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.
9,882
datasets available to search
ShareScore release 0.9.0
Dataset results
9,882 results for “scales”
Subgrid-scale effects in cloud-like atmospheric flows: Colliding thermals - volume 2
<p>Datasets to perform surrogate modeling of subgrid-scale effects in cloud-like atmospheric flows. Please see README.md file for detailed description and metadata.</p>
BirdVox-296h: a large-scale dataset for detection and classification of flight calls
<p>BirdVox 296 hours dataset (BirdVox-296h)<br> ====================================</p> <p>Version 2.1, May 2022.</p> <p><br> Created By<br> ----------</p> <p>Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4)</p> <p>(1): Cornell Lab of Ornithology (CLO)<br> (2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS<br> (3): Adobe Research<br> (4): New York University</p> <p>https://wp.nyu.edu/birdvox<br> <br> </p> <p>Description<br> ---------------</p> <p>The BirdVox-296h dataset contains 148 audio recordings, each two hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured by nine different sensors, originally numbered 1, 2, 3, 4, 5, 6, 7, 8, and 10.<br> <br> Ornithologist Andrew Farnsworth used the Raven software to pinpoint and label every avian flight call in time and frequency. He found 26138 sound events, of which 21546 are flight calls from Passeriformes. Of those, 13385 are identifiable in terms of family, and 8669 are identifiable in terms of both family and species. The annotation process took over 600 hours.</p> <p>The dataset can be used, among other things, for the research, development and testing of machine listening models for bird migration monitoring.</p> <p> </p> <p>Data Files<br> ------------</p> <p>The BirdVox-296h_wav folder contains 148 recordings as WAV files, sampled at 24 kHz, with a single channel (mono). Each recording lasts exactly two hours and is named according to the following format:</p> <p>YYYY-MM-DD_hh-mm-ss_unitUU.wav</p> <p>Where Y means Year, M means Month, D means Day, h means hour, m means minute, and s means second. This date format corresponds to the start time of the recording file, expressed in Coordinated Universal Time (UTC).</p> <p>The field UU contains two digits corresponding to the identifier of the autonomous recording unit (i.e., bioacoustic sensor). UU is either equal to 01, 02, 03, 04, 05, 06, 07, 08, or 10. Note that 09 is absent from the list because sensor 09 failed during the acquisition campaign.</p> <p> </p> <p>Metadata Files<br> -------------------</p> <p>The BirdVox-296h_csv-annotations folder contains CSV files, one for each audio file. The columns of each CSV file are:</p> <p>ID,Time (s),Frequency (Hz),Taxonomy Code,Fine Label,Medium Label,Coarse Label</p> <p><br> "Taxonomy Code" is compliant with the BirdVoxClassify software: github.com/BirdVox/BirdVoxClassify</p> <p>"Fine Label", "Medium Label", and "Coarse Label" most often correspond to species, family and order respectively.</p> <p> </p> <p>The BirdVox-296h_gps-coordinates.csv file contains the approximate GPS coordinates of the sensors (latitudes and longitudes rounded to 2 decimal points) of all nine sensors.</p> <p> </p> <p> </p> <p>Conditions of Use<br> -----------------</p> <p>Dataset created by Andrew Farnsworth, Steve Kelling, Vincent Lostanlen, Justin Salamon, Aurora Cramer, and Juan Pablo Bello.</p> <p>The BirdVox-full-night dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> https://creativecommons.org/licenses/by/4.0/</p> <p>The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, Cornell Lab of Ornithology is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the BirdVox-full-night dataset or any part of it.</p> <p> </p> <p>Feedback<br> -------------</p> <p>Please help us improve BirdVox-296h by sending your feedback to:<br> vincent.lostanlen@ls2n.fr and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p>Acknowledgements<br> --------------------------</p> <p>Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes.</p> <p>We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation, which is part of the Haudenosaunee (Iroquois) confederacy.</p>
Dataset for: Multi-scale approach to biodiversity proxies of biological control service in European farmlands
