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3,481 results for “data set”
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
INDICATORS TO EVALUATE THE LABOUR INSERTION OF PEOPLE WITH DISABILITIES IN CONVENTIONAL COMPANIES IN SPAIN: QUANTITIATIVE DATA SET OF DELPHI STUDY (PHASE 2 AND PHASE 3)
<p><span>The level of labor integration of people with disability (PwD) is notably lower than that of people without disabilities. In order to evaluate the success of the labor market integration of people with disabilities, it is necessary to establish a series of indicators that go beyond hiring rates. Hence, the objective of this study is to develop a list of indicators with their specified individual weight that will serve to evaluate the success of the labor market insertion of PwD in conventional companies. </span></p> <p><span>Methodology: </span></p> <p><span>The Delphi method was used. </span></p> <p><span>PHASE 1</span></p> <p><span>In Phase 1, an open-ended questionnaire was distributed to 48 human resources and disability experts. <span><br></span></span></p> <p><span>PHASE 2</span></p> <p><span>Based on the theoretical dimensions obtained, a list of 52 indicators was drawn up and the experts were asked to evaluate the importance of each item using a scale of 0 to 10 points. In addition, in this second questionnaire, they were encouraged to propose improvements in the final wording of the items, as well as in the relevance and denomination of the dimensions into which they had been grouped. No suggestions were received to modify the wording or to incorporate additional items.</span></p> <p><span>PHASE 3</span></p> <p><span>Once the scores of all the participants had been collected, a third questionnaire was sent out with the aim of achieving a statistical consensus within the group of experts. In this questionnaire, each panel member was informed of their degree of agreement or disagreement in relation to the group as a whole, without revealing the identity of the other participants. In other words, each participant was provided with information on the average rating of the group and their own initial rating (from Phase 2) of each of the 52 indicators, offering them the option to modify their response if they considered it appropriate. If they chose to change their assessment, they were asked to justify their reasons.</span></p> <p><span><span>To assess the possible convergence of opinion, the change in the responses received in the third phase with respect to the second phase was analyzed. We examined whether there had been variations in the scores given by the experts in the second phase once the group's mean ratings had been received. For this purpose, the “proportion of experts” statistic was used to verify that the average value of the responses in this third phase was within a range from [-0.5 to +0.5], compared to the average value of the scores in the second phase. In the case of non-convergence of opinion, this methodology allows for as many rounds as necessary until convergence is achieved. </span></span></p> <p><span><span>THIS DATA SET COLLECT THE ANSWERS OF THE EXPERTS OF PHASE 2 AND PHASE 3.</span></span></p>
Data set for "Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices"
<p>Data set for: Liu Y, Bech P, Tamura K, Délez LT, Crochet S, Petersen CCH (2024) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices. eLife 13: RP97602. https://doi.org/10.7554/eLife.97602</p> <p>There are 3 files in this upload:</p> <p>1. The file named "2024_Liu_eLife.pdf" is the Open Access pdf of the online publication in eLife.</p> <p>2. The file named "Liu_anatomy_data_code.zip" (~35 GB) is a zipped version of a folder "Liu_anatomy_data_code" (~111 GB), which contains the anatomical data analysed in the study along with the Python codes used to generate the published figures 1-7 and their associated figure supplements. </p> <p>3. The file named "Liu_function_data_code.zip" (~10 GB) is a zipped version of a folder "Liu_function_data_code" (~35 GB), which contains the functional data analysed in the study along with the Python codes used to generate the published figure 8 and its associated figure supplement. </p> <p>After unzipping, the Python codes should run as a Jupyter notebook (anatomy .ipynb code) or Python code (function .py code) in Anaconda.</p>
Data Set of Industry Interviews on Industrial Metaverse
<p>Data Set of Industry Interviews on Industrial Metaverse</p> <p>Industrial Metaverse for Industrial Companies: An Exploratory Study<br>Smart Service Summit - Smart Services Supporting the Value Co-creation in Industrial Contexts (2024)</p> <p>This data set comprises the following components:</p> <ul> <li>Interview Questionnaire</li> <li>Interview Metadata</li> <li>Interview Data</li> <li>Interview Coding</li> <li>Interview Quotes</li> </ul>
