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.
124
datasets available to search
ShareScore release 0.9.0
Dataset results
124 results for “predictive variables”
LAGOS-NE Shallow Lakes: a dataset of lake variables and multi-scaled ecological context variables used to predict and compare trophic status and TP:CHLa relationships between shallow and non-shallow lakes in the Upper Midwest and Northeastern United States.
We conducted a macroscale study of 2,210 shallow lakes (mean depth ≤ 3m or a maximum depth ≤ 5m) in the Upper Midwestern and Northeastern U.S. We asked: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4,360 non-shallow lakes, and do results differ by hydrologic connectivity class? To answer this question, we assembled the LAGOS-NE Shallow Lakes dataset described herein, a dataset derived from existing LAGOS-NE, LAGOS-DEPTH, and LAGOS-CLIMATE datasets. Response data variables were the median of available summer (e.g., 15 June to 15 September) values of total phosphorus (TP) and chlorophyll a (CHLa). Predictor variables were assembled at two spatial scales for incorporation into hierarchical models. At the local or lake-specific scale (including the individual lake, its inter-lake watershed [iws] or corresponding HU12 watershed), variables included those representing land use/cover, hydrology, climate, morphometry, and acid deposition. At the regional scale (e.g., HU4 watershed), variables included a smaller set of predictor variables for hydrology and land use/cover. The dataset also includes the unique identifier assigned by LAGOS-NE(lagoslakeid); the latitude and longitude of the study lakes; their maximum and mean depths along with a depth classification of Shallow or non-Shallow; connectivity class (i.e., whether a lake was classified as connected (with inlets and outlets) or unconnected (lacking inlets); and the zone id for the HU4 to which each lake belongs. Along with the database, we provide the R scripts for the hierarchical models predicting TP or CHLa (TPorCHL_predictive_model.R), and the TP—CHLa relationship (TP_CHL_CSI_Model.R) for depth and connectivity subsets of the study lakes.
Prediction of Conformational Variability for RRM proteins in inter3m data base
<p>Predictions for protein Conformational Variability for the entries in InteR3M (<a href="https://inter3mdb.loria.fr/">https://inter3mdb.loria.fr/</a>), performed with the software ConforMine (in preparation).</p>
The effects of solar cycle variability on nanodust dynamics in the inner heliosphere: Predictions for future STEREO A/WAVES measurements
<p>This dataset contains results from the associated manuscript in JGR Space Physics. The dataset consists of two-dimensional nanodust grain fluxes in the HEEQ equatorial plane for various specified Carrington Rotations (CRs), as specified in the parent manuscript.</p>
Mean NDVI Values (1982-2018) and Future Predictions Using CHELSA Bioclim Variables for Türkiye
<p>This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios.</p> <h4>Contents:</h4> <ul> <li><strong>Historical NDVI Data (1982-2018)</strong>: Mean NDVI values derived from remote sensing data.</li> <li><strong>Future NDVI Predictions</strong>: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.</li> </ul> <h4>Methodology:</h4> <ol> <li><strong>Model Training</strong>: <ul> <li>A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.</li> <li>The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.</li> </ul> </li> <li><strong>Future Predictions</strong>: <ul> <li>Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.</li> <li>Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).</li> </ul> </li> </ol> <h4>Data Specifications:</h4> <ul> <li><strong>Extent</strong>: Covers the geographical area of Türkiye and adjacents.</li> </ul> <h4>Sources:</h4> <ul> <li><strong>NDVI Data</strong>: <ul> <li>Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.</li> </ul> </li> <li><strong>CHELSA Bioclim Data</strong>: <ul> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122" target="_new" rel="noreferrer">https://doi.org/10.1038/sdata.2017.122</a></li> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4" target="_new" rel="noreferrer">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a></li> </ul> </li> </ul>
Dataset for "Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"
<p>Datasets for preprint (https://doi.org/10.1101/2020.07.10.190785) entitled:</p> <p>"Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"</p> <p>Contains:</p> <p>- All data files for scripts quoted in the preprint and deposited at https://github.com/krasileva-group/hvNLR</p> <p>- Clade Membership Tables</p> <p>- Clade Alignment Files</p> <p>- Clade Trees</p> <p>- Excel files for Figure S1, Figure S2, and Table 1</p>
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B in PhylOgeOgraphy and pOtential distributiOn OF Sturnira lilium and S. giannae (ChirOptera: PhyllOstOmidae) With range eXtensiOn FOr S. giannae in the CerradO and Pantanal biOmes
Figure 6. Response curve showing how each environmental variable affected the Maxent prediction. Variables A, B, and C were the ones that most affected the potential distribution of S. giannae, and D, E, and F most affected the potential distribution of S. lilium. The curves show the average response of the 10 replicate Maxent runs (red) and the standard deviation (blue).
