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.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
Mud and organic content are strongly correlated with microplastic contamination in a meandering riverbed - Data sets
<p>This is the dataset relating to publication "Microplastics distribution in a meandering riverbed reveasl mud content as a universal normalizer for microplastic contamination in aquatic environments" by Van Daele, M., Van Bastelaere, B., de Clercq, J., Meyer, I., Vercauteren, M. and Asselman, J.</p> <p>It contains the microplastic and sedimentological data that support the findings of that publication, with a seperate file for data obtained from riberbed sediments and the water column. It further contains a file with all source data for the graphs in the figures of the main manuscript and the Supplementary Information<span><span>.</span></span></p>
Collated set of all gravitational wave observations (to current date) from Wikipedia
<p>This is a dataset I needed but couldn't find. So, with the help of Chat GPT (and several hours ot time), I compiled this easy to access html file.</p> <p>It's not perfect but here it is for all of you to enjoy. </p>
Mass mortality among colony-breeding seabirds in the German Wadden Sea in 2022 due to distinct genotypes of HPAIV H5N1 clade 2.3.4.4b: data sets on phylogeographic analyses
<p>Highly pathogenic avian influenza viruses (HPAIV) of clade 2.3.4.4b of the H5 goose/Guangdong (gs/GD) lineage have repeatedly emerged in Germany since 2016. Both poultry holdings and wild birds have been heavily hit but the 2020-2021 and 2021-2022 HPAI winter seasons exceeded all previously recorded epizootics in Germany in terms of number of wild bird cases recorded, genetic diversity of viruses, and duration of virus activity. In past seasons regional massing of wild bird cases were seen at the German coasts of the Baltic and North Sea, but species mainly affected varied from season to season. In 2022 a new and, in Europe, unprecedented aspect was observed when several cormorant and seabird breeding colonies became affected since May at the Baltic Sea coast and in the Wadden Sea, respectively by HPAI H5N1 viruses.</p> <p> </p>
AIT Netflow Data Set
<p><strong>AIT Netflow Data Sets</strong></p> <p>This repository contains labeled synthetic netflows suitable for evaluation of intrusion detection systems, federated learning, and alert aggregation. The netflows are generated from the packet captures contained in the <a href="http://doi.org/10.5281/zenodo.5789064">AIT-LDS-v2.0</a>. A detailed description of that dataset is available in [1]. The packet captures were collected from eight testbeds that were built at the Austrian Institute of Technology (AIT) following the approach by [2]. Please cite these papers if the data is used for academic publications.</p> <p>In brief, each of the datasets corresponds to a testbed representing a small enterprise network including mail server, file share, WordPress server, VPN, firewall, etc. Normal user behavior is simulated to generate background noise over a time span of 4-6 days. At some point, a sequence of attack steps is launched against the network. The following attacks are launched in the network:</p> <ul> <li>Scans (nmap, WPScan, dirb)</li> <li>Webshell upload (CVE-2020-24186)</li> <li>Password cracking (John the Ripper)</li> <li>Privilege escalation</li> <li>Remote command execution</li> <li>Data exfiltration (DNSteal)</li> </ul> <p>This repository contains the following files:</p> <ul> <li><em><testbed>_netflows.zip</em>: CSV files of labeled TCP and UDP netflows for each testbed.</li> <li><em>label_info.txt</em>: File describing which labels in TCP and UDP are benign and which ones are malicious.</li> <li><em>README.md</em>: Instructions on how to reproduce the generation and labeling of the netflows from the <a href="http://doi.org/10.5281/zenodo.5789064">AIT-LDS-v2.0</a>. Note that it is only necessary to run the python scripts if you want to extend or change the labeling procedure.</li> <li><em>1_format_dataset_info.ipynb</em>: Generates the tables necessary for labeling (see README.md).</li> <li><em>2_label_logs.ipynb</em>: Labels the netflows (see README.md).</li> </ul> <p>Acknowledgements: Partially funded by the FFG projects INDICAETING (868306) and DECEPT (873980), and the EU projects GUARD (833456) and PANDORA (SI2.835928).</p> <p><strong>If you use the dataset, please cite the following publications:</strong></p> <p>[1] M. Landauer, F. Skopik, M. Frank, W. Hotwagner, M. Wurzenberger, and A. Rauber. <a href="https://ieeexplore.ieee.org/abstract/document/9866880">"Maintainable Log Datasets for Evaluation of Intrusion Detection Systems"</a>. IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 3466-3482. [<a href="https://arxiv.org/pdf/2203.08580.pdf">PDF</a>]</p> <p>[2] M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner and A. Rauber, <a href="https://ieeexplore.ieee.org/document/9262078">"Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed,"</a> in IEEE Transactions on Reliability, vol. 70, no. 1, pp. 402-415, March 2021, doi: 10.1109/TR.2020.3031317. [<a href="https://www.skopik.at/ait/2020_trel.pdf">PDF</a>]</p>