<p>Dataset for the BiodivERsA COFUND Woodned project. Information on which spatio-temporal factors are simultaneously affecting crop pests and their natural enemies is required to improve conservation biological control practices. The study was conducted in 80 winter wheat crop fields distributed in three regions of North-western Europe (Brittany, Hauts-de-France and Wallonia), along intra-regional gradients of landscape complexity. Five taxa : aphids, slugs, spiders, carabids, and parasitoids were sampled for two consecutive years. We analysed the influence of regional, landscape and local factors on the abundance and species richness of crop-dwelling organisms, as proxies of the service/disservice they provide. Firstly, there was higher biocontrol potential in areas with mild winter climatic conditions. Secondly, natural enemy communities were less diverse and had lower abundances in landscapes with high crop and wooded continuities, contrary to slugs and aphids. Finally, field boundaries with grass strips were more favourable to spiders and carabids than boundaries formed by hedges, while the opposite was found for crop pests, with the latter being less abundant towards the centre of the fields. These results are quite unexpected because they show that hedgerows and woodlots should not be the unique cornerstones of agro-ecological landscape design strategies. We point out that combining woody and grassy habitats to take full advantage of the features and ecosystem services they both provide may promote sustainable agricultural ecosystems. It may be possible to both reduce pest pressure and promote natural enemies by accounting for taxa-specific antagonistic responses to multi-scale environmental characteristics.</p>
TBGA: A Large-Scale Gene-Disease Association Dataset for Biomedical Relation Extraction
<p>This repository contains the TBGA dataset. TBGA is a large-scale, semi-automatically annotated dataset for Gene-Disease Association (GDA) extraction. The dataset consists of three text files, corresponding to train, validation, and test sets, plus an additional JSON file containing the mapping between relation names and IDs. Each record in train, validation, or test files corresponds to a single GDA extracted from a sentence. Records are represented as JSON objects with the following structure:</p> <ul> <li><strong>text:</strong> sentence from which the GDA was extracted.</li> <li><strong>relation:</strong> relation name associated with the given GDA.</li> <li><strong>h: </strong>JSON object representing the gene entity, composed of: <ul> <li><strong>id: </strong>NCBI Entrez ID associated with the gene entity.</li> <li><strong>name:</strong> NCBI official gene symbol associated with the gene entity.</li> <li><strong>pos: </strong>list consisting of starting position and length of the gene mention within text.</li> </ul> </li> <li><strong>t:</strong> JSON object representing the disease entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated with the disease entity.</li> <li><strong>name:</strong> UMLS preferred term associated with the disease entity.</li> <li><strong>pos:</strong> list consisting of starting position and length of the disease mention within text.</li> </ul> </li> </ul> <p>TBGA contains over 200,000 instances and 100,000 bags.<br> The zip file consists of one folder, named TBGA, containing the files corresponding to the dataset.</p> <p>If you use or extend our work, please cite the following: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04646-6#citeas<br> TBGA paper can be found at: <a href="https://rdcu.be/cKkY2">https://rdcu.be/cKkY2</a><br> TBGA code is available at: https://github.com/GDAMining/gda-extraction</p>
Multi-scale simulations for optimizing cancer treatment
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values (10.1101/2021.12.17.473136). The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to 425 different simulations launched and automatically tagged as Interesting/Non-Interesting based on the effect of the parameters on the simulation (see README file). Each simulation was tagged by them with the following parameters (in that order):</p> <ul> <li>oxygen_necrotic, oxygen_critical</li> <li>oxygen_no_proliferation</li> <li>oxygen_reference</li> <li>initial_uptake_rate</li> <li>protein_threshold</li> <li>secretion_rate</li> <li>oxygen_concentration</li> <li>tnf_concentration</li> </ul> <p>Details on how these files are built can be found in the <strong>Biological Use Case</strong> output format file (<a href="https://zenodo.org/record/3921049">https://zenodo.org/record/3921049</a>). </p>
Supplementary material for "High semi-natural vegetation cover and heterogeneity of field sizes promote bird beta-diversity at larger scales in Ethiopian Highlands"
