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.
4,276
datasets available to search
ShareScore release 0.9.0
Dataset results
4,276 results for “Estimation”
Wireless Link Quality Estimation on FlockLab - and Beyond
<p>This repository contains wireless link quality estimation data for the FlockLab testbed [1,2]. The rationale and description of this dataset is described in a the following abstract (pdf is included in this repository -- see below).</p> <blockquote> <p><strong>Dataset: Wireless Link Quality Estimationon FlockLab – and Beyond</strong><br> Romain Jacob, Reto Da Forno, Roman Trüb, Andreas Biri, Lothar Thiele<br> DATA '19 Proceedings of the 2nd Workshop on Data Acquisition To Analysis, 2019</p> </blockquote> <p><strong>Data collection scenario</strong></p> <p>The data collection scenario is simple. Each FlockLab node is assigned one dedicated time slot. In this slot, a node sends 100 packets, called strobes. All strobes have the same payload size and use a given radio frequency channel and transmit power. All other nodes listen for the strobes and log packet reception events (i.e., success or failed). </p> <p>The test scenario is ran every two hours on two different platforms: the TelosB [3] and DPP-cc430 [4] platforms. We used all nodes currently available at test time (between 27 and 29).</p> <p><strong>Final dataset status</strong></p> <ul> <li>3 months of data with about 12 tests per day per platform</li> <li>5 month of data with about 4 tests per day per platform</li> </ul> <p><strong>Data collection firmware</strong></p> <p>We are happy to share the link quality data we collected for the FlockLab testbed, but we also wanted to make it easier for others to collect similar datasets for other wireless networks. To achieve this, we include in this repository the data collection firmware we design. The entire data collection scheduling and control is done entirely in software, in order to make the firmware usable in a large variety on wireless networks. We implemented our data collection software using Baloo [5], a flexible network stack design framework based on Synchronous Transmission. Baloo efficiently handles network time synchronization and offers a flexible interface to schedule communication rounds. The firmware source code is available in the Baloo repository [6].</p> <p>A set of experiment parameters can be patched directly in the firmware, which let the user tune the data collection without having to recompile the source code. This improves usability and facilitates automation. An example patching script is included in this repository. Currently, the following parameters can be patched:</p> <ul> <li>rf_channel,</li> <li>payload,</li> <li>host_id, and</li> <li>rand_seed</li> </ul> <p><em>Current supported platforms</em></p> <ul> <li>TelosB [3]</li> <li>DPP-cc430 [4]</li> </ul> <p><strong>Repository versions</strong></p> <ul> <li><strong>v1.4.1</strong><br> Updated visualizations in the notebook</li> <li><strong>v1.4.0</strong><br> Addition of data from November 2019 to March 2020.<br> Data collection is discontinued (the new FlockLab testbed is being setup).</li> <li><strong>v1.3.1</strong><br> Update abstract and notebook</li> <li><strong>v1.3.0</strong><br> Addition of October 2019 data.<br> The frequency of tests has been reduced to 4 per day, executing at (approximately) 1:00, 7:00, 13:00, and 19:00.<br> From October 28 onward, time shifted by one hour (2:00, 8:00, 14:00, 20:00).</li> <li><strong>v1.2.0</strong><br> Addition of September 2019 data.<br> Many missing tests on the 12, 13, 19, and 20 of September (due to construction works in the building).