Data sets for FASTREAD
<p>Three datasets created with information in existing systematic literature review publications.</p> <p>Hall.csv is created according to Hall, et al., "A systematic literature review on fault prediction performance in software engineering", 2012.</p> <p>Wahono.csv is created according to Wahono, et al., "A systematic literature review of software defect prediction: research trends, datasets, methods and frameworks", 2015.</p> <p>Radjenovic.csv is created according to Radjenovic, et al., "Software fault prediction metrics: A systematic literature review", 2013.</p> <p>One dataset (Kitchenham.zip) provided by Barbara Kitchenham (cleaned and summarized by Zhe Yu) from Kitchenhm et al. "Systematic literature reviews in software engineering- A tertiary study", 2010.</p> <p>These datasets are used in our submission to Empirical Software Engineering: "How to Read Less: On the Benefit of Active Learning for Primary Study Selection in Systematic Literature Reviews".</p>
MPS Data set with images of medieval charters for handwriting-style based dating of manuscripts
<pre>The MPS benchmark data set for handwritten manuscript dating ____________________________________________________________ This data set is collected for the Dutch NWO project: Medieval Paleographical Scale (MPS) by Petros Samara Project website: http://application02.target.rug.nl/monk/Projects/MPS/ Copyright (c) Huygensinstituut, Den Haag, 2016 University of Groningen, 2016. All rights reserved. Organisation of the data: Each .tar.gz file contains a number of NetPBM images. The format is chosen because of its simplicity. Also, there is no doubt about lossy compression in the processing chain. The file names are of the format 'MPS<year>_<seqnr>.ppm', for example, 'MPS1300_0056.ppm'. Note: the files are not in a separate directory, they will be extracted in place. However, due to the unique naming, there is no problem extracting them in one single current (destination) directory. The actual type of the image can be gray scale (.pgm) or color (.ppm), in '8-bit DirectClass' according to ImageMagick's 'identify' tool. The images were cropped out of larger photographs because of irrelevant elements such as a Kodak color calibrator and non-text content such as supporting surface (table) backgrounds, seals (emblems), ribbons, etc. No effort has been made to obtain a balanced set of samples over years: the given frequencies of occurrence in archives are used. There is evidently less data in years before 1375 A.D. while some periods provides us with ample data for historical reasons (e.g, 1450 A.D.). It would have been a pity if the scarce years had determined and limited the size of this data set. Selection criteria for data reduction, whether random or systematic, would have been arbitrary. In any case, these images were used in our publications, such that the performance results of future attempts on manuscript dating can be compared with earlier results. The performances that have been reached using our algorithms are in the order of an MAE (mean average error) of 10 years. If you have any questions, please contact us: Sheng He (heshengxgd@gmail.com) Petros Samara (petros.samara@huygens.knaw.nl) Jan Burgers (jan.burgers@huygens.knaw.nl) Lambert Schomaker (L.Schomaker@ai.rug.nl) Please cite our papers if you use this data set: [1] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Image-based historical manuscript dating using contour and stroke fragments. Pattern Recognition(PR), Vol. 59, pp. 159-171, 2016 [2] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Towards style-based dating of historical documents. International Conference on Frontiers in Handwriting Recognition(ICFHR), Crete, Greece, 2014 [3] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization IEEE Trans. on Image Processing, Vol. 25(11), Nov. 2016. http://ieeexplore.ieee.org/document/7551181/</pre> <p>Data are collected thanks to Dutch NWO grant project 380-50-006</p>
Data set and benchmarks from Eriksson et al., ICAPS 2018