Fig. 3 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 3. Cumulative distribution plots divided by year for (A) T. alpinus and (B) T. speciosus. For each species 2013 is shown in red, 2014 in teal, 2015 in pink. The x-axis represents each host individual, ordered from least to most flea infested, and the y-axis shows the cumulative proportion of total flea counts. The dotted line indicates individuals in the 90th percentile of flea abundances, illustrating that the top 10% most infected chipmunks usually account for close to 50% of all counted fleas. The proportion of individuals without fleas in each year is represented graphically as the proportion at which each colored line departs from the x-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 4. Relationships between fecal glucocorticoid metabolite levels, sex, and flea abundance for (A) T. alpinus and (B) T. speciosus. Points show the mean ± S.E. number of fleas counted for female (white) and male (black) individuals within FGM categories (FGM values were rounded to the nearest 10). Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each sex.
Fig. 2 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 2. Patterns of flea abundance across years, hosts, and flea species. Overall average flea abundances (A–B) and abundances of each flea species (C–D) in each year for T. alpinus (A, C) and T. speciosus (B, D). Abundances of each flea species on hosts of each sex (Males: closed circles, Females: open circles) on T. alpinus (E) and T. speciosus (F).
Fig. 5 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 5. Relationships between flea abundances and (A) the second principal component of temperature data; or (B) elevation for T. alpinus (white) and T. speciosus (black). Points show the mean ± S.E. number of fleas counted for a given study site in a given year. For each study site in each year, a mean ± S.E. temperature or elevation value is shown. Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each species.
Fig. 1 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 1. Map showing study sites. Sites (see Supplementary Data S1 for more information) located in and around Yosemite National Park (green) were visited either in all three years (2013, 2014, and 2015; black), in two of the years (yellow), or in only one year (red). Yellow and black lines show significant roadways in the area. Lakes are shown in blue, including Mono Lake at top right. Inset shows Yosemite National Park (green) on a map of California. Site codes: AL: Arrowhead Lake; CL: Cathedral Lake (upper); GA: Glen Aulin; GL: Gaylor Lakes; HC: Hoffmann Creek; MA: Mammoth Lakes; ML: May Lake; PC: Porcupine Creek; SL: Saddlebag Lake; SLN: Saddlebag Lake, north-side (Greenstone and Steelhead Lakes); TM: Tuolumne Meadows. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
WiBB: An integrated method for quantifying the relative importance of predictive variables
<p>This dataset contains simulated datasets, empirical data, and R scripts described in the paper: "Li, Q. and Kou, X. (2021) <em>WiBB</em>: An integrated method for quantifying the relative importance of predictive variables. Ecography (DOI: 10.1111/ecog.05651)".</p> <p>A fundamental goal of scientific research is to identify the underlying variables that govern crucial processes of a system. Here we proposed a new index, <em>WiBB</em>, which integrates the merits of several existing methods: a model-weighting method from information theory (<em>Wi</em>), a standardized regression coefficient method measured by <em>ß</em>* (<em>B</em>), and bootstrap resampling technique (<em>B</em>). We applied the <em>WiBB</em> in simulated datasets with known correlation structures, for both linear models (LM) and generalized linear models (GLM), to evaluate its performance. We also applied two other methods, relative sum of wight (<em>SWi</em>), and standardized beta (<em>ß</em>*), to evaluate their performance in comparison with the <em>WiBB</em> method on ranking predictor importances under various scenarios. We also applied it to an empirical dataset in a plant genus <em>Mimulus</em> to select bioclimatic predictors of species' presence across the landscape. Results in the simulated datasets showed that the <em>WiBB</em> method outperformed the <em>ß</em>* and <em>SWi</em> methods in scenarios with small and large sample sizes, respectively, and that the bootstrap resampling technique significantly improved the discriminant ability. When testing <em>WiBB</em> in the empirical dataset with GLM, it sensibly identified four important predictors with high credibility out of six candidates in modeling geographical distributions of 71 <em>Mimulus</em> species. This integrated index has great advantages in evaluating predictor importance and hence reducing the dimensionality of data, without losing interpretive power. The simplicity of calculation of the new metric over more sophisticated statistical procedures, makes it a handy method in the statistical toolbox.</p>
Predicting International and Internal Migration in Guatemala with Social, Physical and Climatic Variables (Data)
<p>Accompanying data for the Depsky and Pons (2023) PLoS ONE publication - Predicting International and Internal Migration in Guatemala with Social, Physical and Climatic Variables.</p> <p>This repository contains tables of the raw 2018 Guatemalan national census values at the individual, household, and residence levels, as well as its migration-specific data table. Municipality-average climate values from ERA5-Land are provided as well, in addition to a shapefile of the municipal boundaries. All standardized outcome and predictor variables used for each of the five models are provided as separate tables as well.</p>
Data from: Convolutional neural networks trained on internal variability predict forced response of TOA radiation by learning the pattern effect
Open the record for dataset details and reuse information.