<p><strong>Abstract</strong></p> <ol> <li>The intensification of farming practices exerts detrimental effects on biodiversity. Most research has focused on declines in species richness at local scales (alpha-diversity) although species loss is exacerbated by biotic homogenization that operates at larger scales (i.e., affecting beta-diversity). The majority of studies have been conducted in temperate, industrialized countries while tropical areas remain poorly studied. Agricultural landscapes of sub-Saharan Africa are still largely dominated by small-scale subsistence farming, but strenuous efforts to intensify farming practices are currently spreading to meet a growing food demand. It is therefore crucial to understand how these intensified practices affect biodiversity to mitigate their negative impacts. </li> <li>We investigated how farming system (small- vs large-scale farming) and landscape complexity (semi-natural vegetation cover) drive bird species composition, community turnover, and beta-diversity patterns in Ethiopian Highlands’ agroecosystems. We evaluated the following hypotheses: (1) large-scale farming homogenizes bird communities, (2) community turnover is higher in small-scale farms, (3) interactive effects between landscape complexity and farming systems shape avian communities, (4) heterogeneity of field sizes increases community turnover at larger scales. </li> <li>Bird communities underwent greater compositional changes along the landscape complexity than along the agricultural intensity gradient. Contrary to our expectations, beta-diversity was not significantly lower within large-scale farms (no biotic homogenization), and complex landscapes that still offer a high amount of semi-natural vegetation promoted community turnover in both farming systems. </li> <li>Semi-natural vegetation cover mediated how avian communities responded to agricultural intensification: the compositional differences between small- and large-scale farms increased with vegetation cover, further promoting avian community heterogeneity at the landscape level.</li> <li>The heterogeneity in field sizes also enhanced bird community turnover, suggesting that a combination of both small- and large-scale farming systems within a given landscape unit would promote beta-diversity at larger scales, provided large-scale farms do not become dominant.</li> <li>Synthesis and applications: Landscape complexity shaped avian communities to a stronger degree than farming intensity, emphasizing the importance of semi-natural vegetation and landscape heterogeneity for the maintenance of diverse bird communities and for achieving multifunctional landscapes promoting biodiversity and associated ecosystem services on the High Ethiopian plateaus. <br> </li> </ol>
Periodic Hydraulic Testing Dataset for "Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)"
<p>This dataset is associated with the SNSF-SPARK project “Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)”. Please read the ReadMe file for more information.</p>
MOBDrone: a large-scale drone-view dataset for man overboard detection
<p><strong>Dataset</strong></p> <p>The <em>Man OverBoard Drone (MOBDrone)</em> dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- <em>person, boat, lifebuoy, surfboard, wood</em>. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued.</p> <p>In this repository, we provide:</p> <ul> <li> <p>66 Full HD video clips (total size: 5.5 GB) </p> </li> <li> <p>126,170 images extracted from the videos at a rate of 30 FPS (total size: 243 GB)</p> </li> <li> <p>3 annotation files for the extracted images that follow the MS COCO data format (for more info see <a href="https://cocodataset.org/#format-data">https://cocodataset.org/#format-data</a>):</p> <ul> <li> <p><em>annotations_5_custom_classes.json</em>: this file contains annotations concerning all five categories; please note that class ids do not correspond with the ones provided by the MS COCO standard since we account for two new classes not previously considered in the MS COCO dataset --- <em>lifebuoy </em>and <em>wood</em></p> </li> <li> <p><em>annotations_3_coco_classes.json:</em> this file contains annotations concerning the three classes also accounted by the MS COCO dataset --- <em>person, boat, surfboard</em>. Class ids correspond with the ones provided by the MS COCO standard.</p> </li> <li> <p><em>annotations_person_coco_classes.json</em>: this file contains annotations concerning only the '<em>person</em>' class. Class id corresponds to the one provided by the MS COCO standard.