</li> <li><strong>v1.1.4</strong><br> Update of the abstract to have hyperlinks to the plots. Corrected typos.</li> </ul> <ul> <li><strong>v1.1.0</strong><br> Initial version.<br> Add the data collected in August 2019.<br> Data collected was disturbed at the beginning of the month and resumed normally on the August 13. Data from previous days are incomplete.</li> <li><strong>v1.0.0</strong><br> Initial version.<br> Contain collected data in July 2019, from the 10th to 30th of July.<br> No data were collected on the 31st of July (technical issue).</li> </ul> <p><strong>List of files</strong></p> <ul> <li><strong>yyyy-mm_raw_platform.zip</strong><br> Archive containing all FlockLab test result files (one .zip file per month and per platform).</li> <li><strong>yyyy-mm_preprocessed_all.zip</strong><br> Archive containing preprocessed csv files, one per month and per platform.</li> <li><strong>firmware.zip</strong><br> Archive containing the firmware for all supported platform.</li> <li><strong>firmware_patch.sh</strong><br> Example bash script illustrating the firmware patching.</li> <li><strong>parse_flocklab_results.ipynb </strong>[<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3731498/files/parse_flocklab_results.ipynb">open in nbviewer</a>]<br> Jupyter notebook used to create the pre-process data files. Also includes some example of data visualization.</li> <li><strong>parse_flocklab_results.html</strong><br> HTML rendering of the notebook (static).</li> <li><strong>plots.zip</strong><br> Archive containing high resolution visualization of the dataset, generated by the <strong>parse_flocklab_results </strong>notebook, and presented in the <strong>abstract</strong>.</li> <li><strong>abstract.pdf</strong><br> A 3 page abstract presenting the dataset.</li> <li><strong>CRediT.pdf</strong><br> The list of contributions from the authors.</li> </ul> <p><strong>References</strong></p> <p>[1] R. Lim, F. Ferrari, M. Zimmerling, C. Walser, P. Sommer, and J. Beutel, “FlockLab: A Testbed for Distributed, Synchronized Tracing and Profiling of Wireless Embedded Systems,” in <em>Proceedings of the 12th International Conference on Information Processing in Sensor Networks</em>, New York, NY, USA, 2013, pp. 153–166.</p> <p>[2] “FlockLab,” <em>GitLab</em>. [Online]. Available: <a href="https://gitlab.ethz.ch/tec/public/flocklab/wikis/home">https://gitlab.ethz.ch/tec/public/flocklab/wikis/home</a>. [Accessed: 24-Jul-2019].</p> <p>[3] Advanticsys, “MTM-CM5000-MSP 802.15.4 TelosB mote Module.” [Online]. Available: <a href="https://www.advanticsys.com/shop/mtmcm5000msp-p-14.html">https://www.advanticsys.com/shop/mtmcm5000msp-p-14.html</a>. [Accessed: 21-Sep-2018].</p> <p>[4] Texas Instruments, “CC430F6137 16-Bit Ultra-Low-Power MCU.” [Online]. Available: <a href="http://www.ti.com/product/CC430F6137">http://www.ti.com/product/CC430F6137</a>. [Accessed: 21-Sep-2018].</p> <p>[5] R. Jacob, J. Bächli, R. Da Forno, and L. Thiele, “Synchronous Transmissions Made Easy: Design Your Network Stack with Baloo,” in <em>Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks</em>, 2019.</p> <p>[6] “Baloo,” Dec-2018. [Online]. Available: <a href="http://www.romainjacob.net/research/baloo/">http://www.romainjacob.net/research/baloo/</a>.</p> <p> </p>
Arctic Ocean state estimates for 2009 using the GECCO model
<p>The dataset contains the 2009 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL). in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France
Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL).