<p>These are the benchmarks and the experiment data used in the paper "A Proof System for Unsolvable Planning Tasks" by Eriksson et al. (ICAPS 2018). The raw data contains the logs from all runs, while the eval-directories contain a json file with all parsed attributes. The benchmarks directory contains all benchmarks used in the experiments. Finally, the file eriksson-et-al-icaps2018.html provides an overview over the most interesting attributes across all runs.</p>
ImUnipen image data set for writer identification (N=208) - vectorial handwriting converted to usable images
<p><br> ==============<br> Terms of Usage<br> ==============</p> <p>The ImUnipen data set is intended for non-commercial, scientific use,<br> and is distributed under auspices of the Unipen Foundation.</p> <p>Please always refer to the following paper in IEEE PAMI when using<br> the ImUnipen data set:</p> <p> Bulacu, M.; Schomaker, L.<br> Text-Independent Writer Identification and Verification<br> Using Textural and Allographic Features<br> Pattern Analysis and Machine Intelligence, IEEE Transactions on<br> Volume 29, Issue 4, April 2007 Page(s):701 - 717</p> <p>The ImUnipen data set is derived from the Unipen (unipen.org)<br> data set of on-line (i.e., vectorial, xy) handwriting.<br> The xy-coordinates and a line-generator algorithm are used<br> to generate a raster image, as if the data were optically scanned.</p> <p>Contents: for 208 writers, there are two PNG images per writer of<br> an artificially constructed table of naturally written words (49MByte).<br> These words are pasted onto a white page. For systematics reasons,<br> we call such a page a Paragraph, see below.</p> <p>The file names are organized as (example):</p> <p> Writ990221.Doc01.Par00.png<br> Writ990221.Doc01.Par01.png</p> <p> meaning: writer number 990221, document 01 (there exists only Doc01)<br> and the image with artificial "paragraph" of isolated words "Par00"<br> and "Par01".</p> <p>The Par00 and Pa01 images are typically used as the query<br> and best match in a leave-one-out setting for writer identification.<br> For instance, Par00 is the query, and Par01 is added to the total set<br> of all other images as the attractor for an identification search.</p> <p>For these experiments, word labels are not given in this data set,<br> on purpose, as the goal is to test recognition-free writer identification<br> methods.</p> <p>For a description of the regular<br> Unipen data set, please visit http://unipen.org</p> <p>Lambert Schomaker constructed this set in 2005</p>
Example data set from Diamond Light Source VMXi beamline (Eiger 4M data, NeXus format)
<p>Data set recorded from Thermolysin crystal record <em>in situ</em> with Eiger 4M detector, to demonstrate file format used for this instrument at Diamond Light Source. Processing results using xia2 / DIALS:</p> <p> </p> <pre>For AUTOMATIC/DEFAULT/SAD Overall Low High High resolution limit 1.97 5.35 1.97 Low resolution limit 46.86 46.87 2.01 Completeness 59.8 73.2 6.0 Multiplicity 5.8 8.0 1.1 I/sigma 13.6 24.4 1.8 Rmerge(I) 0.072 0.050 0.308 Rmerge(I+/-) 0.067 0.048 0.000 Rmeas(I) 0.078 0.054 0.436 Rmeas(I+/-) 0.076 0.054 0.000 Rpim(I) 0.028 0.018 0.308 Rpim(I+/-) 0.035 0.023 0.000 CC half 0.997 0.998 0.450 Wilson B factor 13.401 Anomalous completeness 51.3 78.8 0.8 Anomalous multiplicity 3.3 4.8 1.0 Anomalous correlation 0.039 -0.006 0.000 Anomalous slope 0.987 dF/F 0.103 dI/s(dI) 1.086 Total observations 85069 8229 82 Total unique 14747 1031 73 Assuming spacegroup: P 6 2 2 Other likely alternatives are: P 61 2 2 P 65 2 2 P 62 2 2 P 64 2 2 P 63 2 2 Unit cell (with estimated std devs): 93.7184(3) 93.7184(3) 130.864(2) 90.0 90.0 120.0 </pre> <p> </p>
TriGraphSlant - benchmark set for writer identification - writers were asked to write in unnatural slant
<p> <br> Disclaimer and terms of use:<br> ============================<br> <br> /*****************************************************************************\<br> * *<br> * *<br> * This is the TrigraphSlant (Img version) Distribution, release 18/3/2011 * *<br> * *<br> * This distribution contains 188 images of scanned handwritten text, *<br> * scanned at resolution 300dpi Canon LiDE 25, grey scale, *<br> * by 47 Dutch writers, four pages per writer, from four *<br> * writing conditions, one condition per page. The conditions are: *<br> * 1. [AN] Copy text A in your natural handwriting. *<br> * 2. [BN] Copy text B in your natural handwriting. *<br> * 3. [BL] Copy text B and slant your handwriting to the *<br> * left as much as possible. *<br> * 4. [BR] Copy text B and slant your handwriting to the *<br> * right as much as possible. *<br> * The codes AN, BN, BL and BR refer to subsets into