WiBB: An integrated method for quantifying the relative importance of predictive variables
Open the record for dataset details and reuse information.
CNNpred: CNN-based stock market prediction using a diverse set of variables
<p>This dataset contains several daily features of S&P 500, NASDAQ Composite, Dow Jones Industrial Average, RUSSELL 2000, and NYSE Composite from 2010 to 2017. It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of "CNNpred: CNN-based stock market prediction using a diverse set of variables".</p>
Data from: Complexity of frequency receptive fields predicts tonotopic variability across species
<p><span>Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these global tonotopic maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that local heterogeneity of frequency preferences is not unique to rodents. Using two-photon calcium imaging in layers 2/3, we found that local variance in frequency preferences is equivalent in ferrets and mice. Neurons with multipeaked frequency tuning are less spatially organized than those tuned to a single frequency in both species. Furthermore, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a sampling bias towards neurons with simple frequency tuning. These results help explain previous inconsistencies in cortical topography across species and recording techniques. </span></p>
Data from: Climate variability predicts thermal limits of aquatic insects across elevation and latitude
Janzen's extension of the climate variability hypothesis posits that increased seasonal variation at high latitudes should result in greater temperature overlap across elevations, and favor wider thermal breadths in temperate organisms compared to their tropical counterparts. We tested these predictions by measuring stream temperatures and thermal breadths (i.e. the difference between the critical thermal maximum and minimum) of 62 aquatic insect species from temperate (Colorado, USA) and tropical (Papallacta, Ecuador) streams spanning an elevation gradient of ca. 2000m. Temperate streams exhibited greater seasonal temperature variation and overlap across elevations than tropical streams, and as predicted, temperate aquatic insects exhibited broader thermal breadths than tropical insects. However, elevation had contrasting effects on patterns of thermal breadth. In temperate species, thermal breadth decreased with increasing elevation because CTMAX declined with elevation while CTMIN was similar across elevations. In tropical insects, by contrast, CTMAX declined less sharply than CTMIN with elevation, causing thermal breadth to increase with elevation. These macrophysiological patterns are consistent with the narrower elevation ranges found in other tropical organisms, and they extend Janzen's climate variability hypothesis to freshwater streams. Furthermore, because lowland tropical aquatic insects have the narrowest thermal breadths of any region, they may be particularly vulnerable to short-term extreme changes in stream temperature.
Genetic variants regulating the immune response improve the prediction of COVID-19 severity provided by clinical variables
<p>Data set generated to evaluate the association between SNPs from genes related to SARS-CoV-2 pathogenesis and their clinical outcome.</p>
An updated life history scheme for marine fishes predicts recruitment variability and sensitivity to exploitation
<p><b>Aim:</b> Patterns of population renewal in marine fishes are often irregular and lead to volatile fluctuations in abundance that challenge management and conservation efforts. Here, we examine the relationship between life history strategies and recruitment variability in exploited marine fish species using a macroecological approach. <b>Location:</b> Global ocean.</p> <p><b>Time period:</b> 1950-2018.</p> <p><b>Major taxa studied:</b> Bony and cartilaginous fish.</p> <p><b>Methods:</b> Based on trait data for 244 marine fish species, we objectively extend the established Equilibrium-Periodic-Opportunistic (<i>E-P-O</i>) life history classification scheme to include two additional emergent life history strategies: "Bet-hedgers" (<i>B</i>) and Salmonic (<i>S</i>) strategists. <i>B</i> strategists include Rockfishes and other species inhabiting patchy benthic habitats with life histories that blend characteristics of <i>E</i> and <i>P</i> species; they combine very long lifespans with elevated investments in both parental care and fecundity. <i>S</i> strategists are comprised of mostly salmonids that share life history characteristics with <i>E</i> and <i>O</i> species: elevated investments in parental care reminiscent of <i>E</i> strategists, but with reduced fecundity and short lifespans characteristic of <i>O</i> species. We analyzed how the <i>E-B-P-O-S</i> life history classification mapped onto patterns of recruitment variability observed in population time series data (n = 156 species).</p> <p><b>Results:</b> Generalized linear models suggest that life history strategy explains a modest, yet significant amount of recruitment variability across species. Greater predictive power arose after controlling for increased recruitment variance associated with variable fishing pressure, with <i>O</i> strategists showing the strongest sensitivity. <i>B</i> strategists were similarly susceptible to exploitation as <i>P</i> stocks, but their longer times to maturity make them particularly vulnerable to overfishing. <b>Main conclusions:</b> A broader recognition of the distinct ecology of Salmonic and Bet-hedger groups is important when studying life history strategies in marine fish. More generally, our results stress the importance of considering life history strategies for understanding patterns of recruitment variability across fish stocks.</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.