Find research datasets worth reusing

opencc-zeroDec 2023View details →

Data and code for: Does increasing temperature accentuate disease impacts on fisheries species? A meta-analysis

<p>Rapid warming could drastically alter host-parasite relationships, which is especially important for fisheries crucial to human nutrition and economic livelihoods; yet we lack a synthetic understanding of how warming influences parasite-induced mortality in these systems. We conducted a meta-analysis using 301 effect sizes from 60 empirical papers on harvested aquatic species and determined the relationship between parasite-induced host mortality and temperature and how this relationship was altered by host, parasite and study design traits. Overall, temperature increased parasite-induced host mortality; however, the magnitude and sometimes direction of this relationship varied. Hosts from the order Salmoniformes experienced a greater increase in parasite-induced mortality with temperature than average. Opportunistic parasites were correlated with a greater increase in host mortality with temperature than average, while bacterial parasite-induced mortality was lower than average as temperature increased. Thus, parasites will generally increase host mortality as the environment warms; however, this effect will vary among systems.</p>

opencc-by-4.0Dec 2023View details →

Data and analysis code

<p>Data and analysis code for the paper 'Neuronal tuning to threat exposure remains stable in the mouse prefrontal cortex over multiple days' (PLOS Biology).</p> <p><strong>This repository contains:</strong></p> <ol> <li>The full dataset: - dataset.py</li> <li> Main analysis code: fig1-fig4.py and supplemental1-supplemental7.py</li> <li>Function code: glm_to_predict_speed_from_neuronal_activity.py, glm_tst_50_neurons_no_iterations.py, Graphs.py, manifold.py, prediction.py, rate_coding.py, statistics_test.py, time_functions.py, time_locked_functions.py, transient.py</li> <li>Source data for figures: - source_data.xls</li> </ol> <p>The function code is normally stored in a folder called function.</p> <p> </p> <p><strong>Structure of the dataset:</strong></p> <p>The dataset is a python dictionary with 3 levels. </p> <p><em>Level 1: Animal ID</em></p> <ul> <li>'animal294', dict</li> <li>'animal295', dict</li> <li>'animal329', dict</li> <li>'animal337', dict</li> <li>'animal339', dict</li> <li>'animal341', dict</li> </ul> <p><em>Level 2: Recording day</em></p> <ul> <li>'day1', dict</li> <li>'day3', dict</li> <li>'day9', dict</li> </ul> <p><em>Level 3: Variables</em></p> <ul> <li>'A', spatial coordinate of extracted neurons, csc_matrix</li> <li>'assignments':  assignments id of multisession registration, Numpy array</li> <li>'assignments_filts': assignments id of multisession registration containing only detected neurons, NumPy array</li> <li>'background_time': boolean array of timepoints when the behavior could not be clearly assigned to either struggle or immobility behavior, NumPy array.</li> <li>'baseline_speed_bodyparts': dict with multiple NumPy arrays of the animal's speed of various bodyparts during baseline, dict</li> <li>'baseline_speed_dlc': speed of the animal during baseline averaged for all bodyparts, NumPy array</li> <li>'fn_baseline': calcium activity during baseline, NumPy array</li> <li>'fn_tst': calcium activity during tail suspesion, NumPy array</li> <li>'hemis': The hemisphere of the recording was from, str</li> <li>'immobility_time': boolean array of timepoints the animal is immobile, NumPy array</li> <li>'sex': the sex of the animal, str </li> <li>'struggle_time': boolean array of timepoints the animal is struggling, Numpy array</li> <li>'tst_speed_bodyparts': dict with multiple NumPy arrays of the animal's speed of various bodyparts during tail suspension, dict</li> <li>'tst_speed_dlc': speed of the animal during tail suspension averaged for all bodyparts, NumPy array</li> </ul> <p> </p>

opencc-by-4.0Dec 2023View details →

Data for BY-COVID Pathways to MINERVA Analysis Workflow

<p>Source data (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE182152">GSE182152</a>) was analysed with <a href="https://workflowhub.eu/workflows/688">WFHub:688</a> to generate these datasets</p>

opencc-by-4.0Nov 2022View details →

Climate data and geographic data from Madagascar for learning multi-criteria analysis in GIS courses

