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.
594
datasets available to search
ShareScore release 0.9.0
Dataset results
594 results for “demographic data”
Row data for the experiment: "Clinical, psychosocial and demographic factors affect decisions in SLE people".
<p>These datasets correspond to the article titled: “Clinical, psychosocial and demographic factors affect decisions in SLE people”, which can be found at <a href="https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf">https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf</a></p> <p>Analysis scripts, and an explanation of variables, can be found at: <a href="https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE">https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE</a></p> <p>Abstract</p> <p><span>Neurological and psychiatric manifestations affect most lupus individuals and include depression, anxiety, mood disorders, and cognitive dysfunction. Although there is evidence supporting suboptimal decision-making in lupus and its association with glucocorticoids consumption, it is not clear what variables impact such decisions. The aim of this study is to explore how social, clinical, psychological, and demographic factors impact social and temporal decision-making in people with lupus. Through a within-subjects experimental-design, our participants responded to social, clinical, psychological, and demographic electronic questionnaires. Then, they participated in two behavioral economics experiments: the third-party dictator game, and the delay discounting task. Our results show that hostility, and age are essential predictors of social decisions, whereas obsessive-compulsiveness and anxiety better predict temporal decisions. These variables behave as expected, but anxiety shows unexpected results: most anxious people act patiently and prefer delayed but bigger rewards. Finally, clinical factors are critical decision predictors for social and temporal decisions. When people are in remission, they tend to impose higher punishment on those who violate the social norm, and they also tend to prefer immediate rewards. When taking glucocorticoids, they also prefer immediate rewards, and as the dosage of glucocorticoids intake increases, they tend to impose higher punishment on norm violators. Clinicians, researchers, and practitioners must consider the side effects of glucocorticoids on decision-making.</span></p>
Long-term demographic dataset for Cladonia perforata, including fine-scale cover, occupancy, and subpopulation area data, 2011-2024
This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.
Demographic census data for four perennial plants under experimental pollination treatments
These demographic data were collected to measure the effects of manipulated pollination treatments on the population dynamics of four iteroparous perennial plant species: Delphinium nuttallianum (Ranunculaceae), Hydrophyllum fendleri (Boraginaceae [Hydrophyllaceae]), Erigeron speciosus (Asteraceae), and Potentilla pulcherrima (Roseaceae). The pollination treatments consisted of Control corresponding to ambient pollination, Reduced for which 50% of open flowers on each individual were enclosed in mesh to exclude pollinators, Supplemented for which all receptive flowers were hand pollinated with outcross pollen, and Variable for which individuals received the Reduced treatment in ca. 50% of years. The full life cycle was characterized from at least four annual demographic censuses of tagged plants between 2017-2022, germination rates estimated in seed addition plots, and soil seed bank survival estimated from buried seed bags.
Small Mammal Demographic Data at the Sevilleta National Wildlife Refuge, New Mexico
This file contains mark/recapture trapping data collected from 2013-present on permanently established small mammal trapping webs in the creosote-shrubland ecotone on the Sevilleta National Wildlife Refuge in central New Mexico. The two trapping webs are sampled for 3 consecutive nights once per month following the new moon. Each trapping web consists of 145 rebar stakes, 12 spokes originating from a central rebar point, each containing 12 rebars each. The first 4 stakes of the spoke are 5m apart, with the rest being 10m apart for a total of 100m per spoke (200m diameter). Demographic data is collected from each captured animal including age, sex, species, trap location, and reproductive status. Each animal is marked with a unique ear or radio frequency identification (RFID) tag and tissue samples (hair, whiskers, blood and fecal) are collected from each individual once per month. Demographic Findings: From 2013–2023, the program captured an average of 432 unique individuals per year across >10 species from two rodent families, Heteromyidae and Cricetidae. Heteromyids are the most abundant (~77% of captures), with Perognathus flavus (51%), Dipodomys merriami (11%), D. ordii (11%), and D. spectabilis (4%) dominating the community. Recapture rates for these species are >75%, generating longitudinal data on survival.
Demographic data from long-term symbiont removal experiments with grasses and Epichloë fungal endophytes
This project was designed to understand the demographic effects of vertically transmitted fungal endophytes (Epichloë spp.) on their grass hosts. The experiment includes seven host-symbiont taxonomic pairs: Agrostis perennans - E. amarillans, Elymus villosus - E. elymi, Elymus virginicus - E. elymi or EviTG-1, Festuca subverticillata - E. starrii, Poa alsodes - E. alsodes, Poa sylvestris - E. PsyTG-1, Schedonorus arundinaceus - E. coenophiala. Experimental plots were established at the Indiana University Lilly-Dickey Woods Research and Teaching Preserve in south-central Indiana, USA in 2007. For each species, 5-10 plots were planted with naturally symbiotic (S+) hosts, and 5-10 plots were plated with hosts that were disinfected of fungal endophytes by heat treatment (S-). Over 15 years (2007-2022) we collected demographic data on the survival, growth, reproduction, and recruitment of all plants in all plots. Beginning in 2018 we also collected data on the locations of all plants in every plot.
