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.
1,600
datasets available to search
ShareScore release 0.9.0
Dataset results
1,600 results for “input”
Decreased ultraviolet radiation and decomposer biodiversity inhibit litter decomposition under continuous nitrogen inputs
<p>Atmospheric nitrogen (N) deposition has altered biogeochemical cycles and ecosystem functioning. As a key process involved in carbon and nutrient cycles in terrestrial ecosystems, litter decomposition is sensitive to external N inputs. However, it remains unclear how the interactions of ultraviolet (UV) radiation, soil biodiversity (bacteria, fungi and invertebrates) and conventional drivers (e.g., litter chemistry and microbial activities) regulate the responses of litter decomposition to continuous N inputs.</p> <p>Based on an N-addition experimental platform, we conducted a two-year litter decomposition experiment to examine the relative importance of N-induced changes in biotic and abiotic factors in mediating changes in the decomposition rates of four litter types (three representative species and their mixture) along an experimental N gradient in a Tibetan alpine steppe.</p> <p>Our results showed that litter decomposition rates exhibited a consistent decrease in response to N enrichment among all species and their mixture. The slowed decomposition rates with increasing N addition were associated with N-induced reductions in UV radiation and soil bacterial diversity. An additional UV radiation manipulative experiment further confirmed that photodegradation had strong effects on plant litter decomposition at our study site.</p> <p>These results demonstrated that N-induced declines in UV radiation and soil bacterial diversity inhibited litter decomposition, challenging the traditional view that changes in litter chemistry and microbial activities determine the responses of litter decomposition to external N inputs.</p>
Input data for the Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>This repository contains large input data files for generating the RFF-SPs, and small accompanying files with data necessary for interpreting the larger files.</p> <p>--------------------------------------------------------------------------------<br> /MSW_GDP/<br> --------------------------------------------------------------------------------</p> <p>-MSW.mat is a MATLAB data file based on Müller, Stock, and Watson (forthcoming) containing two series:<br> 1) Year-by-sample draws "F-paths" representing frontier values of ln(GDP/capita) for the OECD, and <br> 2) Year-by-country-by-sample deviations from those F-paths, denoted as the U-path.</p> <p>A country's ln(GDP/capita) is equal to the sum of the sample's F-path and the country-sample's U-path. There are 418 years representing 1900-2317, 113 countries with names in country_names.txt, and 2000 sample trajectories. GDP/capita is measured in 2011$.</p> <p>-country_names.txt contain iso codes for the 113 countries in the order in which they appear in MSW.mat's U-path object. OECD countries are indicated with an "x" for reference.</p> <p>This information was transmitted via email from Ulrich Mueller (Princeton) to Kevin Rennert (RFF) on January 28, 2020, with a link to the file on Mueller's website at http://www.princeton.edu/~umueller/matlabJan2020.mat.</p> <p>--------------------------------------------------------------------------------<br> /Raftery population/<br> --------------------------------------------------------------------------------</p> <p>-pop_trajectories_probmig.csv contains country-by-year-by-sample (here, a sample is labelled a "Trajectory") population estimates based on Raftery and Ševčíková (2021). Population is in units of thousands of people, with 1000 samples. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on August 11, 2021.</p> <p>-death_rates.csv contains country-by-year-by-sample death rates in units of annual deaths per one thousand people, with 1000 samples corresponding to the sample order of samples in pop_trajectories_probmig.csv. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on October 7, 2021.</p> <p>-iso_key.csv contains a key mapping ISO alpha-3 country codes ("ISO3") to ISO numeric-3 country codes (denoted "NumericCode" in this file and "LocID" in pop_trajectories_probmig.csv). This data was acquired from a search on https://www.iso.org/obp/ui#search in 2021. The "country codes" box was checked and no keywords were used. The list was trimmed to the 184 countries included in the current version of the GIVE model.</p> <p>--------------------------------------------------------------------------------<br> Information<br> --------------------------------------------------------------------------------</p> <p>The probabilistic economic and emissions projections are from Müller, Stock, and Watson (forthcoming). </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>--------------------------------------------------------------------------------<br> References <br> --------------------------------------------------------------------------------</p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: https://www.sciencedirect.com/science/article/pii/S0169207021001394 </p>