Sesquialtera in the Colombian bambuco: Perception and estimation of beat and meter
<p> Supplementary material for the ISMIR2020 paper titled: " Sesquialtera in the Colombian bambuco: Perception and estimation of beat and meter". The data in this repository includes the audio files, transcriptions and annotations of the 10 bambucos used in the study.</p> <p>This work was conducted in the context of the ACMus project. You can find more information about our project <a href="https://acmus-mir.github.io/">here.</a></p> <p>For more information about ACMUS-MIR, our annotated dataset of Colombian music from the Andes region, you can visit our <a href="https://acmus-mir.github.io/acmus-mir/">website</a> and our zenodo <a href="https://zenodo.org/record/3268961#.XShkTo8RVPZ">repository</a>.</p> <p> </p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Consensus QSAR models estimating acute aquatic toxicity for three trophic levels organisms: Algae, Daphnia and Fish
<p>We report new consensus models estimating acute toxicity for algae, daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSE<sub>ext</sub> = 0.56 – 0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/<em>ColorAtom</em> utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracies and data coverage.</p> <p>Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending applicability domain and relevance for application in an industrial context. Generate models and collected public data are made freely available.</p> <p><strong>Available fields in the SDF file:</strong></p> <ul> <li>SMILES_Canonical: canonical SMILES code</li> <li>DB: source of the data, "Litterature set" means that the data is originated from an article (see the companion article of the dataset for details).</li> <li>endpoint: organism for which endpoint is available</li> <li>CASRN: CAS registration number</li> <li>98-81-7</li> <li>pEC50 - DAPHNIA: Daphnia, mortality, which is evaluated by the immobilization of the invertebrate is recorded at 48 hours and expressed as the log median effective concentration (pEC50)</li> <li>mg/L - DAPHNIA: Daphnia, mortality, which is evaluated by the immobilization of the invertebrate is recorded at 48 hours and expressed as the median effective concentration (EC50)</li> <li>pLC50 - FISH: Fish, the log median lethal concentration measured at 96 hours is considered (pLC50)</li> <li>mg/L - FISH: Fish, the log median lethal concentration measured at 96 hours is considered (LC50)</li> <li>pEC50 - ALGA: Algae, the purpose is to determine the substance’s growth inhibition effect, expressed as the log median effective concentration (pEC50) measured at 72 hours</li> <li>mg/L - ALGA: Algae, the purpose is to determine the substance’s growth inhibition effect, expressed as the median effective concentration (EC50) measured at 72 hours</li> </ul>
Remaining useful life estimation of bearings: Meta-analysis of Experimental Procedure
<p>The file <strong>analysis.xlsx</strong> contains the data and statistics obtained from a survey that analyzed the machine learning procedures used for estimating the remaining useful life (RUL) of bearings. The goal is to evaluate the extent to which proper protocol is adhered to for RUL estimation in the domain of predictive maintenance. We surveyed 3 knowledge bases with keywords targeting this specific field, sampled the research and recorded the current practices. Below we present the details of the various spreadsheets where the collected data is registered and analyzed.</p>
Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3.0T
<p>3D MR Fingerprinting T1/T2/M0 maps of twelve healthy volunteers obtained in eight different sites (1.5T and 3.0T scanners, single vendor). Each subject/site dataset includes two acquisitions (test-retest) to assess repeatability of the measurement.</p>
Figure 2 in First record of a non-pollinating fig wasp (Hymenoptera: Sycophaginae) from Dominican amber, with estimation of the size of its host figs
Figure 2. Linear regression between Idarnes carme sp. gp ovipositor sheath lengths and median host fig diameters. Lines represent fitted model (red) and fig diameter ±95% CI (blue) for a 1.2 mm ovipositor sheath length, which represents the ovipositor sheath length of I. thanatos sp. nov. Each point represents a different fig wasp species and the fig diameter of its host species.
