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.
149
datasets available to search
ShareScore release 0.9.0
Dataset results
149 results for “offset”
Data for "PTP Over Wide Area Networks With Offset Measurement Outlier Filtering"
<p>Dataset used in the manuscript "PTP Over Wide Area Networks With Offset Measurement Outlier Filtering". This dataset contains synchronization accuracy measurements over long distance links using both NTP and PTP, as well as synthetically generated PTP replays used for offline testing.</p> <p>A detailed description of the contents is found in the <code>README.md</code> file at the root of the dataset.</p>
Dataset of Norwegian forest albedo carbon offset potential
<p>This dataset contains the following six files in geotiff format with additional user detail provided as a README.txt file: 1) Forest albedo effect in C-equivalent units for the present day climate 2) Forest albedo effect in C-equivalent units for the transient 21st century RCP4.5 climate 3) Forest albedo's carbon offset potential for the present day climate 4) Forest albedo's carbon offset potential for the transient 21st century RCP4.5 climate 5) Site Index (productivity class) of dominant tree species 6) Dominant tree species 7) README</p> <p>The dataset contains results and input data related the following publication: <br>Bright, R. M., Cataneo, N., Antón-Fernández, C., Eisner, S., Astrup, R., "Relevance of surface albedo to forestry policy in high latitude and altitude regions may be overvalued". <em>Environmental Research Letters, <span><a href="https://doi.org/10.1088/1748-9326/ad657e">https://doi.org/10.1088/1748-9326/ad657e</a></span></em> </p>
Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621
Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.
Supplementary data for "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise"
<p>This dataset comprises topography and bathymetric data at three coastal locations in Australia (Narrabeen), UK (Perranporth) and used for the publication "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise". Please refer to readme files for metadata</p>
Economic losses from hurricanes cannot be nationally offset under unabated warming - Data Supplement
<p>This data set includes the raw data for the figures of the article "Economic losses from hurricanes cannot be nationally offset under unabated warming".</p>
3D displacement field and fault-offset measurements for the northern Kaikōura ruptures
<p>Contents:</p> <p>1. East, north and vertical components of the co-seismic displacement field for three faults (the Kekerengu, Jordan and Upper Kowhai faults) that ruptured in the 2016 Kaikoura earthquake, New Zealand (east.tif, north.tif, vertical.tif).</p> <p>2. Shapefiles containing offsets across the faults of interest, measured from the displacement field (shapefiles.zip).</p> <p>3. CSV files containing offsets across the faults of interest, measured from the displacement field (csvs.zip). </p> <p>Our methodology is described in the following manuscript:</p> <p>Howell et al., 2019. 3D surface displacements during the 2016 MW 7.8 Kaikōura earthquake (New Zealand) from photogrammetry-derived point clouds, Journal of Geophysical Research Solid Earth, submitted.</p>
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Tree Growth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025
Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes cumulative stem biomass carbon data (from pre-treatment in 2012 until 2022) and annual stem biomass growth rates (not cumulative) for 2015-2022 for the red maple trees at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: Soil Temperature, Soil Frost, and Snow Depth Data in support of "Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems", Conrad-Rooney et al. PNAS 2025
Data associated with the publication: Conrad-Rooney E, AB Reinmann, PH Templer. Declining Winter Snowpack Offsets Carbon Storage Enhancement from Growing Season Warming in Northern Temperate Forest Ecosystems. Proceedings of the National Academy of Sciences, 2025. This dataset includes soil temperature (winter 2021-2022) and snow depth and frost depth (winter 2022-2023) at the Climate Change Across Seasons Experiment. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Basic Considerations for the Observability of Kinematically Offset Binary AGN
<p>KDE resampled massive black-hole (MBH) binary population used for the analysis in Kelley 2020, <em>Considerations for the Observability of Kinematically Offset Binary AGN</em> (2005.10255).</p> <p>We start with a population of MBH binaries derived from the Illustris cosmological simulations, and evolved using semi-analytic post-processing models. These methods are described in Kelley et al. 2016 (1606.01900) and Kelley et al. 2017a,b (1702.02180 and 1711.00075). We then use the kernel density estimation (KDE) technique to resample this population from the Illustris volume, into a sample of binaries from a full, simulated light-cone of observations. The binaries are resampled in bins of observed orbital period. The number of resampled binaries in a given period bin are restricted to 500,000. When this occurs, the 'weight' of those binaries is assigned a value greater than one, while the 'weight' of binaries in unrestricted period bins is set to unity. For example, if the 'true' number of binaries in a bin should have been 1e6, the number of samples would be restricted to 5e5, and the weight of each of those binaries would be set to 2.0. If the number of binaries in a bin were instead 2e5, which is less than the limit, then each of those binaries is given a weight of 1.0. Only a single realization of this sample is included, but additional realizations can be produced if needed and on request.