fishery landings and target fish populations in the lower La Plata basin
FIGURE 2 | Trends in annual landings of 20 freshwater fish taxa in the historical fishery production of Argentina (1934–1986) for all rivers of the La Plata basin and separately for each one of the rivers. Symbols represent the slopes of the linear regressions between the annual landings and year. Red symbols indicate slopes with significant negative values, green symbols indicate slopes with significant positive values and grey symbols indicate slopes not significantly different from zero. Whiskers around each symbol indicate 95% confidence intervals generated by bootstrap method.
Long COVID IRIS Study Olink Proteomics Dataset
<p>Plasma samples collected from Stanford University's “Infection Recovery in SARS-CoV-2” (IRIS) study participants during acute SARS-CoV-2 infection, approximately 3 months post infection, and approximately 12 months post infection were analyzed using the Olink® Target 96 Inflammation panel and Olink® Target 96 Immune Response panel. </p> <p>Proteomic data were obtained from the Olink biomarker platform and presented as "NPX" (i.e. Normalized Protein eXpression) for each protein assay. The dataset features de-identified patient metadata, including participant study number (i.e. IRIS_number), sex, long COVID status (0 = recovered; 1 = Long COVID), and timepoint of the plasma sample (i.e. acute infection sample, approximately 3 months post infection, or approximately 12 months post infection). </p> <p>Notes:</p> <ul> <li>Consistent with the World Health Organization (WHO) definition, long COVID was defined in this study as the continuation or development of symptoms three months after SARS-CoV-2 infection, which were not readily attributable to other etiologies.</li> <li>For acute infection samples, long COVID status (0 = recovered; 1 = Long COVID) refers to whether the patient will have fully recovered or will have long COVID at 3 months post infection. </li> <li>For 3 month samples, long COVID status (0 = recovered; 1 = Long COVID) refers to whether the patient has fully recovered or has long COVID at 3 months post infection.</li> <li>For 12 month samples, long COVID status (0 = recovered; 1 = Long COVID) refers to whether the patient has fully recovered or has ongoing long COVID at 12 months post infection, but these patients all had long COVID at 3 months post infection.</li> </ul>
European Long-term Irrigation Area Datasets version 1.0
<p>These files accompany the manuscript titled "<strong><em>Climate-Driven Interannual Variability in Subnational Irrigation Areas Across Europe</em></strong>" in the journal <strong><em>Communications Earth & Environment</em>.</strong></p> <p>We have developed the <strong>European Long-term Irrigation Area Dataset (ELIAD)</strong>, which offers annual subnational data on total irrigated and irrigable areas for 32 European regions from 1990 to 2020. For most countries, the data is at the NUTS2 level, except for the UK, Germany, and Ireland, where it is at the NUTS1 level.</p> <p>Supplementary A: Contains the supplementary figures and tables referenced in the manuscript.<br>Supplementary B: Provides detailed information on the irrigation reference periods for EU farm structure surveys and agricultural censuses.<br>Supplementary C: Describes the methodology used to generate the ELIAD dataset.<br>Supplementary D: Includes the ELIAD dataset itself.<br>Supplementary E: Details the remote sensing products used for comparison with ELIAD in the manuscript.<br>Supplementary F: Contains the data directly used in the figures and tables presented in the manuscript.</p>
Parking Long Walk
<p>Measurements were conducted in an environment where the user is moving in an open space where there is a clear and direct view between the device and the 5G network antenna. This scenario is essential for evaluating 5G network performance in mobility situations, such as walking down a street, a park, or any other environment where people are moving around while using their mobile devices. The presence of a clear line of sight between the device and the antenna ensures optimal signal conditions, allowing the network performance to be analyzed in the absence of significant obstructions.</p>
(09)-Pereyra2021A-DS0001--DS0003 – Three Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy
<p>(09)-Pereyra2021A-DS0001--DS0003 – Three <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>
Figure 4 in Analysis of sexual dimorphism in the Persian long-tailed desert lizard, Mesalina watsonana (Stoliczka, 1872; Sauria: Lacertidae)
Figure 4. Scatter plots of the head length (HL) against the snout-vent length (SVL) for the Zagros populations (A) Northeastern popUlations (B) Eastern popUlations (C) Male = (▲) and Female = (○). Regression lines are shown whenever the slopes are significantlY different from Zero.
Figure 3 in Analysis of sexual dimorphism in the Persian long-tailed desert lizard, Mesalina watsonana (Stoliczka, 1872; Sauria: Lacertidae)
Figure 3. Ordination of individUal male (▲) and female (○) specimens of the Zagros populations (A) Northeastern populations (B) Eastern popUlations (C) on the first two principal components.