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
Data from: Estimation of regional annual abundance and evidence for increasing numbers of white sharks off central California
<p>Raw data consisting of individual identification photographs of white sharks (Carcharodon carcharias) and data table with corresponding metadata. These data support PhD thesis of Paul E. Kanive entitled "VITAL RATES, ANNUAL ABUNDANCE, AND MOVEMENT OF WHITE SHARKS IN THE NORTHEASTERN PACIFIC" and peer-reviewed manuscript "Estimation of regional annual abundance and evidence for increasing numbers of white sharks off central California."</p>
Flux estimates for the Fermi map (Bayesian GCNN vs. NPTFit)
<p>Prediction of the Bayesian graph-convolutional neural network (GCNN) for the Fermi photon-count map. The shaded regions show the predictive (aleatoric and epistemic summed in quadrature) 1σ uncertainty. The markers with error bars (68% credible intervals) indicate the Non-Poissonian template fit (NPTFit) estimates for comparison. The NN predictions for the GCE flux in the Fermi map are similar in magnitude to those of NPTFit, but the GCE is almost entirely attributed to the smooth dark matter template.</p> <p>To view the plot, open it with a browser such as Google Chrome or Firefox. Templates can be (de-)activated by clicking on the colored rectangles. For zooming and panning, click on the buttons in the lower left corner.</p>
Catchment-scale estimates of Amazon evapotranspiration
<p>Amazon basin-mean monthly estimates of evapotranspiration estimated from catchment-balance analysis, satellites (MODIS, P-LSH and GLEAM), reanalysis (ERA5) and the CMIP5 and CMIP6 climate models. Catchment level estimates of climate variables that influence ET are also included (precipitation, radiation and leaf area index). Full details of all source datasets are provided in 'Evapotranspiration in the Amazon: spatial patterns, seasonality and recent trends in observations, reanalysis and CMIP models' in Hydrology and Earth System Sciences, <a href="https://doi.org/10.5194/hess-2020-523">https://doi.org/10.5194/hess-2020-523</a>.</p>
FIA Benchmark Biomass Estimates for sub-regions of the United States
<p>This attached GIS (available in both geodatabase and kml formats) contains all of the hexagon-level estimates described by Menlove and Healey's Technical Note: "A comprehensive forest biomass dataset for the US allows customized validation of remotely sensed biomass estimates." Version 1.2: 28 November, 2020</p>
Grouping strategies in number estimation extend the subitizing range
<p>In the calculation folder: each file contains a matrix called “MATR”. Each row of the matrix “MATR” is a trial. </p> <p>The columns contain the following information:</p> <p>1<sup>st</sup>: Number of trial</p> <p>2<sup>nd: Subject response</sup></p> <p>4<sup>th</sup>: Response time</p> <p>5<sup>th</sup>: first number </p> <p>6<sup>th</sup>: math symbol (1=*; 2= +; 3= –)</p> <p>7<sup>th</sup>: second number </p> <p>8<sup>th</sup>: third number </p> <p> </p> <p>In the calculation folder: each file contains a matrix called “matr”. Each row of the matrix “matr” is a trial. </p> <p>The columns contain the following information:</p> <p>1<sup>st</sup>: subject response in the numerosity task</p> <p>2<sup>nd</sup>: the presented numerosity </p> <p>3<sup>rd</sup>: subject response in the numerosity task</p> <p>4<sup>th</sup>: zero</p> <p>5<sup>th</sup>: stimulus duration</p> <p>6<sup>th</sup>: Response time in the numerosity task</p> <p>7<sup>th</sup>: Grouped (1) or random (2) presentation </p> <p>8<sup>th</sup>: 1</p> <p>9<sup>th</sup>: 1</p> <p>10<sup>th</sup>: Number of items of the upper-left quadrant</p> <p>11<sup>th</sup>: Number of items of the lower-left quadrant</p> <p>12<sup>th</sup>: Number of items of the upper-right quadrant</p> <p>13<sup>th</sup>: Number of items of the lower - right quadrant</p> <p>14<sup>th</sup>: odd shape presented (1=diamond; 2=triangle; 3=circle)</p> <p>15<sup>th</sup>: subject response in the shape task</p> <p>16<sup>th</sup>: 0.2 in the single task response time in the shape task when dual task</p> <p>17<sup>th</sup>: single (0) or dual (1) task</p> <p>18<sup>th</sup>: time stimulus on</p> <p>19<sup>th</sup>: time stimulus off</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
The IDOL (Inertial Deep Orientation-estimation and Localization) Dataset