</p> <p>The data are presented in hdf5 format and contain the following arrays:</p> <pre><code># Meta-Data `periods`: shape: (201,) units: seconds descr: the edges of the bins in observed orbital period from which the population was sampled. `counts`: shape: (200,) units: None descr: The number of binaries produced in each period bin. This number for each bin is capped at 500,000. The sum of these values equals the total number of samples (70622013). # Data In this particular realization of data, the number of sampled binaries is `S = 70622013`. `mtot`: shape: (S,) units: grams descr: the total mass of each binary (`M = M1 + M2`). `mrat`: shape: (S,) units: None descr: The mass ratio of each binary, `q = M2/M1 <= 1.0`, where M2 is the mass of the secondary (lower mass) component. `pobs`: shape: (S,) units: seconds descr: The orbital period of each binary in the observer's rest frame (at redshift, `z = 0`). `redz`: shape: (S,) units: None descr: The redshift of each binary. `weights`: shape: (S,) units: None descr: The number of 'true' binaries (i.e. the number that are expected to occur in this realization of an observed universe) represented by this individual 'sample', and having similar binary parameters.</code></pre> <p> </p>
Data from: Plant uptake offsets silica release from a large Arctic tundra wildfire
Rapid climate change at high latitudes is projected to increase wildfire extent in tundra ecosystems by up to five-fold by the end of the century. Tundra wildfire could alter terrestrial silica (SiO2) cycling by restructuring surface vegetation and by deepening the seasonally-thawed active layer. These changes could influence the availability of silica in terrestrial permafrost ecosystems and alter lateral exports to downstream marine waters, where silica is often a limiting nutrient. In this context, we investigated the long-term effects of the largest Arctic tundra fire in recent times on plant and peat amorphous silica content and dissolved silica concentration in streams. Ten-years after the fire, vegetation in burned areas had 73% more silica in aboveground biomass compared to adjacent, unburned areas. This increase in plant silica was attributable to significantly higher plant silica concentration in bryophytes and increased prevalence of silica-rich gramminoids in burned areas. Tundra fire redistributed peat silica, with burned areas containing significantly higher amorphous silica concentrations in the O-layer, but 29% less silica in peat overall due to shallower peat depth post burn. Despite these dramatic differences in terrestrial silica dynamics, dissolved silica concentration in tributaries draining burned catchments did not differ from unburned catchments, potentially due to the increased uptake by terrestrial vegetation. Together, these results suggest that tundra wildfire enhances terrestrial availability of silica via permafrost degradation and associated weathering, but that changes in lateral silica export may depend on vegetation uptake during the first decade of post-wildfire succession.
Yield increases mediated by pollination and carbon payments can offset restoration costs in coffee landscapes
<p>This dataset and code can be used to reproduce results from the article with the same title</p> <p>The scripts to generate the farm-level scenarios and the regional-level scenarios are as following:</p> <ul> <li><strong>farm-level</strong> = <em>optimization_scenarios.R</em></li> <li><strong>regional-level</strong> = <em>optimization_scenarios_rl.R</em></li> </ul> <p>The <strong>properties boundaries</strong> and <strong>APP limits</strong> for the municipalities considered in this study can be obtained from the adress:</p> <ul> <li><a href="https://www.car.gov.br/publico/imoveis/index">https://www.car.gov.br/publico/imoveis/index</a>.</li> </ul> <p>The <strong>list of municipalities</strong> is located at the file "list_mun.csv" within the tables folder.</p> <p>The raster with <strong>land-use and land-cover classes</strong> is classified as follows:</p> <ul> <li>class 0: all other uses;</li> <li>class 1: natural vegetation;</li> <li>class 2: coffee;</li> </ul> <p>The <strong>estimated yield per property</strong> is at the yield_estimation.csv file. Each property (n=507) is identified with an unique ID. The dataframe contains:</p> <ul> <li>the predicted yield in coffee bags/ha (<em>predictec_coffee_bags</em>);</li> <li>yield in USD/ha (<em>yield_USD_future</em>); the difference in yield bewteen the future scenarios and the baseline scenario (<em>diff_USD</em>);</li> <li>the net present value of the difference between the future scenarios and the baseline (<em>npv_yield_USD_future</em>)</li> </ul> <p>The script "merging_scen_land_use.R" was used to combine the restoration scenarios generated by the optimization model with the baseline land-use and land-cover;</p> <p> </p> <p> </p>
Data for: Dissolved organic matter (DOM) offsets the detrimental effects of climate change in the nitrogen fixing cyanobacterium Crocosphaera
<div> <div> <div> <div> <p>Diazotrophs provide a significant reactive nitrogen source in the ocean. Increased warming and stratification may decrease nutrient availability in the future, forcing microbial communities to use dissolved organic matter (DOM). Not depending on reactive nitrogen availability, diazotrophs may be "winners" in a nutrient depleted ocean. However, their ability to exploit DOM may influence this success. We exposed cultures of the widespread <em>Crocosphaera</em> to low (26°C, pH 8.1), moderate (28°C, pH 8.0), and extreme (30°C, pH 7.9) climate change scenarios, under control or DOM-amended conditions. Growth was suboptimal in the low and extreme treatments, and favoured in the moderate treatment. DOM was preferred as a carbon source regardless of the treatment, and promoted N<sub>2</sub> fixation in extreme conditions. This was reflected in the increased expression of photosynthesis genes to obtain energy. DOM provides <em>Crocosphaera</em> with a key ecological advantage, possibly dictating diazotroph-derived nitrogen inputs in the future ocean.</p> </div> </div> </div> </div>