Hybrid gridded demographic data for the world, 1950-2020
<p>This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.</p> <p>This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.</p> <p>Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)</p> <p><strong>Method - demographic fractions</strong></p> <p>Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.</p> <p>To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:</p> <p><span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)</span></p> <p> </p> <p>Where:</p> <p>- <span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}}\)</span> is the ratio of change in demographic for a given age and and country from the UN WPP dataset.</p> <p>- <span class="math-tex">\(f_{year,\ country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country, and year.</p> <p>- <span class="math-tex">\(f_{2010,country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.</p> <p> </p> <p>The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.</p> <p>For each subset of cells corresponding to a given country <em>c</em>, the fraction of population in a given age band is calculated as:</p> <p><span class="math-tex">\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)</span></p> <p>Where:</p> <p>- <span class="math-tex">\(f_{year,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for given year, for the grid cell <em>c</em>.</p> <p>- <span class="math-tex">\(f_{2010,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for 2010, for the grid cell <em>c</em>.</p> <p>The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.</p> <p> </p> <p><strong>Method - demographic totals</strong></p> <p>Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.</p> <p>The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.</p> <p>Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.</p> <p> </p>
Potentilla demographic and environmental data for Rocky Mountains of Colorado (Niwot LTER & RMBL), 2018 - 2020.
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and the Rocky Mountain Biological Laboratory (RMBL). This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.
Skin-blubber biopsy samples and associated demographic data collected from cetaceans encountered along the Western Antarctic Peninsula (WAP), 2010 – 2024
Baleen whale populations in the Southern Ocean are recovering after intense commercial whaling in the 20th century. Along the Western Antarctic Peninsula (WAP), this recovery is occurring in one of the planet's most rapidly changing marine ecosystems. Understanding how climate-driven changes influence the population dynamics of whales in this region is critical for understanding what conservation and management actions must be prioritized to maintain the structure and function of this marine ecosystem. To begin understanding the dynamics of whale recovery under continued environmental change, we need to study these whales' demography and population dynamics. As part of our annual sampling surveys for cetaceans along the WAP through the PAL LTER program, we actively collect remote non-lethal skin-blubber biopsy samples and have developed one of the most extensive tissue archives in the Southern Ocean. With these samples, we conduct a series of demographic and physiological measurements. Using the skin portion of the biopsy sample, we isolate nuclear and mitochondrial DNA (mtDNA) to develop a DNA profile for each sample, including genetic sex, a microsatellite genotype, and a mtDNA haplotype. These profiles are used to compare sex ratios of the population, determine individual recaptures through genotype analysis, and better understand population mixing. Using the blubber portion of the biopsy sample, we isolate endocrine markers (e.g., progesterone and cortisol) to monitor population pregnancy rates and stress levels. This data represents some of the first non-lethal quantitative observations of the demography and population dynamics of recovering whale populations in the Antarctic and provides a critical reference point for future work as the Antarctic climate continues to change and populations continue to recover from whaling.