<p><strong>The IDOL Dataset</strong><br> The IDOL (Inertial Deep Orientation-estimation and Localization) dataset consists of 20+ hours of pedestrian walking IMU data in indoor environments. Data was collected in 3 different buildings from 15 different users of varying body types (not all users are present in each building set). Data collection procedures were approved by an IRB.</p> <p>Data was collected using a LiDAR-Visual-Inertial SLAM system (Kaarta Stencil) as ground truth, with the an iPhone 8 rigidly attached with the Stencil rig. The rig's ground truth position and orientation are recorded, along with IMU readings from both the Stencil internal XSens IMU (gyroscope, accelerometer) and the iPhone IMU (gyroscope, accelerometer, magnetometer). Because both systems were rigidly mounted to each other, the gyroscopic readings are identical (minus a reference frame transformation). However, as there is an offset in position between the Stencil and phone IMU, accelerometer readings differ slightly due the additional lever arm. The offset between the phone's true position and the Stencil's origin was less than the size of the phone, which we empirically determined (using a Vicon mocap studio) to be within the error margin of the Stencil estimate. All readings are sampled at 100Hz.</p> <p><strong>Data subsets</strong><br> At the root level, the dataset has been divided into 3 buildings. The recorded trajectories for each building dataset are grouped into two subsets: "known" and "unknown". The dataset for building 1 also contains a subset entitled "train" because cross-subject performance was evaluated in that building. All "known" and "train" set users are from the same common pool of users, which is disjoint from the set of users present in all the `unknown` trajectories. This allows for testing the generalization of networks across users. These were the splits we used for evaluation of results in the paper, but these can be re-split arbitrarily, as we include subject IDs for each trajectory.</p> <p>Each data subset has a "metadata.json" file, with information about each trajectory file in the subset. This information includes the subject ID for the trajectory and whether or not and when during the run a calibration was performed.</p> <p><strong>Trajectory calibration</strong><br> Calibrations involved rotating the data collection rig along each axis at either the start or end of the trajectory. This allows the magnetometer readings to be calibrated. This was not performed for all trajectories, especially if they were collected back-to-back in the same location, as there is minimal variation in magnetic readings. These can be omitted by truncating a few seconds of data at the relevant parts of the trajectory, although for our evaluation we left these sections in, as there was negligible impact on performance.</p> <p>Each trajectory starts and ends in roughly the same location in each building. Each also starts and ends with the user quickly jostling the data collection rig in the air, generating a "synchronization spike" in the IMU data. This spike is used to perform time alignment of the iPhone and Stencil data, as we find this approach is slightly more accurate than relying on ntp-synchronized timestamps between devices due to the high sample rate. This synchronization has already been performed in the published data files. These artifacts can be omitted using a peak detection algorithm or simply truncating the beginning/end of a trajectory, although we leave them in the dataset because they have negligible impact on results.</p> <p><strong>Trajectory global alignment</strong><br> Trajectories in this dataset were aligned to a global map after being collected, so orientations and positions in one building are globally correct relative to trajectories in other buildings, minus a static positional (x,y) offset (i.e. the trajectories in all buildings begin at (0,0)). The initial pose of trajectories in Building 1 is set as the common origin. In order to recover the data as originally collected, a counter-clockwise rotation in the (x,y) plane must be applied to the position and orientation data in Buildings 2 and 3. This rotation offset is:</p> <pre><code class="language-markdown">Building | Rotation Offset (radians) --- | --- Building 2 | 1.8510 Building 3 | 0.2822</code></pre> <p><br> <strong>Reading the data</strong><br> Each trajectory is stored as a `.feather` file, encoded via Apache Arrow. Python's `pandas` library can be used to read these files as DataFrames using the following:</p> <pre><code class="language-python">import pandas as pd df = pd.read_feather("path/to/feather/file")</code></pre> <p> </p> <p>Each trajectory file contains the following data columns:</p> <pre><code class="language-markdown">Column Name | Data Description --- | --- timestamp | Time of data point in seconds orient[W,X,Y,Z] | Ground truth orientation from Stencil as a quaternion processedPos[X,Y,Z] | Ground truth position from Stencil, smoothed to remove artifacts. This was used as ground truth in our work iphoneOrient[W,X,Y,Z]| iPhone CoreMotion API estimate of device orientation as a quaternion iphoneAcc[X,Y,Z] | Raw acceleration reported by iPhone IMU iphoneGyro[X,Y,Z] | Raw angular velocity reported by iPhone IMU iphoneMag[X,Y,Z] | Magnetometer values reported by iPhone IMU stencilAcc[X,Y,Z] | Raw acceleration reported by Stencil IMU stencilGyro[X,Y,Z] | Raw angular velocity reported by Stencil IMU</code></pre> <p>Note the iPhone and Stencil IMU raw measurements are not in the same reference frames. The Stencil IMU reference frame and ground truth reference frame are the same. The frames are roughly mapped as Stencil +x -> iPhone -x, Stencil +y -> iPhone -y, Stencil +z -> iPhone +z in right-handed coordinate frames.