Emergence and function of cortical offset responses in sound termination detection
<p>Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining <i>in vivo</i> electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated <i>de novo</i>. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization.</p>
Offset central stop data
<p>Results of shifting optics on the full width at half maximum and fractional integrated intensity of focal spot.</p>
Data from: Seeing the Forest for the trees: Assessing genetic offset predictions from Gradient Forest
<p><span>Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF predicted environmental association of adapted alleles and a new environment (GF Offset), is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic "population genetic" model with a single environmentally adapted locus; and (3) a polygenic "quantitative genetic" model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation.</span></p>
California's forest carbon offset buffer pool is severely undercapitalized
<p>A copy of the input, intermediate, and final output data files underlying the analysis and figures of Badgley et al., "California’s forest carbon offset buffer pool is severely undercapitalized."</p>
Population connectivity and genetic offset in the spawning coral Acropora digitifera in Western Australia
<p><span>Anthropogenic </span>climate change has caused widespread loss of species biodiversity and ecosystem productivity across the globe, particularly on tropical coral reefs. Predicting the future vulnerability of reef-building corals, the foundation species of coral reef ecosystems, is crucial for cost-effective conservation planning in the Anthropocene. In this study, we combine regional population genetic connectivity and seascape analyses to explore patterns of genetic offset (the mismatch of gene-environmental associations under future climate conditions) in <em>Acropora digitifera</em> across 12 degrees of latitude in Western Australia. Our data revealed a pattern of restricted gene flow and limited genetic connectivity among geographically distant reef systems. Environmental association analyses identified a suite of loci strongly associated with the regional temperature variation. These loci helped forecasting future genetic offset in random forest and generalised dissimilarity models. These analyses predicted pronounced differences in the response of different reef systems in Western Australia to rising temperatures. Under the most optimistic future warming predictions (RCP 2.6), we observed a general pattern of increasing genetic offset with latitude. Under the most extreme climate scenario (RCP 8.5 in 2090-2100), coral populations at the Ningaloo World Heritage Area were predicted to experience a higher mismatch in genetic composition, compared to populations in the inshore Kimberley region. The study suggest complex and spatially heterogeneous patterns of climate-change vulnerability in coral populations across Western Australia, reinforcing the notion that regionally tailored conservation efforts will be most effective at managing coral reef resilience into the future.</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</p>
Rock magnetic data from IODP Exp. 382 Sites U1537 and U1538 to support Reilly et al. "A geochemical mechanism for >10 m offsets of magnetic reversals inferred from the comparison of two Scotia Sea drill sites"
<p>Rock magnetic data from IODP Exp. 382 Sites U1537 and U1538 to support Reilly et al. "A geochemical mechanism for >10 m offsets of magnetic reversals inferred from the comparison of two Scotia Sea drill sites"</p><p>Excel Files:</p><ul><li>U1537_CubeSummary_Zenodo.xlsx : Summary of NRM, ARM, IRM, and magnetic susceptibility investigations on U1537 cube samples</li><li>U1538_CubeSummary_Zenodo.xlsx : Summary of NRM, ARM, IRM, and magnetic susceptibility investigations on U1538 cube samples</li></ul><p>Zip Files:</p><ul><li>FORC_Data.zip : First order reversal curve data files in MicroMag format for samples discussed in paper</li><li>DCD_Data.zip : DC Demagnetization curve data files for samples discussed in paper</li><li>Hysteresis_Data.zip : Hysteresis Loops for samples discussed in paper</li><li>MPMS_Data.zip : Data collected on Magnetics Property Measurement System 3, including Field Cooled/Zero Field Cooled Curves, Low Temperature Cycling of Room Temperature IRM, and AC Susceptibility</li></ul><p> </p><p>NRM = Natural Remanent Magnetization; ARM = Anhysteretic Remanent Magnetization; IRM = Isothermal Remanent Magnetization</p>
A global database of nature-based carbon offset project boundaries
<p><strong>Overview</strong></p> <p>Nature-based climate solutions (NBS) have become an important component of strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies, however, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data. Herein, we detail methods and present a database of nature-based offset project boundaries. This database provides the locations of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries (<em>n</em>=433, 75.3%) as well as manual georeferencing and digitization (<em>n</em>=127, 22.1%). Database entries include three varieties of carbon projects: avoided deforestation, afforestation, reforestation and re-vegetation, and improved forest management. An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 +/- 0.015).</p> <p>Details about using the database are provided in the README.txt file.</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.