which the collected *<br> * pages of the writers were subdivided. AN represents a collection of *<br> * authentic documents; BN, BL and BR can be seen as collections of *<br> * questioned documents. To avoid structural effects of fatigue, the order *<br> * of item 3 and 4 was randomized at each collection: half of the subjects *<br> * wrote the BR page before the BL page. The data were collected at three *<br> * sites, in three cities: The Hague: NFI (N...), Donders Institute for *<br> * Brain, Cognition and Behaviour, Radboud University Nijmegen (D...) *<br> * and the Artificial Intelligence Dept. of University of Groningen (R...) *<br> * *<br> * Copyright The International Unipen Foundation, 2010, All rights reserved *<br> *******************************************************************************<br> * *<br> * *<br> * DISCLAIMER AND COPYRIGHT NOTICE FOR ALL DATA CONTAINED ON THIS CARRIER: *<br> * *<br> * *<br> * 1) PERMISSION IS HEREBY GRANTED TO USE THE DATA FOR RESEARCH *<br> * PURPOSES. IT IS NOT ALLOWED TO DISTRIBUTE THIS DATA FOR COMMERCIAL *<br> * PURPOSES. *<br> * *<br> * *<br> * 2) PROVIDER GIVES NO EXPRESS OR IMPLIED WARRANTY OF ANY KIND AND ANY *<br> * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR PURPOSE ARE *<br> * DISCLAIMED. *<br> * *<br> * 3) PROVIDER SHALL NOT BE LIABLE FOR ANY DIRECT, INDIRECT, SPECIAL, *<br> * INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF ANY USE OF THIS *<br> * DATA. *<br> * *<br> * 4) THE USER SHOULD REFER TO THE FOLLOWING ARTICLE ON THIS DATA SET: *<br> * *<br> * A.A. Brink, R.M.J. Niels, R.A. van Batenburg, C.E. van den Heuvel, *<br> * L.R.B. Schomaker, Towards robust writer verification by correcting *<br> * unnatural slant, Pattern Recognition Letters, Volume 32, Issue 3, *<br> * 1 February 2011, Pages 449-457, ISSN 0167-8655, *<br> * DOI: 10.1016/j.patrec.2010.10.010. *<br> * *<br> * 5) THE RECIPIENT SHOULD REFRAIN FROM PROLIFERATING THE DATA SET TO THIRD *<br> * PARTIES EXTERNAL TO HIS/HER LOCAL RESEARCH GROUP. PLEASE REFER INTERESTED *<br> * RESEARCHERS TO HTTP://UNIPEN.ORG FOR OBTAINING THEIR OWN COPY. *<br> \*****************************************************************************/<br> <br> Abstract<br> <br> Towards robust writer verification by correcting unnatural slant<br> <br> A.A. Brink, , R.M.J. Niels, R.A. van Batenburg, C.E. van den Heuvel, <br> and L.R.B. Schomaker, <br> <br> a Institute of Artificial Intelligence and Cognitive Engineering (ALICE), <br> University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands<br> <br> b Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, <br> P.O. Box 9104, 6500 HE Nijmegen, The Netherlands<br> <br> c Netherlands Forensic Institute, P.O. Box 24044, 2490 AA Den Haag, The Netherlands<br> <br> Received 11 September 2009. Available online 30 October 2010.<br> <br> Slant is a salient feature of Western handwriting and it is considered to be an<br> important writer-specific feature. In disguised handwriting however, slant is<br> often modified. It was tested whether slant is indeed an important factor and it<br> was tested whether the distorting effect of deliberate slant change can be<br> countered by a simple shear transform. This was done in two off-line writer<br> verification experiments in image processing conditions of slant elimination and<br> slant correction. The experiments were performed using three features based on<br> statistical pattern recognition, including the state-of-the-art features<br> Fraglets and Hinge. A new public dataset was created and used, containing<br> natural and slanted handwriting by 47 writers. A striking result is that the<br> average natural slant value is much less important for biometric systems than is<br> usually assumed: eliminating slant yields just a 1-5% performance loss. A<br> second result is that the effects of deliberate slant change cannot be fully<br> countered by a simple shear transform: it raises performance on the distorted<br> handwriting from 53-68% to 64-90%, but this is still lower than normal<br> operation on natural handwriting: 97-100%.<br> <br> Research highlights<br> - The value of slant as a writer identification feature has been overrated. <br> - Deliberate slant change can be partly countered by the shear transform. <br> - Deliberate slant change introduces non-affine distortions to the handwriting. <br> - A new dataset of deliberately slanted handwriting was introduced.<br> <br> Keywords: Handwriting biometrics; Writer verification; Slant; Disguise; Statistical<br> pattern recognition</p> <p> </p>
SSIX BREXIT Twitter Annotated Data Set