<p>Climate data and geographic data from Madagascar for learning multi-criteria analysis in GIS courses. </p> <ul> <li><strong>MultiCriteriaAnalysis_ENG_v2023_v4.pdf</strong> - summary of Multi-Criteria Analysis methods for suitability of mango tree fruitculture.</li> <li><strong>MultiCriteriaAnalysis_simple_ENG_v2023_v4.xlsx</strong> - sheets with support material fromPDF and videos in lecture channel</li> <li><strong>Country borders</strong>: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/</li> <li><strong>Rivers</strong>: https://www.naturalearthdata.com/downloads/10m-physical-vectors/</li> <li><strong>Roads</strong>: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/ </li> <li><strong>Global land cover classes - GLOBCOVER</strong>: http://due.esrin.esa.int/page_globcover.php</li> <li><strong>Temperature and precipiatation </strong>- climate variables: clipped from WorldClim database rasters (see http://www.worldclim.org/) </li> <li><strong>Digital Elevation Model - DEM </strong>from EarthExplorer web portal (see https://www.cirgeo.unipd.it/didattica/GIS/01x_access_geodata.html#USGS:_EarthExplorer<br> </li> </ul>

opencc-by-4.0Nov 2023View details →

Analysis of Metabolomics Data to Assess Interactions in Microalgal Co-culture of Skeletonema marinoi and Prymnesium parvum

<p>This dataset refers to the metabolomics results from Metabolome Annotation QWorkflow on a co-culture of two microalgae: <em>Skeletonema marinoi </em>and <em>Prymnesium parvum</em>. The metabolomics data was acquired from endometabolome and exometabolome in both positive and negative MS modes. These will be referred as conditions. The files ms2_spectra_condition.mzML files have the MS2 combined from different MS2 files found on Zenodo with DOI: 10.5281/zenodo.10143233. The MS1 files are available on Zenodo as well with the DOI: 10.5281/zenodo.10143127</p> <p>The first section is about the results from the MS1 analysis. For the code used to generate these files, please refer to the code: <a href="https://github.com/zmahnoor14/MAW/tree/main/co-culture">https://github.com/zmahnoor14/MAW/tree/main/co-culture</a> </p> <ol> <li>Feature_info_condition.csv refers to the list of features with IDS, m/z, rt and intensity values. <ul> <li>The feature list is used to link the MS1 features to the features extracted from MS2 spectra.</li> </ul> </li> <li>Feature_list_condition.csv refers to the list of mzML origin file (samples) and the intensity of the features in those samples.</li> </ol> <p>The second section relates to the MS2 results. For source code please refer to: <a href="https://github.com/zmahnoor14/MAW/tree/main/Docker">https://github.com/zmahnoor14/MAW/tree/main/Docker</a></p> <ol> <li>SL_MAW_Coculture.csv contains list of metabolic features that were annotated and found to be present in the suspect list of either of the two organisms or both. The suspect lists for Skeletonema marinoi can be found at 10.5281/zenodo.5772755, and for Prymnesium parvum can be found at 10.5281/zenodo.7864506.  </li> <li>unique_MAW_SMILES_coculture.csv file contains all information on unique SMILES.</li> <li>onlyDAF.csv contains differentially abundant features in either of the conditions: <em>S. marinoi </em>co-culture, <em>S. marinoi</em> mono-culture and similar conditions for <em>P. parvum</em>.</li> <li>condition_mergedResults-with-one-Candidates_sig_feat_for_only_inclusion.csv files contain all MS2 features and annotations together with the information on whether these features were found in the inclusion list (List provided for generating MS2 spectra in orbitrap), and whether these features were differential.</li> </ol>

opencc-zeroDec 2023View details →

Data from: Charcoal analysis for temperature reconstruction with infrared spectroscopy

