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.
477
datasets available to search
ShareScore release 0.9.0
Dataset results
477 results for “input data”
Data from: Anthropogenic nutrient inputs affect productivity-biodiversity relationships in marine tintinnid assemblages
Open the record for dataset details and reuse information.
Data for: Predation and biophysical context control long-term carcass nutrient inputs in an Andean ecosystem
Open the record for dataset details and reuse information.
Data from: No evidence for sex differences in the electrophysiological properties and excitatory synaptic input onto nucleus accumbens shell medium spiny neurons
Open the record for dataset details and reuse information.
Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges
Open the record for dataset details and reuse information.
Data from: Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs
Open the record for dataset details and reuse information.
COVID-19 patient data from a study in Singapore curated for input into an in silico infection model
Open the record for dataset details and reuse information.
Data from: Microplastic biodegradability does not modify plant carbon input in soil but accelerate soil carbon loss in agroecosystems
Open the record for dataset details and reuse information.
Input data and model implementation from: How do terrestrial wildlife communities respond to small-scale Acacia plantations embedded in harvested tropical forest?
Open the record for dataset details and reuse information.
Data from: Nitrifier controls on soil NO and N2O emissions in three chaparral ecosystems under contrasting atmospheric N inputs
Open the record for dataset details and reuse information.
Data from: Riverine transport and nutrient inputs affect phytoplankton communities in a coastal embayment
Open the record for dataset details and reuse information.
Merged Global Pluvial Floods DataBase (Input Data)
<p>This repository contains the input data used in the Jupyter notebook downloadable from Github <a href="https://github.com/FatimaPillosu/Merged_Global_Pluvial_Floods_DataBase">here</a>.</p> <p>Such input data consists in two main datasets:</p> <ul> <li>A raw (global and regional) flood reports from four different databases.</li> <li>A global daily rainfall analysis from NOAA (CPC_GLOBAL_PRCP_v1.0).</li> </ul> <p>The Jupyter notebook runs a Python code that post-processes the raw flood reports, using information extracted from other datasets, to select some reports of interest (mainly regarding pluvial and flash floods). At a later stage, such reports are merged into a single database for global pluvial/flash flood reports. The Jupyter notebook also runs a Metview-Python code to visualize partial and final results as map plots.</p> <p>The four original databases are:</p> <ol> <li>FloodList, FL (Global domain): <a href="http://floodlist.com/">http://floodlist.com/</a></li> <li>Emergency Events Database, EMDAT (Global domain): <a href="https://www.emdat.be/">https://www.emdat.be/</a></li> <li>European Severe Weather Database, ESWD (Europe): <a href="https://www.essl.org/cms/european-severe-weather-database/">https://www.essl.org/cms/european-severe-weather-database/</a></li> <li>Storm Events Database, SED (USA): <a href="https://www.ncdc.noaa.gov/stormevents/">https://www.ncdc.noaa.gov/stormevents/</a></li> </ol> <p><em>NOTE: </em>For more details about these databases (documentation, licenses, etc.), look at the README.md file.</p> <p><em>NOTE: </em>The data in this repository is intended for an exclusive NON-COMMERCIAL academic or personal use, and it is released under the Creative Commons Attribution-ShareAlike 4.0 International Public License. For more information, look at the LICENSE.md file.</p>
Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Input and validation data for the Askervein Hill benchmark
<p>This dataset has been generated within the IEA-Wind Task 31 Wakebench project to validate microscale flow models over complex terrain. The data has been digitized from the original technical reports of the experiment from Taylor and Teunissen (1983/1985).</p>
Dataset for "Recursive Input and State Estimation: A General Framework for Learning from Time Series with Missing Data"
<p>Dataset for "Recursive Input and State Estimation: A General Framework for Learning from Time Series with Missing Data"</p> <p> </p> <p>Missing values in the blood glucose datasets are represented with -2.</p>
Numerical model code, input files and output data for publication ``Mixing and Transformation in a Deep Western Boundary Current: a case study''
<p>Contains numerical model data (code, input files, selected output) to supplement publication ``Mixing and Transformation in a Deep Western Boundary current'', by Spingys and co-authors. All umerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations used in the above-referenced paper.</p>
Tethys - Input and Output Data
<p>This dataset includes input and output data for 20 combinations of GCM/RCP scenarios for the Tethys model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
CGAM LAC - Input and Output Data
<p>This dataset includes input and output data for 20 combinations of GCM/RCP scenarios for the GCAM LAC (v5.1.3) model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
Data from: Multi-alternative decision making with non-stationary inputs
One of the most widely implemented models for multi-alternative decision-making is the multihypothesis sequential probability ratio test (MSPRT). It is asymptotically optimal, straightforward to implement, and has found application in modelling biological decision-making. However, the MSPRT is limited in application to discrete ('trial-based'), non-time-varying scenarios. By contrast, real world situations will be continuous and entail stimulus non-stationarity. In these circumstances, decision-making mechanisms (like the MSPRT) which work by accumulating evidence, must be able to discard outdated evidence which becomes progressively irrelevant. To address this issue, we introduce a new decision mechanism by augmenting the MSPRT with a rectangular integration window and a transparent decision boundary. This allows selection and de-selection of options as their evidence changes dynamically. Performance was enhanced by adapting the window size to problem difficulty. Further, we present an alternative windowing method which exponentially decays evidence and does not significantly degrade performance, while greatly reducing the memory resources necessary. The methods presented have proven successful at allowing for the MSPRT algorithm to function in a non-stationary environment.