Integrated DInSAR + GNSS example data sets
<p>This data repository contains sample datasets of raw DInSAR time series (NSBAS_PARAMS.h5), raw, interpolated GNSS time series maps (GPS_East/North/Up.h5) , errors associated with the GNSS data (GPS_East/North/Up_sigma.h5), and integrated DInSAR + GNSS time series (fused.h5). Details about the data can be read about in the following publication: [Corsa, B. "Integration of DInSAR Time Series and GNSS data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications" <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>(3), 784; <a href="https://doi.org/10.3390/rs14030784">https://doi.org/10.3390/rs14030784</a>]. The raw DInSAR time series spans 245 dates between 2015-11-11 to 2021-04-13 over the Big Island of Hawaii. The current raw GPS data and fused time series used 22 data points between those same dates. </p>
Z+2Jet Unfolding Data Set
<p>Inspired by the recent application of ML-based unfolding to ATLAS data, we will unfold events from pp to Z + 2 jets production from the reco-level to the pre-detector or gen-level. We generate the events with Madgraph 5, shower and hadronization are simulated with Pythia 8.311, and detector effects are included via Delphes 3.5.0 using the default CMS card. Jets are clustered at gen-level and reco-level using an anti-kT algorithm with R=0.4 implemented in FastJet~3.3.4.</p> <p>We apply a set of cuts resembling the ATLAS analysis. For details on the cuts please see the associated paper. <br>All events must pass all cuts on gen and reco level. </p> <p>The training set consists of 1.5M events, the test set of 400k events.</p>
Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers [Data set].
<p>Data set covering the meta data of the 39 well fields, the macro chemistry data and the data of the noble gases and carbon, hydrogen and oxygen isotope tracers used for assessing the paleoclimate signals and age distributions in the publication in Water Resources Research (2021)</p> <p><strong>Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers</strong></p> <p>Hans Peter Broers, Jürgen Sültenfuß<sup> </sup>, Werner Aeschbach, Arne Kersting,, Armin Menkovich, Jasperien de Weert and Jeroen Castelijns</p>
Data set accompanying the research article "Complete representation of action space and value in all striatal pathways"
<p>GCaMP6s calcium imaging data set recorded from freely behaving mice performing open field and 2-choice decision-making tasks using miniscopes. Mice were implanted in the right dorsomedial striatum and three types of output neurons were genetically targeted using transgenic Cre-lines. The data set comprises single-cell spatial filters and calcium activity traces extracted using CaImAn (https://github.com/flatironinstitute/CaImAn) as well as behavioral event logs and tracking coordinates. For more details please refer to the article "Complete representation of action space and value in all striatal pathways" published by the data sets' authors. Analysis code can be found at https://doi.org/10.5281/zenodo.5034618.</p>
DRALOD D1.3 Results of performance testing of the prototype of energy recovery system data set
<p>Data set for delivery D1.3 Results of performance testing of the prototype of energy recovery system </p>
Food riots and food prices in the Eastern Mediterranean (Bilād al-Shām) in the 19th and 20th centuries: a data set
<p>This is an archival release to document the state of the data set for this research project before it got severely derailed by the Covid-19 pandemic and the explosion in Beirut on 4 August 2020. Please consult the readme for a detailed description of the contents and workflows.</p>
SIPIBEL data set
<p>The Bellecombe pilot site – SIPIBEL – was created in 2010 in order to study the characterisation, treatability and impacts of hospital effluents in an urban wastewater treatment plant. This pilot site is composed of: i) the CHAL hospital, opened in February 2012, ii) the Bellecombe wastewater treatment plant, with two separate treatment lines allowing to fully separate the hospital wastewater and the urban wastewater, and iii) the Arve River as the receiving water body and a tributary of the Rhône River and the Geneva aquifer. The data base includes in total 48 439 values measured on 961 samples (raw and treated hospital and urban wastewater, activated sludge in aeration tanks, dried sludge after dewatering, river and groundwater, and a few additional campaigns in aerobic and anaerobic sewers) with 44 455 physico-chemistry values (including 15 pharmaceuticals and 14 related transformation products, biocides compounds, metals, organic micropollutants, etc.), 2 193 bioassay values (ecotoxicity), 1 679 microbiology values (including microorganisms and antibioresistance indicators) and 112 hydrobiology values.</p> <p>The ZIP file available on Zenodo contains i) a PDF file describing the complete data set and its content, and ii) ten Excel files with all SIPIBEL data.</p> <p>Additional information is given in this Open Access paper: <a href="https://doi.org/10.1016/j.dib.2021.107726">https://doi.org/10.1016/j.dib.2021.107726</a></p>
Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'
<p><strong>Self-supervised machine learning code and data for segmenting live cell imagery (Matlab)</strong></p> <p><em>Running the Code</em></p> <p>SSL_Demo_2.m : main program for self-supervised machine learning segmentation</p> <p>SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m)</p> <p>This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background.</p> <p>It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure.</p> <p>Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run.</p> <p><em>Code Methodology</em></p> <p>The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required.</p> <p>Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination.</p> <p>The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy.</p> <p>Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions.</p> <p>The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing): </p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1">https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1</a></p> <p>This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.</p>
Data set of anthropogenic contaminants in snow from polar regions (Ny-Alesund and Dome C)
<p>The produced dataset (in MS Excel format) contains concentrations of mercury, trace elements and organic contaminants in snow samples collected in the Ny-Alesund area (Svalbard - Norway) (78.917° N 11.933° E) and from the Antarctic Plateau, Dome C (75.103°S, 123.35°E). The Arctic sampling sites are reported in figure 1. The concentrations for trace elements and mercury are in ngg<sup>-1</sup> while for the organic contaminants they are reported in ngL<sup>-1</sup>.</p> <p>The inorganic contaminants dataset reports concentration of Hg, Trace elements and Black Carbon in Arctic and Antarctic site. The Arctic sites are subdivided in annual snow pack on the glacier and surface snow sampling close to the Gruvebadet Aerosol Laboratory. In Antarctica mercury concentrations in surface snow are also reported.</p> <p>The organic contaminants dataset reports the concentrations of Polycyclic Aromatic Hydrocarbons (PAHs) in surface snow samples collected close to the Gruvebadet Aerosol Laboratory (78.91622°N 11.89536°E, Ny Alesund, Norway). Samplings were performed from 04/10/2018 to 13/05/2019, obtaining a total of 35 samples, encompassing the entire winter season with an approximatively weekly resolution. Total PAH (sum of naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo(<em>a</em>)anthracene, chrysene, benzo(<em>b</em>)fluoranthene, benzo(<em>k</em>) fluoranthene, benzo(<em>a</em>)pyrene, benzo(<em>ghi</em>)perylene, indeno(<em>1,2,3-c,d</em>)pyrene and dibenzo(<em>a,h</em>)anthracene) concentrations range from 0.8 to 37 ng L<sup>-1</sup>. Individual PAHs were mean blank corrected and average percentage abundances in the samples are reported in the dataset.</p>
SERPENT MT data set, in MARE2DEM format
<p>This file contains the marine MT data from the SERPENT experiment offshore Nicaragua. The equipment used were the Scripps EM Lab's EM broadband receivers. The data is in MARE2DEM format. It includes the transfer functions (apparent resistivity and phase) in units of ohm-m and degrees, respectively, along with their errors. Data IDs 103 and 104 refer to the TE mode apparent resistivity and phase, respectively, while the Data IDs 105 and 106 refer to the TM mode apparent resistivity and phase, respectively.</p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"
<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2021_Sippy_FUNCTION.pdf</strong>" is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named "<strong>Sippy_data_code.zip</strong>" (~5 GB) is a zipped version of a folder ‘<em>Sippy_data_code</em>’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘<em>Sippy_data_code</em>’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘<em>Data</em>’ contains the data structure ‘<em>Data.mat</em>’ to be analyzed, as well as a Matlab file called ‘<em>p_value_colormap.mat</em>’ used to plot the p value color bars in some figures.</p> <p>The subfolder ‘<em>Functions</em>’ contains functions called by the main codes.</p> <p>The subfolder ‘<em>Codes</em>’ contains the following codes:</p> <p><em>‘AnalyzeDataStructure.m’: </em>computes the results and saves them as a new data structure called ‘<em>Analyzed_Data</em>’, in the subfolder ‘<em>Results</em>’.