<p><strong>SSIX BREXIT Gold Standard</strong></p> <p>This repository contains the BREXIT Twitter Gold Standard produced by the SSIX Project <a href="https://ssix-project.eu/">https://ssix-project.eu/</a>.</p> <p>Only a sample is available here, to rebuild the full dataset, follow the instructions on the SSIX Project code repository: </p> <p><a href="https://bitbucket.org/ssix-project/brexit-gold-standard">https://bitbucket.org/ssix-project/brexit-gold-standard</a></p>
Data set for "Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex"
<p>Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033</p> <p>There are 25 files in this data upload:</p> <p>1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication.</p> <p>2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018.</p> <p>3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018.</p> <p>4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018.</p> <p>5. 'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018.</p> <p>6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press.</p> <p>7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199.</p> <p>8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201.</p> <p>9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202.</p> <p>10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203.</p> <p>11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced.</p> <p>12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced.</p> <p>13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced.</p> <p>14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced.</p> <p>15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced.</p> <p>16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced.</p> <p>17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced.</p> <p>18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced.</p> <p>19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced.</p> <p>20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced.</p> <p>21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced.</p> <p>22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced.</p> <p>23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced.</p> <p>24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced.</p> <p>25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.</p>
Sedimentary charcoal data set from the Villena Paleolake (VL3 core)
<p><strong>1. Data set, and sampling methods</strong></p> <p>This data set contains the quantification of sedimentary charcoal from the VL3 record of the Villena Paleolake (Villena, Alicante, Spain). The analyzed section covers the Early and Middle Holocene section of this deposit according (Jones et al., 2018), depths between -120 a -400 cm, with centimetric sampling interval which has produced a total number of 281 samples.</p> <p><strong>2. Treatment</strong></p> <p>The samples have been treated as follows, adapting the methods published by Rhodes (1998) and Talon <em>et al</em>.(1998):</p> <ul> <li>Samples were soaked in 10% H2O2 for 12h for sediment deflocculation and to bleach non-charcoal organic material.</li> <li>After this first step, it was noted if the samples contained shell fragments.</li> <li>A 10% HCl solution was used in samples with high carbonate content.</li> <li>Then samples were sieved (150µ) under a soft-water jet.</li> <li>The samples were stored in distilled water for later counting.</li> </ul> <p><strong>3. Quantification procedures</strong></p> <ul> <li>Each sample was washed with distilled water, employing a 150 micron sieve.</li> <li>The wet sample was examined under binocular microscope <em>Stereomicroscopy CETI STEDDY-T</em> at 40 magnification, using a reference grid with squares of different size categories (cat. 0,5; cat. 1, cat. 2, cat. 3, cat. 4 and cat. 5). The total number of examined charcoal was also calculated.</li> <li>Each size category corresponds to one of the following area categories Cat. 0,5: 0,015625mm<sup>2</sup>; Cat. 1: 0,0625 mm<sup>2 </sup>(0,25 mm*0,25 mm); Cat. 2: 0,0125 mm<sup>2</sup>; Cat. 3: 0,25 mm<sup>2</sup>; Cat. 4: 0,5625 mm<sup>2</sup>; Cat. 5: 1 mm<sup>2</sup>. In the Excel spreadsheet, the area is multiplied by the number of fragments of each category. The sum of the areas is also calculated.</li> <li>In addition, oocytes and insects were also counted.</li> </ul> <p><strong>References</strong></p> <ul> <li>Carcaillet, C., Bouvier, M., Fréchette, B., Larouche, A. C., Richard, P. J. H. (2001). Comparison of pollen-slide and sieving methods in lacustrine charcoal analyses for local and regional fire history. <em>The Holocene</em> 11 (4): 467- 476.