SWMF input files for the 23 July 2012 solar superstorm event
<p>The dataset includes the SWMF input files for the 23 July 2012 solar superstorm event. The solar wind conditions and the geomagnetic observatories where we calculated the geomagnetic perturbations and other files needed to run the model are included.</p>
A validation sample (input, label, and prediction) for the network: "SeisLoca_Unet"
<p>This file is a validation sample including the input, label, and prediction. The data of variable "pred" can be obtained by running the prediction file: "prediction.py". Please remember to modified the data directory.</p>
West Atlantic simulation input files and run
<p>Dataset to reproduce runs in Irrmann, G. et al.: <em>Improving Ocean Modelling Software NEMO 4.0 benchmarking and communication efficiency</em>, Geosci. Model Dev (https://doi.org/10.5194/gmd-15-1-2022).</p> <p><a href="https://zenodo.org/api/files/fdfef221-a04f-49bc-8bdb-5a7e84c0e989/IAS24_light.tar?versionId=b6e40319-d3c9-4b56-a2eb-f406f11b4ed2">IAS24_light.tar</a> contains input files needed to reproduce runs of the West Atlantic simulation.</p> <p><a href="https://zenodo.org/api/files/fdfef221-a04f-49bc-8bdb-5a7e84c0e989/19940101_19940103.tar?versionId=ea8757ef-65ce-4fd5-9f90-86122bf22117">19940101_19940103.tar</a> is an example of a run of the West Atlantic simulation, the <em>timing.output </em>file contains data to reproduce a plot of computing time per time step.</p>
DEMSI v0.0.1 input data
<p>DEMSI v0.0.1 test case data set</p>
Arlequin input file for phylogenetic analysis [ARP file]
<p>Input file for Arlequin program to conduct phylogenetic analysis on the correlates of stress in the languages surveyed in the author's dissertation, "Archival Phonetics & Prosodic Typology in Sixteen Australian Languages," 2022.</p>
United States Environmentally-Extended Input-Output (USEEIO) Modeling Framework
<p>This is a snapshot of the <a href="https://github.com/USEPA/useeio">USEEIO modeling framework</a> repository that is compilation of a README, versioning scheme, and a python script supplemented with a to convert a USEEIO model generated from <a href="https://github.com/USEPA/useeior">useeior </a>in the API format into openLCA schema JSON-LD files. This is not source code for generating the USEEIO v2.0 model.</p>
input files for " Martini bead form factors for nucleic-acids and their application in the refinement of protein/nucleic-acid complexes against SAXS data".
<p>input files for " Martini bead form factors for nucleic-acids and their application in the refinement of protein/nucleic-acid complexes against SAXS data", cf plumed-nest</p>
Macromolecule distributions input file for the OCEANFILMS parameterization
<p>This dataset provides the inputs required for the OCEANFILMS parameterization of sea spray organic matter emissions, as it has been implemented in the Energy Exascale Earth System Model version 1 (E3SMv1; Burrows et al., 2014; 2022). The file was generated as described in Burrows et al. (2014). Briefly, we used the Parallel Ocean Program (POP; Maltrud et al., 1998) to simulate the general circulation and its biogeochemical elemental cycling (BEC) routines (Moore et al., 2004) to simulate marine biogeochemistry. Both are components of the Community Earth System Model (CESM; http://www2.cesm.ucar.edu, Hurrell et al., 2013). Calculations were performed using the CESM 1.0 beta release 11.</p> <p>The macromolecular distributions contained in these files were calculated based on various fields from these CESM simulations.</p> <p>1. <strong>Lipids</strong> were calculated on the basis of the POP-simulated phytoplankton and zooplankton concentrations (Burrows et al., 2014; Eq. 11).</p> <p>2. <strong>Polysaccharides</strong> were assumed to have a concentration identical to POP-simulated semilabile dissolved organic carbon (SDOC; Burrows et al., 2014; Eq. 12).</p> <p>3. <strong>Proteins</strong> were assumed to have a concentration equal to one-third of POP-simulated SDOC (Burrows et al., 2014; Eq. 13).</p> <p>4. A <strong>processed</strong> DOC group was estimated as a residual term from these surface pools (Burrows et al., 2014; Eq. 14, and surrounding text). This field has negligible impacts on the results of the OCEANFILMS parameterization, but has been retained here for clarity.</p> <p>5. A <strong>humic</strong> compound group was estimated by assuming a fixed concentration of humic substances below the mixed layer, and estimating the effects of mixing this humic matter into the ocean mixed layer (Burrows et al., 2014; Eq. 17). As with the processed DOC group, this field's impact on the resulting predictions of the OCEANFILMS parameterization is negligible, but it has been retained here for clarity.</p> <p>All fields have the units µM C.</p> <p>The dataset is also available form the E3SM inputdata repository (last access 17 March 2022): https://web.lcrc.anl.gov/public/e3sm/inputdata/atm/cam/chem/trop_mam/marine_BGC/monthly_macromolecules_0.1deg_bilinear_latlon_year01_merge_date.nc </p>