Data from: Input matters matter: bioclimatic consistency to map more reliable species distribution models
1. Accuracy of global bioclimatic databases is essential to understand biodiversity-environment relationships. Many studies have explored biases and uncertainties related to species distribution models (SDMs) but the effect of choosing a specific database among the different alternatives has not been previously assessed. 2. The lack of bioclimatic congruence (degree of agreement) between different databases is a main concern in distribution modelling and it is critical in single-source models, for which the database choice is decisive. In order to prevent unreliable predictions derived from distorted input data, SDMs accuracy can be assessed by mapping model predictions according to a bioclimatic congruence measure derived from the comparison of multiple databases, which can be achieved with the bioclimatic consistency maps that we propose in this study. Here, i) we present the first global-scale bioclimatic congruence map to analyse environmental mismatches between recently updated bioclimatic databases. We also test the importance of input matters on the reliability of distribution models of sixteen mammals, by addressing ii) inconsistencies among species response curves (temperature and precipitation), and iii) discrepancies among SDMs predictions depending on the chosen bioclimatic database. Finally, iv) we propose a strategy to assess bioclimatic consistency of model predictions, showing its application to the specific case of Litocranius walleri. 3. Our results confirm that the single-source modelling approach greatly influences the estimation of species-environment relationship and consequently, bias spatial predictions derived from SDMs. This is especially true for studies conducted in polar and mountainous regions which showed the smallest bioclimatic congruence. We show that by adding bioclimatic congruence to SDMs projections, we can build a bioclimatic consistency map that enables the detection of both risky and consistent areas, as revealed for the case of L. walleri. 4. Assessing uncertainty in bioclimatic input data is key to avoid erroneous conclusions in macroecological and biogeographical studies. The spatial characterisation of bioclimatic consistency provides an adequate empirical framework which effectively illustrates bioclimatic data limitations. We strongly recommend that this new strategy should be formally and systematically incorporated into distribution modelling to build more reliable SDMs, which are essential to develop successful biodiversity conservation programmes.
Data from: Reduced tillage, but not organic matter input, increased nematode diversity and food web stability in European long-term field experiments
Soil nematode communities and food web indices can inform about the complexity, nutrient flows and decomposition pathways of soil food webs, reflecting soil quality. Relative abundance of nematode feeding and life-history groups are used for calculating food web indices, i.e. maturity index (MI), enrichment index (EI), structure index (SI) and channel index (CI). Molecular methods to study nematode communities potentially offer advantages compared to traditional methods in terms of resolution, throughput, cost and time. In spite of such advantages, molecular data have not often been adopted so far to assess the effects of soil management on nematode communities and to calculate these food web indices. Here, we used high-throughput amplicon sequencing to investigate the effects of tillage (conventional vs reduced) and organic matter addition (low vs high) on nematode communities and food web indices in ten European long-term field experiments and we assessed the relationship between nematode communities and soil parameters. We found that nematode communities were more strongly affected by tillage than by organic matter addition. Compared to conventional tillage, reduced tillage increased nematode diversity (23% higher Shannon diversity index), nematode community stability (12% higher MI), structure (24% higher SI), and the fungal decomposition channel (59% higher CI), and also the number of herbivorous nematodes (70% higher). Total and labile organic carbon, available K and microbial parameters explained nematode community structure. Our findings show that nematode communities are sensitive indicators of soil quality and that molecular profiling of nematode communities has the potential to reveal the effects of soil management on soil quality.
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.