</li> <li>Clark, J. S. (1988). Particle motion and the theory of charcoal analysis: Source area, transport, deposition, and sampling. <em>Quaternary Research</em> 30 (1), 67-80.</li> <li> <p>Jones S.E., Burjachs, F. Ferrer-García C., Giralt, S., Schulte, L., Fernández-López de Pablo, J. (2018). A multi-proxy approach to understanding complex responses of salt-lake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain. Quaternary Science Reviews https://doi.org/10.1016/j.quascirev.2017.12.015.</p> </li> <li>Ohlson, M. and Tryterud, E. 2000. Interpretation of the charcoal record in forest soils: forest fires and their production and deposition of macroscopic charcoal. The Holocene, 10(4), 529-525.</li> <li>Rhodes, A. N. (1998). A method for the preparation and quantification of microscopic charcoal from terrestrial and lacustrine sediment cores. <em>The Holocene </em>8 (1), 113-117.</li> <li>Talon, B., Carcaillet, C., Thinon, M. (1998). Etudes pedoanthracologiques des variations de la limite superieure des arbres au cours de l'Holocene dans les Alpes françaises. <em>Geographie physique et Quaternaire</em> 52 (2), 195-208.</li> </ul>
Carbon sequestration in riparian forests: a global meta-analysis data set
<p>Data collected for a global meta-analysis of riparian forest biomass and soil carbon stocks. Includes studies estimating the carbon stored in the soil or standing live and dead woody vegetation, or the total biomass of woody vegetation in plots described as "riparian" or "floodplain". Also includes soil carbon metrics for plots considered to be "baseline" plots paired with a riparian plot. Excludes studies focused solely on depressional or tidal wetlands, plots lacking woody vegetation, greenhouse experiments, or those that measured only the biomass or carbon content of individual plants.</p> <p>The data file includes DOIs for all studies included (where available), study area coordinates, descriptions of study plots, vegetation age and soil texture (if known), reported values for woody biomass, biomass carbon stock, soil bulk density, soil carbon concentration, soil carbon stock, and/or soil sampling depth. All field descriptions are provided in the accompanying metadata file.</p>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
Data sets for simulation of urban construction consolidation centres
<p>Data from SUCCESS H2020 project used as input in the simulation activities of the Work Package 4</p> <p>This data sets is a public version of the data used in the simulation activities of the workpackage 4 in the SUCCESS project.</p> <p>It can be used to simulate the options of using one, several or no construction consolidation centers in an urban area.</p> <p>The dataset is composed of 7 distinct CSV files. All CSV files have headers.</p> <ol> <li>CCC_options_data.csv</li> <li>construction sites_data.csv</li> <li>material_demand.csv</li> <li>material_demand_periods.csv</li> <li>origin_destination.csv.csv</li> <li>suppliers_data.csv</li> <li>trucks_data.csv</li> </ol> <p><strong>construction sites_data </strong>file</p> <p>This file contains descriptions of construction sites that would be candidate to use the services of a Construction Consolidation Center (CCC).</p> <p>This file contains 99 observations of 11 fields :</p> <ol> <li> <p>site_id (<em>String</em>)<br> a unique identifier of the construction site, composed of:</p> <ul> <li> <p>one letter, </p> </li> <li> <p>an underscore, and </p> </li> <li> <p>3 digits.</p> <p>The letter represents the success pilot that provided the data. The digits sequence is the numeric identifier for the pilot.</p> </li> </ul> </li> <li> <p>private_public (<em>String</em>)<br> The mention whether the site builds a public building, a private building or a mixed building (both public and private)</p> </li> <li> <p>site_profile (<em>String</em>)<br> The profile of the building under construction:</p> <ul> <li> <p><strong>Profile I</strong> is an <strong>apartments building</strong>,</p> </li> <li> <p><strong>Profile II </strong>is an <strong>offices building</strong>,</p> </li> <li> <p><strong>Profile III </strong>is a <strong>leisure </strong>construction,</p> </li> <li> <p><strong>Profile IV</strong> is a <strong>specific building </strong>like an hospital</p> </li> </ul> </li> <li> <p>Y1 (<em>Integer</em>)<br> the turnover of the construction site on the first year of operations in EUR</p> </li> <li> <p>Y2 (<em>Integer</em>)<br> the turnover of the construction site on the second year of operations in EUR</p> </li> <li> <p>Y3 (<em>Integer</em>)<br> the turnover of the construction site on the third year of operations in EUR</p> </li> <li> <p>start (<em>Date</em>)<br> The start date of the construction project</p> </li> <li> <p>end (<em>Date</em>)<br> The end date of the construction project</p> </li> <li> <p>duration (<em>Integer</em>)<br> The duration of the construction project in months</p> </li> <li> <p>total_value_eur (<em>Integer)</em><br> The total value of the construction project in EUR</p> </li> <li> <p>size_sqm (<em>Integer</em>)<br> The size of the construction project in square meters</p> </li> </ol> <p><strong>CCC_options_data </strong>file</p> <p>This file contains descriptions of Construction Consolidation Centers that could service construction sites.