The input files and processed output data for the OSSEs using particle filter for the forecast of cloud and precipitation
<p>DART_input.tar.gz contains the input files for the DART system for the control run, Exp-1 ~ Exp-5.</p> <p>WRF&WRS_namelist.tar.gz contains the input namelist files for the WPS and WRF model for the nature run, the control run, and Exp-1 ~ Exp-5.</p> <p>cloud_data.tar.gz contains the procssed output data for the WRF/DART system corresponding to the nature run, the control run, and Exp-1 ~ Exp-5. Untarring the file will generate several subdirectories named after the UTC time. For example, 202008200200 denotes the results for at 02:00 UTC, 20 August, 2020. Because the data for all times are quite large (~10G), we only uploaded the data at 02:00 UTC, 20 August, 2020, 10:30 UTC, 20 August, 2020, 19:00 UTC, 20 August, 2020, 03:30 UTC, 21 August, 2020, and 12:00 UTC, 21 August, 2020. Each subdirectory contains preassim_mean.nc and postassim_mean.nc, which mean the posterior and prior estimate of atmosphere state variables and other processed variables. Either preassim_mean.nc or postassim_mean.nc contains the cloud water path (CWP, unit:kgm-2), cloud water content (CWC, which is the sum of the mixing ratio of six cloud hydrometeors, unit: kgkg-1), RE_CLOUD(unit:um), RE_ICE(unit:um), QVAPOR(unit:kgkg-1), T(the perturbation of potential temperature, unit:K).</p> <p>rain_rate.tar.gz contains the procssed output data for the rain rate corresponding to the nature run, the control run, and Exp-4 ~ Exp-5.</p>
Niche differentiation in the bank vole: Maxent input and output data
<p>Species-level environmental niche modelling has been crucial in efforts to understand how species respond to climate variation and change. However, species often exhibit local adaptation and intraspecific niche differences that may be important to consider in predicting responses to climate. Here, we explore if phylogeographic lineages of the bank vole originating from different glacial refugia (Carpathian, Western, Eastern and Southern) show niche differentiation, which would suggest a role for local adaptation in biogeography of this widespread Eurasian small mammal. We first model the environmental requirements for the bank vole using species-wide occurrences (210 filtered records) and then model each lineage separately to examine niche overlap and test for niche differentiation in geographical and environmental space. We then use the models to estimate past [Last Glacial Maximum (LGM) and mid-Holocene] habitat suitability to compare to previously hypothesized glacial refugia for this species. Environmental niches are statistically significantly different from each other for all pairs of lineages in geographical as well as environmental space and these differences cannot be explained by habitat availability within their respective ranges. Together with the inability of most of the lineages to correctly predict the distributions of other lineages, these result support intraspecific ecological differentiation in the bank vole. Model projections of habitat suitability during the LGM support glacial survival of the bank vole in the Mediterranean region as well as in central and western Europe. Niche differences between lineages and the resulting spatial segregation of habitat suitability suggest ecological differentiation has played a role in determining the present phylogeographic patterns in the bank vole. Our study illustrates that models pooling lineages within a species may obscure the potential for different response to climate change among populations.</p>
Simulation input files and analysis scripts for "Small ionic radii limit time step in Martini 3molecular dynamics simulations"
<p>Simulation input files and analysis scripts for "Small ionic radii limit time step in Martini 3molecular dynamics simulations".</p> <p>See <a href="https://aip.scitation.org/doi/10.1063/5.0095523">https://aip.scitation.org/doi/10.1063/5.0095523</a> for details.</p> <p>NOTE: some combinations of settings result in simulations that crash!<br> </p>
Input dataset for the life cycle optimization of biobased PET supply chain
<p>Dataset with economic and environmental information used for the life cycle optimization of biobased PET using miscanthus as feestock.</p>
Input data from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>These files are a 1GB fragments of a compressed archive containing the data used in the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance".</p> <p>To simplify the uploading process, the file was split into chunks using the `split` utility in Linux. They can be joined back together with the command `cat input_data* > input_data.tar.gz`</p> <p>All data used is either publicly available or generated by this project. The subsets of the Recount3 and GTEx that we used are not owned by us, so putting them in this creative commons repository should not be construed as re-licensing them. </p> <p> </p>
Data from: Do microorganism stoichiometric alterations affect carbon sequestration in paddy soil subjected to phosphorus input?