Data from "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region"
<p>Table 1 and Table 2 from Tazzari et al., 2021, "The first ALMA survey of protoplanetary discs at 3 mm: demographics of grain growth in the Lupus region", Monthly Notices of the Royal Astronomical Society, arXiv:2010.02248</p> <p>Both tables are available in IPAC format, which is in human- and machine-readable:</p> <pre><code class="language-python">from astropy.io import ascii tb = ascii.read('Table1.txt', format='ipac')</code></pre> <p>Table comments (stored at the beginning of the ASCII file as lines starting with "/") can be read as:</p> <pre><code class="language-python">tb.meta['comments'] </code></pre> <p> </p>
Data for "Demographic inequalities in digital spaces in China: The case of Weibo"
<p>These data underlie the results and figures used in the article "Demographic inequalities in digital spaces in China: The case of Weibo" (https://doi.org/10.36190/2023.01). This research was presented at the ICWSM workshop "Data for the wellbeing of the most vulnerable" on June 5, 2023.</p><p>The corresponding workflow can be found in the linked repository.</p><p>`README.md` provides more details.</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Genotype Data for "A genomic snapshot of demographic and cultural dynamism in Upper Mesopotamia during the Neolithic Transition"
<p>This repository contains genotype data from the article "<a href="https://www.science.org/doi/10.1126/sciadv.abo3609">A genomic snapshot of demographic and cultural dynamism in Upper Mesopotamia during the Neolithic Transition</a>". Dataset preparation protocols are described in the article. Here, we only include the genotype files of 13 newly published Çayönü samples in eigenstrat format.</p> <p>The 1KGYoruba suffix refers to the dataset prepared using variable positions in the Yoruba population (see the paper for details). Others are well known Human Origins and 1240K panels. </p> <p>Code and processed data related to the paper has been deposited <a href="http://doi.org/10.5281/zenodo.7086441">here</a>.</p> <p>* The first version has missing individuals. </p>
Mammalian herbivores restrict the altitudinal range limits of three alpine grass species (transplant and herbivore exclusion experiment and demographic data from natural populations), West Elk Mountains, Colorado, USA 2015-2018
Though rarely experimentally tested, biotic interactions have long been hypothesized to limit low-elevation range boundaries of species. We tested the effects of herbivory on three alpine-restricted plant species by transplanting plants below (novel), at the edge (limit), or in the center (core) of their current elevational range and factorially fencing-out above- and belowground mammals in the West Elk Mountains, Colorado, USA from 2015-2018. Herbivore damage was greater in range limit and novel habitats than in range cores. Exclosures increased plant biomass and reproduction more in novel habitats than in range cores, suggesting demographic costs of novel interactions with herbivores. We then used demographic models to project population growth rates, which increased 5-20% more under herbivore exclosure at range limit and novel sites than in core habitats. Our results identify mammalian herbivores as key drivers of the low-elevation range limits of alpine plants and indicate that upward encroachment of herbivores could trigger local extinctions by depressing plant population growth.
Demographic, economic, geospatial data for municipalities of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) in 2010-2016
<p>The database contains demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016.</p> <p>The sources of data are the municipal-level statistics of Rosstat, Google Maps data and calculated indicators. The statistical data were arranged by the year, the data on municipalities for which there were administrative and territorial transformations for the period under study were excluded (in some cases, the data were provided in accordance with the administrative-territorial demarcation as of 2016).</p> <p>Municipalities' websites were used to fill the lack of population information in individual municipalities for some years.</p> <p>Calculated variables were made to estimate a number of indicators per capita, to introduce additional demographic indicators (e.g. migration inflow rate), to bring price economic indicators to base year prices (2010). For example, indicators of income of the local budget, volumes of investments in fixed assets (excluding budgetary funds), level of wages are modified to a comparable form (to 2010 prices).</p> <p>The distances on roads in different units of measurement from the geographical center of municipalities to the center of the capital of the region are calculated using the Google Maps database.</p> <p>Data mapping was performed using ArcGIS software.</p> <p>The data set consists of</p> <p>1) Municipalities_CFD_Russia_2010_2016_ENG.xlsx - The database of demographic, economic, geospatial data for 452 municipalities of the 16 administrative units of the Central Federal District in Russia (excluding the city of Moscow and the Moscow oblast) for 2010-2016,</p> <p>2) MUNICIPALITIES_CFD_RUSSIA_SHAPE.rar - The shape-files for maps construction,</p> <p>3) Fig.1. Municipalities ENG.jpg - The map of studied administrative units and municipalities of the Central Federal District in Russia .</p>
Data from: Exploring rainforest diversification using demographic model testing in the African foam-nest treefrog (Chiromantis rufescens)
Aim: Species with wide distributions spanning the African Guinean and Congolian rainforests are often composed of genetically distinct populations or cryptic species with geographic distributions that mirror the locations of the remaining forest habitats. We used phylogeographic inference and demographic model testing to evaluate diversification models in a widespread rainforest species, the African Foam-nest Treefrog (Chiromantis rufescens). Location: Guinean and Congolian rainforests, West and Central Africa. Taxon: Chiromantis rufescens. Methods: We collected mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data for 130 samples of Chiromantis rufescens. After estimating population structure and inferring species trees using coalescent methods, we tested demographic models to evaluate alternative population divergence histories that varied with respect to gene flow, population size change, and periods of isolation and secondary contact. Species distribution models were used to identify regions of climatic stability that could have served as forest refugia since the Last Interglacial. Results: Population structure within Chiromantis rufescens resembles the major biogeographic regions of the Guinean and Congolian forests. Coalescent-based phylogenetic analyses provide strong support for an early divergence between the western Upper Guinean forest and the remaining populations. Demographic inferences support diversification models with gene flow and population size changes even in cases where contemporary populations are currently allopatric, which provides support for forest refugia and barrier models. Species distribution models suggest that forest refugia were available for each of the populations throughout the Pleistocene. Main conclusions: Considering historical demography is essential for understanding population diversification, especially in complex landscapes such as those found in the Guineo-Congolian forest. Population demographic inferences help connect patterns of genetic variation to diversification model predictions. The diversification history of Chiromantis rufescens was shaped by a variety of processes, including vicariance from river barriers, forest fragmentation, and adaptive evolution along environmental gradients.