</p> <p>This file contains 25 observations of 40 fields :</p> <ol> <li>ccc_id (<em>String</em>)<br> a unique identifier of the CCC, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>capacity_sqm (<em>Integer</em>)<br> The storage area capacity of the CCC in square meters</li> <li>capacity_cubic_meters (<em>Integer</em>)<br> The storage volume capacity of the CCC in cubic meters</li> <li>Activation_Cost (<em>Integer</em>)<br> The CCC activation costs in EUR</li> <li>Accessories (<em>Integer</em>)<br> The storage capacity for Accessories</li> <li>Bitumen (<em>Integer</em>)<br> The storage capacity for Bitumen</li> <li>Bricks (<em>Integer)</em><br> The storage capacity for Bricks</li> <li>Cement (Integer)<br> The storage capacity for Cement</li> <li>Coating (<em>Integer)</em><br> The storage capacity for Coating</li> <li>Electrical (Integer)<br> The storage capacity for Electrical</li> <li>Epoxi (<em>Integer)</em><br> The storage capacity for Epoxi</li> <li>External_Doors (<em>Integer</em>)<br> The storage capacity for External_Doors</li> <li>Fences (<em>Integer</em>)<br> The storage capacity for Fences</li> <li>Fire_Doors (<em>Integer</em>)<br> The storage capacity for Fire_Doors</li> <li>Gabions (<em>Integer</em>)<br> The storage capacity for Gabions</li> <li>Garden_Equipment (<em>Integer</em>)<br> The storage capacity for Garden_Equipment</li> <li>Geotexil (<em>Integer</em>)<br> The storage capacity for Geotexil</li> <li>Glass_wool (<em>Integer</em>)<br> The storage capacity for Glass_wool</li> <li>Hydraulic (<em>Integer</em>)<br> The storage capacity for Hydraulic</li> <li>Internal_Doors (<em>Integer</em>)<br> The storage capacity for Internal_Doors</li> <li>Lift (<em>Integer</em>)<br> The storage capacity for Lift</li> <li>Metal (<em>Integer</em>)<br> The storage capacity for Metal</li> <li>Metal_1 (<em>Integer</em>)<br> The storage capacity for Metal_1</li> <li>Paint (<em>Integer</em>)<br> The storage capacity for Paint</li> <li>Parquet (<em>Integer</em>)<br> The storage capacity for Parquet</li> <li>Pipes (<em>Integer</em>)<br> The storage capacity for Pipes</li> <li>Plants (<em>Integer</em>)<br> The storage capacity for Plants</li> <li>Plaster (<em>Integer</em>)<br> The storage capacity for Plaster</li> <li>Polystyrene (<em>Integer</em>)<br> The storage capacity for Polystyrene</li> <li>Precasted_Concrete (<em>Integer</em>)<br> The storage capacity for Precasted_Concrete</li> <li>Roof (<em>Integer</em>)<br> The storage capacity for Roof</li> <li>Scaffolding (<em>Integer</em>)<br> The storage capacity for Scaffolding</li> <li>Signals (<em>Integer</em>)<br> The storage capacity for Signals</li> <li>Steel (<em>Integer</em>)<br> The storage capacity for Steel</li> <li>Stone (<em>Integer</em>)<br> The storage capacity for Stone</li> <li>Store_Equipment (<em>Integer</em>)<br> The storage capacity for Store_Equipment</li> <li>Tar (<em>Integer</em>)<br> The storage capacity for Tar</li> <li>Tiles (<em>Integer</em>)<br> The storage capacity for Tiles</li> <li>Windows (<em>Integer</em>)<br> The storage capacity for Windows</li> <li>Wood (<em>Integer</em>)<br> The storage capacity for Wood</li> </ol> <p> </p> <p><strong>suppliers_data</strong> file</p> <p> </p> <p>This file contains description of suppliers that provide materials to the above construction sites.</p> <p>This file contains 407 observations of 3 fields :</p> <ol> <li>supplier_id (<em>String</em>)<br> an identifier of the supplier, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>Material_delivered (<em>String</em>)<br> the material delivered by the supplier</li> <li>Truck (<em>Integer</em>)<br> the truck identifeir of the usual truck used by the supplier to deliver the material</li> </ol> <p><strong>trucks_data</strong> file</p> <p>This file contains description of truck used by suppliers to deliver construction sites.