Ecological stoichiometry provides a powerful tool for integrating microbial biomass stoichiometry with ecosystem processes, opening far-reaching possibilities for linking microbial dynamics to soil carbon (C) metabolism in response to agricultural nutrient management. Despite its importance to crop yield, the role of phosphorus (P) with respect to ecological stoichiometry and soil C sequestration in paddy fields remains poorly understood, which limits our ability to predict nutrient-related soil C cycling. Here, we collected soil samples from a paddy field experiment after 7 years of superphosphate application along a gradient of 0, 30, 60, 90 (P-0 through P-90, respectively) kg P ha-1 y-1 in order to evaluate the role of exogenous P on soil C sequestration through regulating microbial stoichiometry. P fertilization increased soil total organic C and labile organic C by 1-14% and 4-96%, respectively, while rice yield is a function of the activities of soil β-1, 4-glucosidase (BG), acid phosphatase (AP) and the level of available soil P through a stepwise linear regression model. P input induced C limitation as reflected by decreases in the ratios of C:P in soil and microbial biomass. An ecoenzymatic ratio indicating microbial investment in C versus P acquisition, i.e., ln(BG):ln(AP), changed the ecological function of microbial C acquisition and was stoichiometrically related to P input. This mechanism drove a shift in soil resource availability by increasing bacterial community richness and diversity, and stimulated soil C sequestration in the paddy field by enhancing C degradation-related bacteria for the breakdown of plant-derived carbon sources. Therefore, the decline in the C:P stoichiometric ratio of soil microorganism biomass under P input was beneficial for soil C sequestration, which offered a "win-win" relationship for the maximum balance point between C sequestration and P availability for rice production in the face of climate change.
Input data for Sampedro et al
<p>Input data for reproduction of results and figures shown in Sampedro et al: "Residential energy demand, emissions, and expenditures at region and income-decile level for alternative SSP scenarios"</p>
Input files and structures for article "Carbon dioxide fixation in RuBisCO is protonation state dependent and irreversible"
<p>Pdb file and amber topology of the initial RuBisCO model together with parameter files for the MD simulations with AMBER to equilibrate the system.</p> <p>Structures of reactant, transition state and products in the enzyme RuBisCo used to correct the activation and reaction free energies. Molpro input files used in the projector-based embedding calculations are provided. The same holds for the calculations involving the Zn cation. </p> <p>Optimized structures used to calculate descriptors with conceptual DFT. </p>
Data for "Reactive nitrogen input and low rainfall mitigate soil respiration responses to warming"
<p>This dataset is used to make tables and figures for the paper entitled "Reactive nitrogen input and low rainfall mitigate soil respiration responses to warming" submitted to Ecology Letters in July 2022. It contains field monitoring data on soil respiration and ecosystem productivity and a meta-analysis database.</p>
Input data and model output for study about wind changes and impact on the Subtropical Front
<p>This dataset contains:</p> <p>Model data for the CONTROL simulation (CONTROL.gz)</p> <p>Model data for the SHIFT simulation (SHIFT.gz) where the westerly winds have been shifted by 1degree per decade</p> <p>Model data for the INCREASE simulation (INCREASE.gz) where the westerly winds have been incresaed by 1 percent per decade</p> <p>Reference dataset are provided (Argo.gz and Modiz.gz)</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.