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7,195 results for “SET”
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>
Set of images published in publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization"
<p>Figures of publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ceramint.2024.10.160" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ceramint.2024.10.160</span></span></a></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>
Setting files from: Spatially explicit paleogenomic simulations support cohabitation with limited admixture between Bronze Age Central European populations.
<p><strong>Simulated Data and Custom Scripts</strong></p> <p>This dataset release permits to simulate the expansion of populations from the Pontic Steppes to Central Europe with the version of SPLATCHE3 which is included. There are 2 main zipped folders: i) the one called "SPLATCHE3executableAndSettings" contains a "ReadMe.txt" file that contains all required information to make the simulations: the resulting ".prop" file contains proportions of genomic ancestry of the P2 layer, ancestry from P1 layer is equal to 1-(proportion from P2) ; ii) the other one called "HowToMakeFigure2" contains the R script and the tables necessary to reproduce Figure 2. See Rio J, Quilodrán CS & Currat M., Communications Biology (2021), for background.</p> <p><strong>Acknowledgments</strong></p> <p>This project was financially supported by the Swiss National Research Foundation grants n° 31003A_182577 to MC and n° P400PB_183930 to CQ, as well as the IGE3 Student Salary Award to JR.</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>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set
<p><strong>General Description</strong></p> <p>The evaluation set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 31 audio files acquired from different bioacoustic sources. </p> <p>In Evaluation_Set_Annotations: the first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest. This is the same setup used during the DCASE 2021 challenge.</p> <p>In Evaluation_Set_Full_Annotations: the full annotations are provided for each file, with events marked as positive (POS) or unknown (UNK) for the class of interest.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Evaluation_Set.zip contains audio files and annotation files with 5 first POS events (as used during DCASE 2021 challenge)</em></p> <p>|__Evaluation_Set/</p> <p> |___DC/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ML/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p><em>Evaluation_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Evaluation_Set_Annotations.zip</em> has the same structure but contains only the *.csv files with first 5 POS annotations.</p> <p><em>Evaluation_Set_Full_Annotations.zip</em> has the same structure but contains only the *.csv files with all POS annotations.</p> <p>The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files.</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:<br> [ Audiofilename, Starttime, Endtime, Q ]</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_evaluation_set.csv provides a table with class code correspondance to class name for all the recordings of the Evaluation set.</p> <p>DCASE2021_task5_evaluation_set.csv<br> -------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Development Set</strong></p> <p>The development set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5412896">https://doi.org/10.5281/zenodo.5412896</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk</p>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description</strong></p> <p>The development set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 19 audio files acquired from different bioacoustic sources. The dataset is split into training and validation Sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |__Validation_Set/</p> <p> |___HV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 11<br> Total duration | 14 hours and 20 mins<br> Total classes (excl. UNK) | 19<br> Total events (excl. UNK) | 4,686<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes (excl. UNK) | 11<br> Total events (excl. UNK) | 2,662<br> Sampling rate | 24,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 3<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 3<br> Total events (excl. UNK) | 435<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes (excl. UNK) | 1<br> Total events (excl. UNK) | 355<br> Sampling rate | 22,050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 1,234<br> Sampling rate | 8,000 Hz<br> -----------------------------------------------------</p> <p><br> -----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 8<br> Total duration | 5 hours<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 310<br> -----------------------------------------------------<br> VALIDATION SET/HV<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 2 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 50<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 260<br> Sampling rate | 44,100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_training_set_classes.csv and DCASE2021_task5_validation_set_classes.csv provide a table with class code correspondace to class name for all classes in the Development set.</p> <p>DCASE2021_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2021_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5413149">https://doi.org/10.5281/zenodo.5413149</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><br> <strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk<br> </p>
All centralising monoids with binary operations as witnesses on the set {0,1,2,3}.
<p>This dataset contains all 33684 centralising monoids on the set <span>\(\{0,1,2,3\}\)</span> that can be witnessed by sets of binary operations. Among them the inclusion maximal proper monoids are shown. Classifications of various subsets of these monoids up to conjugacy are presented, as well. A detailed description of the files in the dataset can be obtained from the file binary_centralising_monoids.pdf (the code source for this file is given in binary_centralising_monoids.tex).</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>
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