Data from: Evolutionary and demographic history of the Californian scrub white oak species complex: an integrative approach
<p>Understanding the factors promoting species formation is a major task in evolutionary research. Here, we employ an integrative approach to study the evolutionary history of the Californian scrub white oak species complex (genus <em>Quercus</em>). To infer the relative importance of geographical isolation and ecological divergence in driving the speciation process, we (i) analyzed inter- and intra-specific patterns of genetic differentiation and employed an approximate Bayesian computation (ABC) framework to evaluate different plausible scenarios of species divergence. In a second step, we (ii) linked the inferred divergence pathways with current and past species distribution models, and (iii) tested for niche differentiation and phylogenetic niche conservatism across taxa. ABC analyses showed that the most plausible scenario is the one considering the divergence of two main lineages followed by a more recent pulse of speciation. Genotypic data in conjunction with species distribution models and niche differentiation analyses support that different factors (geography vs. environment) and modes of speciation (parapatry, allopatry and maybe sympatry) have played a role in the divergence process within this complex. We found no significant relationship between genetic differentiation and niche overlap, which probably reflects niche lability and/or that multiple factors have contributed to speciation. Our study shows that different mechanisms can drive divergence even among closely related taxa representing early stages of species formation and exemplifies the importance of adopting integrative approaches to get a better understanding of the speciation process.</p>
Data for: Environmental complexity mitigates the demographic impact of sexual selection
<p>Sexual selection and the evolution of costly mating strategies can negatively impact population demography and adaptive potential. While laboratory studies have documented outcomes stemming from these processes, theory suggests that the demographic impact of sexual selection is contingent on the environment and therefore may have been overestimated in simple laboratory settings. Here we find support for this claim. We exposed copies of beetle lines, previously evolved with or without sexual selection, to a 10-generation heatwave while maintaining half of them in a simple environment and the other half in a complex environment. Populations with an evolutionary history of sexual selection maintained larger sizes and more stable growth rates in complex (relative to simple) environments, an effect not seen in populations that evolved without sexual selection. These results have implications for evolutionary forecasting and suggest that the demographic impact of sexual selection in natural populations might be lower than predicted.</p>
Data from: Ability of seedlings to survive heat and drought portends future demographic challenges for five southwestern US conifers
<p>Climate change and disturbance are altering forests and the rates and locations of tree regeneration. We examined seedling survival of five southwestern United States (US) conifer species found in warmer and drier woodlands (<em>Pinus edulis</em>, <em>P. ponderosa</em>) and cooler and wetter subalpine forests (<em>Pseudotsuga menziesii</em>, <em>Abies concolor</em>, and <em>Picea engelmanii</em>) under hot and dry conditions in incubators. We constructed models that explained 53% to 76% of the species-specific survival variability, then applied these to recent climate (1980-2019) and projected climate (1980-2099) for the southwestern US. We found that lower elevations within species' range would have low survival under projected climate and that range contraction would be greatest for species that currently occupy warm-dry conditions. These results demonstrate that empirically derived physiological limitations can be used to identify where species composition or vegetation type change are likely to occur in the southwest US.</p>
Hybrid gridded demographic data for the world, 1950-2020 0.25˚ resolution
<p>This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.25 degree grid resolution.</p> <p>This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020). More detailed can be found on the page of <a href="https://doi.org/10.5281/zenodo.3768003">the original version</a> (https://doi.org/10.5281/zenodo.3768003).</p> <p>This release increases the resolution to 0.25˚ and is explicitly designed to match with the grid definition of the ERA5 climate reanalysis dataset. For pre-2000 population data, the ISIMIP Histsoc data was upscaled from it's native 0.5˚ resolution.</p>
Metabolic and demographic data from the LTEE
<p>These data are the metabolic data and demographic data underlying the paper, "<strong>Long-term experimental evolution decouples size and production costs in <em>Escherichia coli</em></strong><span>". </span></p>
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.