</p> <p><strong>Citation</strong></p> <p>If you use this dataset or part of it, please cite our paper to appear in AAAI 2021:</p> <blockquote> <p>Scott Sun, Dennis Melamed, Kris Kitani. "IDOL: Inertial Deep Orientation-estimation and Localization". AAAI 2021 (in press).</p> </blockquote>
Global agricultural land use scenarios for estimating the potential of forest regeneration for climate mitigation to 2050
<p>The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use.</p> <p>The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions.</p> <p>The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)".</p> <p>Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information.</p> <p>This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.</p>
High-resolution CONUS-wide downscaled rainfall estimates (HRCDRE)
<p>The spatiotemporal character of rainfall is particularly important for hydrologic modeling, as well as hydroclimatic risk estimation and impact assessment. Existing atmospheric reanalysis datasets offer extensive record lengths and global coverage, but usually their spatial resolution is coarse for distributed hydrologic simulations at small spatial scales. On the other hand, the temporal coverage of high-resolution radar-based rainfall estimates can be rather short for risk applications. To address these shortcomings, we simultaneously bias-correct and downscale a state-of-the-art atmospheric reanalysis (<a href="https://doi.org/10.24381/cds.adbb2d47">ERA5</a>) rainfall dataset, using the radar-based <a href="https://doi.org/10.5065/D6PG1QDD">Stage IV</a> precipitation product as fine resolution reference, to develop an hourly CONUS-wide precipitation product over a 4-km grid, which extends back to 1979. In this regard, we refine an existing parametric quantile mapping framework based on a two-component theoretical distribution model, where we impose continuity of the parametric forms via optimal threshold selection to transition between higher and lower rain rates. An evaluation over the probability frequency and time domains, using <a href="https://doi.org/10.25921/p7j8-2170">NOAA's raingauge</a> measurements as benchmark, reveals that the developed product benefits from the strengths of the calibration datasets, demonstrating good performance and robust behavior over all studied time periods and Köppen climate classification zones, including snow-prone regions or areas where mesoscale convective systems become dominant. The accuracy of the yielded high spatial-resolution rain rates, especially in low probability events, shows that the developed product can be effectively used for hydroclimatic risk applications and frequency analysis, while its high temporal and spatial resolution makes it particularly useful for distributed hydrologic modeling.</p>
Dataset for "Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?"
<p>Datasets belonging to the paper "Long-term extreme response of an offshore turbine: How accurate are<br> contour-based estimates?" by Haselsteiner, Frieling, Mackay, Sander and Thoben.</p> <p>Available are:<br> * A 1000-year time series of hourly environmental conditions<br> * 516 1-hour time series of the mudline overturning moment, simulated using openFAST</p> <p> </p>
Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic - Estimates for Local Authority Districts
<p><strong>Overview:</strong></p> <p>Population estimates from the publication: <em>Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic.</em> </p> <p>Population estimates were aggregated to Local Authority Districts (LADs). </p> <p><strong>Methodology: </strong></p> <p>Population estimates were extracted from Bing Tiles (Zoom Level 12) to 2019 LADs by assigning tiles to LADs by their percent areal overlap. This method assumes constant population distribution across a single Bing Tile.</p> <p>2019 LAD boundaries are available from the <a href="https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-bfc/explore">UK Government Open Geography Portal</a>.</p> <p> </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.