</p> </li> </ul> </li> </ul> <p>The MOBDrone dataset is intended as a test data benchmark. However, for researchers interested in using our data also for training purposes, we provide training and test splits:</p> <ul> <li><em>Test set: </em>All the images whose filename starts with "DJI_0804" (total: 37,604 images)</li> <li><em>Training set:</em> All the images whose filename starts with "DJI_0915" (total: 88,568 images)</li> </ul> <p>More details about data generation and the evaluation protocol can be found at our MOBDrone paper: <a href="https://arxiv.org/abs/2203.07973">https://arxiv.org/abs/2203.07973</a><br> The code to reproduce our results is available at this GitHub Repository: <a href="https://github.com/ciampluca/MOBDrone_eval">https://github.com/ciampluca/MOBDrone_eval</a><br> See also <strong> </strong><a href="http://aimh.isti.cnr.it/dataset/MOBDrone">http://aimh.isti.cnr.it/dataset/MOBDrone</a></p> <p><strong>Citing the MOBDrone</strong></p> <p>The MOBDrone is released under a Creative Commons Attribution license, so please cite the MOBDrone if it is used in your work in any form.<br> Published academic papers should use the academic paper citation for our MOBDrone paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people</p> <blockquote> <pre>@inproceedings{MOBDrone2021, title={MOBDrone: a Drone Video Dataset for Man OverBoard Rescue}, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi}, booktitle={ICIAP2021: 21th International Conference on Image Analysis and Processing}, year={2021} } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{donato_cafarelli_2022_5996890, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi}, title = {{MOBDrone: a large-scale drone-view dataset for man overboard detection}}, month = feb, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.5996890}, url = {https://doi.org/10.5281/zenodo.5996890} }</pre> </blockquote> <p>Personal works, such as machine learning projects/blog posts, should provide a URL to the MOBDrone Zenodo page (<a href="https://doi.org/10.5281/zenodo.5996890">https://doi.org/10.5281/zenodo.5996890</a>), though a reference to our MOBDrone paper would also be appreciated.</p> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the MOBDrone or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">mobdrone[at]isti.cnr.it</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was partially supported by NAUSICAA - "NAUtical Safety by means of Integrated Computer-Assistance Appliances 4.0" project funded by the Tuscany region (CUP D44E20003410009). The data collection was carried out with the collaboration of the Fly&Sense Service of the CNR of Pisa - for the flight operations of remotely piloted aerial systems - and of the Institute of Clinical Physiology (IFC) of the CNR - for the water immersion operations. </p>
Language intensity scale
<p>This is an index comprised by 3 subindexs and a global index (which is the result of a combination of the other three). These are:<br> LC Index: Linguistic Competences<br> CA Index: Communication Skills<br> IA Index: Information-processing Abilities<br> ILT/LIO Index: Linguistic Intensity of Occupations<br> To elaborate the different indexes, the information contained in the O*Net database (corresponding to the USA of the year 2015) has been used, from which those variables (taken as items) directly related to each one have been considered of the first three indices (CL, HTI, HSC) considered as reflective constructs. In contrast, the ILT index has been constructed as a second order construct, of a formative nature, based on the factorial scores of the first three indices. For the validation of the measurement scales and the obtaining of the factorial scores of the different indexes, a statistical model of structural equations has been used, specifically based on the technique of the variances (PLS). The factorial scores obtained for each dimension, both first order (CL, HTI, HSC) and second order (ILT), have been transformed into a range of variation in the 0-100 range, to obtain the values For each occupation of the different indexes.</p>
Literacy scales
<p>This is a measure of literacy needs by occupations. It measure literacy according to the deffinition made by the OECD (2010). In that sense, it includes linguistic skills (reading, writting and speaking) but also numeracy and ICT skills. More in detail, 32 Likert-type questions from the Survey of Adult Skill were used. (8 questions about the frequency of use of reading at work , 4 questions about writing , 7 questions about speech which are titled influence and it is considered a general skill, 6 questions about numeracy and 7 questions about the use of ICT). The measures quantify the intensity of use for each of the five kinds of skills literacy linguistic skill in an occupation based on quantile distributions. A global indicator of literacy is also provided as a simple average of the other 5 skills.</p>