</p> <p>This file contains 5 observations of 6 fields:</p> <ol> <li>Truck_id (<em>Integer</em>)<br> a unique identifier for the truck</li> <li>Vehicle (<em>String</em>)<br> description of the vehicle (including the number of axles)</li> <li>Capacity_(kg) (<em>Integer</em>)<br> the material transport capacity of the truck in kilograms</li> <li>Capacity_(m3) (<em>Integer</em>)<br> the material transport capacity of the truck in cubic meters</li> <li>FlagFirstEchelon (<em>String)</em><br> a flag indicating if the truck is used in 1st echelon</li> <li>FlagSecondEchelon (<em>String</em>)<br> a flag indicating if the truck is used in 2nd echelon</li> </ol> <p><strong>origin_destination </strong>file</p> <p>This file contains the quantitative data of distance and time to travel from construction sites, suppliers, and CCCs to construction sites, suppliers and CCCs using a delivery truck.</p> <p>This file contains 38640 observations of 4 fields:</p> <ol> <li>origin (<em>String</em>)<br> A composite identifier of the origin location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>destination (<em>String</em>)<br> A composite identifier of the destination location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>meters (<em>Integer</em>)<br> The drive distance from origin to destination in meters</li> <li>seconds (<em>Integer</em>)<br> The driving time from origin to destination in seconds</li> </ol> <p><strong>material demand </strong>file</p> <p>This file contains the qualitative data representing the material demand of construction sites per construction site profile .</p> <p>This file contains 1277 observations of 7 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> a unique identifier for the material demand</li> <li>profile (<em>String)</em><br> the profile of the construction site for such demand</li> <li>start_date (<em>Date</em>)<br> the start date of the activity</li> <li>end_date (<em>Date</em>)<br> the end date of the activity</li> <li>number_of_days (<em>Integer</em>)<br> the duration of the activity in days</li> <li>material (<em>String</em>)<br> the type of material requested</li> <li>supplier_id (<em>String</em>)<br> the identifier of the supplier providing the material</li> </ol> <p><strong>material_demand_periods </strong>file</p> <p>This file contains the quantitative demand data per demand and per period. Units of periods are weeks.</p> <p>This file contains 93663 observations of 5 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> the identifier for the material demand</li> <li>profile (<em>String</em>)<br> the profile type of construction for the demand</li> <li>period (<em>Integer</em>)<br> the period of the construction project during which the material has to be delivered (in number of weeks from the beginning of the construction project)</li> <li>demand_m3 (<em>Integer)</em><br> the volume of material to be delivered during the period</li> <li>demand_kg (<em>Integer)</em><br> the weight of material to be deliverd during the period</li> </ol>
Arab-Andalusian metadata set
<p>This repository contains metadata of the <a href="https://zenodo.org/record/1291776#.WzOqSq2B28o">Arab-Andalusian corpus</a> collected in the CompMusic project composed of 164 concert recordings (overall playable time more than 125 hours).</p> <p>Metadata involves the following information: title, form, mizan, nawba and tab of all recordings of the corpus, saved in separate json files. The corresponding MusicBrainz collection is available at <a href="https://musicbrainz.org/collection/142ea0d7-7fdf-4ea5-9b04-219f68023d01">this link</a>.</p> <p>The lyrics for the recordings are available from <a href="https://zenodo.org/record/1291904#.Wyea5Bx9jCI">the Arab-Andalusian music lyrics dataset</a>.</p> <p>For more information, please refer to <a href="http://compmusic.upf.edu/corpora">http://compmusic.upf.edu/corpora</a></p> <p> </p>
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
EU-CIRCLE - Virtual City Data Sets
<p>For achieving the goals of EU-CIRCLE project, a virtual datasets were created base on a reference region, enriched with bibliography. The datasets are related to Critical Infrastructure data, Forest Fire and Flood Hazards and a number of supplementary data for supporting the Hazard and Risk modelling procedures.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2018_DiDonFranceesco
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign the magnet S0805 and S0808 were used. The tests were conducted for several bottom configurations (smooth bottom; thin sand d<sub>50</sub>=0.24 mm; coarse sand d<sub>50</sub>=0.56 mm; and mixed sand 70% thin sand and 30% coarse sand). The goals of such tests were: to study the effects of the type of magnets and to carry out a preliminary analysis the ferrofluid behavior over sandy bottom.</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.