Figure 2 in Analysis of sexual dimorphism in the Persian long-tailed desert lizard, Mesalina watsonana (Stoliczka, 1872; Sauria: Lacertidae)
Figure 2. The mean and standard error (bars) for significantlY different head siZe characters between males and females of Mesalina watsonana, revealed from the analysis of variance (ANOVA). Head length (A), head width (B), head height (C), and snout length (D).
Figure 1 in Analysis of sexual dimorphism in the Persian long-tailed desert lizard, Mesalina watsonana (Stoliczka, 1872; Sauria: Lacertidae)
Figure 1. Geographic distribution of 19 Operational Taxonomic Units (OTU) of Mesalina watsonana used in this study.
HadGEM3-GC31-MM year-long ENSO, QBO, and sea-ice simulations
<p>HadGEM3-GC31-MM year-long (June to May) simulations of two ENSO, two QBO, and two sea-ice states. The ENSO states are neutral ENSO and El Niño, the QBO states are QBO westerly and easterly, and the sea-ice states are present day and future sea-ice (following the<a href="https://gmd.copernicus.org/articles/12/1139/2019/"> PAMIP protocol</a>). There are 150 ensemble members per experiment. HadGEM3 is run in an atmosphere only configuration. The variables making up this dataset are zonal wind (ua), sea level pressure (psl), surface temperature (ts), precipitation (pr), atmospheric temperature (ta), geopotential height (zg), vertical Eliassen-Palm flux (epfz), meridional Eliassen-Palm flux (epfy) and Eliassen-Palm flux divergence (epfd). Zonal means are given for ua, ta, zg, epfz, epfy, and epfd. Both daily and monthly version of zonal wind are provided.</p> <p>This data is uploaded to support the paper "Nonlinear response of the extratropics to tropical climate variability" published in Geophysical Research Letters and in support of the paper "Interdependent extratropical atmospheric responses to Arctic sea-ice loss, QBO and ENSO" submitted to Journal of Climate.</p>
(08)-Strobl2021A-DS0002 – Tribolium castaneum ACOS{ATub'H2B-mRuby} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(08)-Strobl2021A-DS0002 – <em>Tribolium castaneum</em> ACOS{ATub'H2B-mRuby} #1 subline long-term live imaging data of embryonic development acquired with light sheet fluorescence microscopy</p>
(08)-Strobl2021A-DS0001 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(08)-Strobl2021A-DS0001 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0005 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0005 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
Data for Stable isotope composition of long and short term carbon pools can screen drought tolerance in cassava
<p>This repository contains data and scripts to reproduce results that are presented in the article: Van Laere, J., Martinez Maya, M.A., Selvaraj M.G., Becerra Lopez-Lavalle, L.A., Guzman, D., Casas, J.A., Merckx, R., Hood-Nowotny, R., Dercon, G. (2024)<strong> Stable isotope composition of long and short term carbon pools can screen drought tolerance in cassava</strong>. <em>Field Crops Research. </em>https://doi.org/10.1016/j.fcr.2024.109586</p>
(07)-Ratke2020A-DS0004 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0004 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0003 – Drosophila melanogaster w[*]; P{w[+mC]=His2Av-EGFP.C}2/SM6a line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0003 – <em>Drosophila melanogaster</em> w[*]; P{w[+mC]=His2Av-EGFP.C}2/SM6a line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0001 – Drosophila melanogaster y[1] w[67c23]; P{w[+mC]=Ubi-GFP.nls}ID-2; P{Ubi-GFP.nls}ID-3 line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0001 – <em>Drosophila melanogaster</em> y[1] w[67c23]; P{w[+mC]=Ubi-GFP.nls}ID-2; P{Ubi-GFP.nls}ID-3 line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0002 – Drosophila melanogaster w[*]; P{w[+mC]=Tub84B-EGFP.NLS}3 long-term live imaging dataset acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0002 <em>–</em> <em>Drosophila melanogaste</em>r y[1] w[67c23]; P{w[+mC]=Ubi-GFP.nls}ID-2; P{Ubi-GFP.nls}ID-3 (Bloomington <em>Drosophila</em> Stock Center #29724) long-term live imaging dataset acquired with light sheet fluorescence microscopy</p>
(08)-Strobl2021A-DS0003 – Tribolium castaneum Gruul #1 hybrid line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(08)-Strobl2021A-DS0003 – <em>Tribolium castaneum</em> Gruul #1 hybrid line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
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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.