A Large-scale Dataset of (Open Source) License Text Variants
<p>We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers.<br> The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing.<br> Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared.<br> The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.</p> <p>For more details see the included README file and companion paper:</p> <ul> <li>Stefano Zacchiroli. <a href="https://doi.org/10.1145/3524842.3528491"><em>A Large-scale Dataset of (Open Source) License Text Variants</em></a>. In proceedings of the <a href="https://conf.researchr.org/home/msr-2022">2022 Mining Software Repositories Conference (MSR 2022)</a>. 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.</li> </ul> <p>If you use this dataset for research purposes, please acknowledge its use by citing the above paper.</p> <ul> </ul>
Scale Soft Sensor for steel semi-products [CSS4]
<p> </p> <p>During production of steel bars in hot rolling mills there is a formation of scale on the surface of the products. It is differentiated between two scale types: primary scale and secondary scale. During the reheating of the product in the furnace the scale is called primary scale. After reheating and before rolling the scale is removed by a descaler, for instance with high water pressure. After rolling, while the product is located on the cooling bed, the secondary scale grows up.</p>
LARD: Large-scale Artificial Disfluency Generation
<p>This dataset contains 95,992 examples of utterances with 71,994 artificial inserted disfluencies using the LARD method. We use the <a href="https://arxiv.org/pdf/1801.04871.pdf">Schema-Guided Dialogue (SGD) </a>dataset as a base to construct the synthetic disfluencies. The LARD dataset contains three different types of disfluencies: repetitions, replacements, and restarts. </p>
Litter decomposition is moderated by scale-dependent microenvironmental variation in tundra ecosystems
<p><strong>QHI_crop.tiff </strong>= We carried out topographic surveys using unoccupied aerial vehicles photogrammetry in August 2017. We used three UAV platforms to collect RGB multispectral data at a fine (3 cm) spatial resolution: DJI Phantom 4 Pro and Advanced (multicopter), and Phantom FX-61 (fixed wing), and used used structure from motion with multiview steriopsis to obtain a fine-grain 10 cm spatial resolution digital surface model and orthomosaic as described in Cunliffe et al. (2019a, 2019b).</p> <p><strong>thermsum.tif </strong>= We used the microclima package in R (Kearney et al., 2020; Maclean et al., 2019) to model surface air temperature at a 1-m spatial grain. Using our fine resolution DSM, we modelled mean surface temperatures at the study site for each day spanning the teabag burial period of 13th July to 9th August 2017. The microclima model incorporates local daily climate, radiation, cloud cover and coastal exposure data from gridded global datasets derived from RCNEP (<a href="https://www.zotero.org/google-docs/?broken=Zl6wgI">Kemp et al., 2012)</a>. We summed the 28 TIF files produced through this modelling technique to produce a 28-day thermal sum variable - a metric which captures the overall heating of the ground surface over the course of the experiment.</p> <p><strong>Cited Works:</strong></p> <p> </p> <p>Cunliffe, A., I. Myers-Smith. J. Kerby and W. Palmer (2019a). Orthomosaic of permafrost landscape on Qikiqtaruk – Herschel Island, Yukon, Canada: August 2017. NERC Polar Data Centre. DOI:10.5285/29bf1c9f-a39a-452c-b9f9-de35d9fb9179.</p> <p> </p> <p>Cunliffe, A., G. Tanski, B. Radosavljevic, W. Palmer, T. Sachs, H. Lantuit, J. Kerby, and I. Myers-Smith (2019b) Rapid retreat of permafrost coastline observed with aerial drone photogrammetry. The Cryosphere 13(5):1513-1528. DOI: 10.5194/tc-13-1513-2019.</p> <p> </p> <p><a href="https://www.zotero.org/google-docs/?hjdBYY">Maclean, I. M. (2020). Predicting future climate at high spatial and temporal resolution. <em>Global Change Biology</em>, <em>26</em>(2), 1003–1011.</a></p> <p> </p> <p>Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. (2020). A method for computing hourly, historical, terrain‐corrected microclimate anywhere on Earth. <em>Methods in Ecology and Evolution</em>, <em>11</em>(1), 38-43.</p> <p> </p> <p>Kemp, M. U., Van Loon, E. E., Shamoun-Baranes, J., & Bouten, W. (2012). RNCEP: global weather and climate data at your fingertips. <em>Methods in Ecology & Evolution</em>, <em>3</em>(1), 65-70.