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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>
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>
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>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2017_Privitera
<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 magnet type S0805 and a number of magnets equal to 2,3 and 4 were used. The tests were conducted both over a fixed bed (Perspex<sup>©</sup>) and in the presence of mobile beds. The goals of such tests were: to study of the velocity profiles for some fixed and mobile bottoms; to study the effects of the number of magnets on the ferrofluid behavior; preliminary analysis of the bed shear stress over sandy bottom.</p>
The intermittency regions of powder snow avalanches [Data-set]
<p>This data repository contains the data-sets presented in the publication:</p> <p>Sovilla, B., McElwaine, J. N., & Köhler, A. (2018). The intermittency regions of powder snow avalanches. Journal of Geophysical Research: Earth Surface, 123, <a href="https://doi.org/10.1029/2018JF004678">https://doi.org/10.1029/2018JF004678</a>.</p> <p>This data set should be cited, together with the publication, as a:</p> <p>B. Sovilla, J. N. McElwaine, and A. Köhler (2018), The intermittency regions of powder snow avalanches [Data set]. Zenodo. <a href="https://doi.org/10.5281/">https://doi.org/10.5281/</a> zenodo.1415456.</p> <p>Information on the data can be found in the Readme file or can be obtained by writing an e-mail at: avalanche.data@slf.ch.</p>
A Panel Data Set of Cryptocurrency Development Activity on GitHub
<p>Contents:</p> <ul> <li><strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all data, sorted by date. This file contains some imputed values for missing data, and all fields across all repositories and normalized to "null". This is the most convenient form to use.</li> <li><strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong>: CSV format of all, sorted by date. It is the raw data after processing the raw format.</li> <li><strong>raw-data-2018-01-21-to-2019-02-04.tar.gz</strong>: The raw format of data collected (S-expressions). Contains additional contributor data and CoinMarketCap data not currently in the CSV datasets.</li> <li><strong>recovered.patch</strong>: The modification on <strong>all-sorted-2018-01-21-to-2019-02-04.csv</strong> after recovering (imputing) data<strong>, </strong>showing what was recovered.</li> <li><strong>recovered-normalized.patch</strong>: The modification of <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>after normalizing the recovered data set. Thus, patching <strong>all-sorted-2018-01-21-to-2019-02-04.csv </strong>with<strong> recovered.patch</strong>, then <strong>recovered-normalized.patch </strong>gives <strong>all-sorted-recovered-normalized-2018-01-21-to-2019-02-04.csv</strong></li> <li><strong>missing-dates.txt</strong>: Days for which we missed GitHub data collection (partial or completely).</li> </ul> <p>Related publications:</p> <pre><code>@inproceedings{van-tonder-crypto-oss-2019, title = {{A Panel Data Set of Cryptocurrency Development Activity on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "{van~Tonder}, Rijnard and Trockman, Asher and {Le~Goues}, Claire", series = {MSR '19}, year = 2019 } @inproceedings{trockman-striking-gold-2019, title = {{Striking Gold in Software Repositories? An Econometric Study of Cryptocurrencies on GitHub}}, booktitle = "International Conference on Mining Software Repositories", author = "Trockman, Asher and {van~Tonder}, Rijnard and Vasilescu, Bogdan", series = {MSR '19}, year = 2019 }</code></pre> <p>Related code: <a href="https://github.com/rvantonder/CryptOSS">https://github.com/rvantonder/CryptOSS</a></p>
FEUTURE Elite Survey - Data Set
<p>This deliverable consists of the data set with value and variable labels of the Elite Survey in SPSS.</p>
A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, the Netherlands
<p>The data set contains 39 digital elevation models and 11 orthophotos of a beach-foredune system near Egmond aan Zee, the Netherlands, a high-wave storm-dominated site with an approximately 25 m high foredune. The elevation data set combines a long duration (six years; January 2013 - January 2019) with a high temporal resolution (typically 2-4 months) and is spatially extensive (1.4 km alongshore) with a high spatial (1 m) resolution. To facilitate the testing and further development of coastal dune evolution models, the data set is supplemented with high-frequency time series of offshore wave, water level and wind characteristics as well as several subtidal bathymetries.</p><p>The data set is described in detail in the following open-access, peer-reviewed paper:</p><p>Ruessink, G.; Schwarz, C.S.; Price, T.D.; Donker, J.J.A. A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, The Netherlands. <i>Data</i> <strong>2019</strong>, <i>4</i>, 73. <a href="https://doi.org/10.3390/data4020073">https://doi.org/10.3390/data4020073</a></p><p>Update December 7, 2023: The data descriptor paper contains a typo related to the rotation of the RD and local coordinate schemes. On page 4/15 it is said that this rotation angle is 177 degrees, it should be 172.8 degrees. A big thank-you to Haoyang Peng (UNSW, Australia) for pointing out that the 177 degrees is incorrect. </p><p> </p><p> </p>
Historical contingency shapes adaptive radiation in Antarctic fishes [Data set]
<p>Assembled reference contigs for protein-coding exons and conserved non-coding regions from targeted sequence enrichment of notothenioid fishes and outgroups. </p> <p>Published in : Daane, JM, Dornburg, A, Smits, P, MacGuigan, D, Hawkins, B, Near, TJ, Detrich, HW III*, Harris MP*. (2019). Historical contingency shapes adaptive radiation in Antarctic fishes. <em>Nature Ecology & Evolution.</em></p> <p> </p> <p>-contigs.zip contains the assembled contigs for each species. Each contig represents a targeted region with the addition of flanking DNA sequence</p> <p>-cnes.zip contains the targeted conserved non-coding regions isolated from the larger contigs in contigs.zip</p> <p>-exons.zip contains the targeted protein coding exons isolated from the larger contigs in contigs.zip</p> <p>-protein.zip contains the translated protein coding exons from exons.zip</p>
Use Case 1 Data set
<p>Effect of nanofiller on the stiffness of a Carbon Fiber Reinforced Thermoplastic (CFRP).<br> The effect of different volume fractions (0%, 2%, 5% and 12%) of Multiwall Carbon Nanotubes (MWCNT) are computed for PEEK reinforced Carbon Fiber UD. Two different Carbon Fiber(CF) volume fractions are considered (40% and 60%). </p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
Inferelator Saccharomyces Cerevisiae Data Set
<p>This data is associated with the Inferelator package. It consists of an expression data set, a prior data matrix generated from ATAC-seq data, and a gold standard derived from YEASTRACT. It was initially used in Tchourine, K., Vogel, C., and Bonneau, R. (2018). Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks. Cell Reports 23, 376–388.</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.