</p> <p><em>‘Figure_1.m’: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>‘Figure_2.m’: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>‘Figure_3.m’: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>‘SuppFigure_2.m’: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>‘SuppFigure_3.m’: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>‘SuppFigure_4.m': </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>‘Mouse_Name’</em>: name of the mouse.</p> <p><em>‘Mouse_RecordingDate’</em>: date of recording (YMD).</p> <p><em>‘Mouse_DateOfBirth’</em>: date of birth of the mouse (YMD).</p> <p><em>‘Mouse_Sex’</em>: sex of the mouse (F or M).</p> <p><em>‘Mouse_Genotype’</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>‘Mouse_Level’</em>: Training level (NAÏVE or EXPERT).</p> <p><em>‘Cell_Counter’</em>: cell recorded in a given mouse.</p> <p><em>‘Cell_Type’</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>‘Cell_TargetedBrainArea’</em>: Brain area targeted (DLS).</p> <p><em>‘Cell_Recovered’</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>‘Cell_Coordinates’</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>‘Cell_Fluorescence’</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>‘Sweep_Counter’</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>‘Sweep_Type’</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>‘Sweep_MembranePotential’</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>‘Sweep_CurrentInjected’</em>: current injected into the cell (pA).</p> <p><em>‘Sweep_PiezoLick’</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>‘Sweep_Trial’</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>‘Sweep_WhiskerStim’</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_Valve’</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>‘Sweep_SamplingRate’</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>‘Sweep_TimeStamp’</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>‘Sweep_Reward’</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_APThresh’</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p> </p>
Open Science Team 7 Data Set and Bibliography
<p>As a small exercise before delving into a group research project we generated a small data set based on the review of 7 websites ranging in subject matter. We provide the Data set and bibliography here. </p>
Data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler.
<p>This file system package contains data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler. It presents three quantum chemically calculated data sets ("MP2", "DMol", and "ZBL pair-specific") for diatomic interatomic potentials in the repulsive region, where the separation of the atoms is so short that the potential energy is >> 10 eV. The set also contains the fitted parameters for analytical NLH repulsive potentials that consist of a Coulomb term multiplied by a three-exponential screening function.</p> <p>The version from Oct 9, 2025 has updated NLH parameters for the pair Na-O (Z1=8, Z2=11). Otherwise the data is identical to before.</p> <p>The MP2 data sets contain potential data for all elements pairs Z1+Z2<=36, and the DMol, ZBL pair-specific and NLH potentials are proved for all elements pairs Z1, Z2 <=92.</p> <p>The data sets are arranged in the following directories:</p> <p>mp2/ : Data sets for the Hartree-Fock Moller-Plesset2 level calculations</p> <p>dmol/ : Data sets for the Density Functional Theory calculations with the DMOL code</p> <p>nlh/ : Coefficients for the fits of the Nordlund-Lehtola-Hobler potential to the DMol data sets</p> <p>zbl/ : Data sets for the pair-specific Ziegler-Biersack-Littmark potential calculations</p> <p>zbluniv/ : A directory with a Linux bash/awk script that generates the ZBL universal potential.</p> <p>zblspec/ : Coefficients for the pair-specific ZBL screening functions as contained in SRIM-2013.</p> <p>Each subdirectory has its own README.txt file giving additional details on the content of the directory and its subdirectories.</p>
Task and Dimension Switching Data Set
<p>This data set contains the data of 8 experiments concerned with the question how multi-component task sets are organised. Four different views on task set organisation were tested in these experiments. Experiments 1-4 were used in Vandierendonck, A., Christiaens, E., & Liefooghe, B. (2008). On the representation of task information in task switching: Evidence from task and dimension switching. <em>Memory & Cognition, 36</em>(7), 1248-1261. doi: 10.3758/mc.36.7.1248. The entire data set was used for a test of the utility of integrated measures of speed and accuracy. This is currently a submitted paper: Vandierendonck, A. Further tests of the utility of integrated speed-accuracy measures in task switching. Journal of Cognition.</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.