</p> <p><strong>Paper Abstract:</strong></p> <ol> <li> <p><strong>The Arctic tundra is one of the world’s largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. We currently know very little about landscape-scale controls of litter decomposition in tundra ecosystems, which hinders our understanding of the global carbon cycle. </strong></p> </li> <li> <p><strong>Here, we examined how local-scale topography, surface air temperature, soil moisture and permafrost conditions influenced litter decomposition rates across a heterogeneous tundra landscape on Qikiqtaruk - Herschel Island (Yukon, Canada).</strong></p> </li> <li> <p><strong>We used the Tea Bag Index protocol to derive decomposition metrics which we then compared across environmental gradients, including thermal sum surface temperature data derived from fine-resolution microclimate data modelled from drone derived topographic data.</strong></p> </li> <li> <p><strong>We found greater green tea litter mass loss and faster decomposition rates in wetter and warmer areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness.</strong></p> </li> <li> <p><strong>Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at the landscape-scale (> 10 m) better than surface temperature.</strong></p> </li> <li> <p><strong>Surprisingly, there was no strong control of elevation or slope of litter decomposition. We also found higher decomposition rates on North-facing relative to South-facing aspects at microsites that were wetter rather than warmer.</strong></p> </li> </ol>
Fine-scale population spatialization data of China in 2018 based on real location-based big data
<p><strong>This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).</strong></p>
Video Examples from: Creating Audio Object-focused Acoustic Environments for Room-Scale Virtual Reality
<p>Video recordings illustrating the issues and possible solutions mentioned in the paper.</p> <p>Please use headphones when watching the videos.</p>
Raw data to "Scaling at quantum phase transitions above the upper critical dimension"
<p>This directory contains the data used to generate the numerical results in the work "Scaling at quantum phase transitions above the upper critical dimension[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: A. Langheld et al., Scaling at quantum phase transitions above the upper critical dimension, <a href="https://scipost.org/10.21468/SciPostPhys.13.4.088">SciPost Phys. <strong>13</strong> 088</a>, 2022.</p>
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Sp(2N) Yang-Mills theories on the lattice: scale setting and topology—data release
<p>This release contains all data and metadata used to prepare the publications <a href="https://arxiv.org/abs/2205.09254">Topological susceptibility in Yang-Mills theories</a> and <a href="https://arxiv.org/abs/2205.09364">Sp(2N) Yang-Mills theories on the lattice: scale setting and topology</a>.</p> <p>Included are:</p> <ul> <li>The raw log output from the Wilson flow computation, as well as metadata describing the ensembles used, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate `README.md` describing the layout of the data.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `datapackage.h5`.</li> <li>The data presented in all tables in both papers, in CSV format, as described in more detail below.</li> </ul> <p>Further details are given in the file README.md.</p>
HISTORIAN: a large-scale HISTORIcal film dataset with cinematographic ANnotation
<p>Developing automated tools for sustainable film preservation of extensive historical film collections assumes an understanding of fundamental cinematographic settings. In order to be able to investigate new approaches to detect and classify cinematographic settings, this paper proposes a novel large-scale historical film dataset with cinematographic annotations (HISTORIAN), i.e., shot boundaries, shot types, camera movements. The dataset consists of 98 digitized original analog film reels related to the Second World War and 10593 film shots manually annotated by human film experts. Moreover, annotations for overscan areas such as sprocket holes are included. A baseline film analysis pipeline is introduced and evaluated. To the best of our knowledge, HISTORIAN is the first dataset that covers the challenges and characteristics of historical film documentaries and provides novel possibilities for exploring automatic film analysis tools.</p> <p>This repository presents a tiny set including a few examples for demonstration.</p> <p>A link to the Github repository (including helper scripts and readme) can be found <a href="https://github.com/dahe-cvl/historian_dataset">here</a></p> <p> </p> <p> </p> <p> </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.