<p>The duration and maximum combustion temperature of vegetation fires are important fire properties with implications for ecology, hydrology, hazard potential, and many other processes. Directly measuring maximum combustion temperature during vegetation fires is difficult. However, chemical properties of charcoal formed as a by-product of fire reflect key chemical transformations associated with temperatures. Therefore, they could be used indirectly to determine the maximum combustion temperature of vegetation fires. To evaluate the reliability of charcoal chemistry as an indicator of maximum combustion temperature, we studied the chemical properties of charcoal formed through two laboratory methods at measured temperatures. Using a muffle furnace, we generated charcoal from the woody material of ten different tree and shrub species at seven distinct peak temperatures (from 200 °C to 800 °C in 100 °C increments). Additionally, we simulated more natural combustion conditions by burning woody material and leaves of four tree species in a combustion facility instrumented with thermocouples, including thermocouples inside and outside of tree branches. Charcoal samples generated in these controlled settings were analyzed using Fourier Transform Infrared (FTIR) spectroscopy to characterize their chemical properties. The Modern Analogue Technique (MAT) was employed on FTIR spectra of muffle furnace charcoal to assess the accuracy of inferring maximum pyrolysis temperature. The MAT modeltemperature matching accuracy improved from 46% for all analogues to 81% when including ±100 ℃. Furthermore, we used MAT to compare charcoal created in the combustion facility with muffle furnace charcoal. Our findings indicate that the spectra of charcoals generated in a combustion facility can be accurately matched with muffle furnace-created charcoals of similar temperatures using MAT, and the accuracy improved when comparing the maximum pyrolysis temperature from muffle furnace charcoal with the maximum inner temperature of the combustion facility charcoal. This suggests that charcoal produced in a muffle furnace may be representative of the inner maximum temperatures for vegetation fire-produced charcoals. FTIR spectroscopy is a promising tool for determining maximum fire temperature from charcoals of vegetation and prescribed fires and may have implications for fossil charcoal from palaeoecological records. </p>

opencc-by-4.0Dec 2023View details →

Comparative Analysis of Methods to Estimate Geodetic Strain Rates from GNSS Data in Italy

<p>This dataset comprises GNSS velocity field and strain rate maps for Italy.</p> <p><strong>List of Files:</strong></p> <ol> <li> <p><strong>velocity_dataset.dat</strong></p> <ul> <li>GNSS velocity field of stations with time series longer than 4.5 years.</li> <li>Columns: Longitude (degrees), Latitude (degrees), East component of velocity (mm/yr), North component of velocity (mm/yr), Uncertainty on the East component (mm/yr), Uncertainty on the North component (mm/yr), Station Name.</li> </ul> </li> <li> <p><strong>velocity_dataset_filtr.dat</strong></p> <ul> <li>Filtered velocity field.</li> <li>Columns: Longitude (degrees), Latitude (degrees), East component of velocity (mm/yr), North component of velocity (mm/yr), Uncertainty on the East component (mm/yr), Uncertainty on the North component (mm/yr), Station Name.</li> <li>Stations ending with 'GPM' represent velocity values obtained by merging neighboring stations.</li> </ul> </li> <li> <p><strong>strain_rate_nn.dat (strain_rate_visr.dat, strain_rate_wav.dat)</strong></p> <ul> <li>Strain rate computed on cells spaced by 0.025°.</li> <li>Suffixes in the file names: 'nn' refers to the Nearest Neighbor method, 'visr' refers to the VISR method, and 'wav' refers to the Wavelet-based method</li> <li>Columns: Longitude (degrees), Latitude (degrees), exx (east) component of the strain rate tensor (nstr/yr), exy (east, north) component of the strain rate tensor (nstr/yr), eyy (north) component of the strain rate tensor, second invariant of the strain rate (nstr/yr), most extensive eigenvalue (nstr/yr), most compressive eigenvalue (nstr/yr), angle between north and the direction of the eigenvector corresponding to the most compressive eigenvalue (degrees, positive clockwise).</li> </ul> </li> </ol> <p><strong>Reference:</strong></p> <p>For further details, please refer to the article "Comparative Analysis of Methods to Estimate Geodetic Strain Rates from GNSS Data in Italy", published in <em>Annals of Geophysics</em>.</p>

opencc-zeroJan 2024View details →

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

<p>Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,459 hereditary diseases. Overall, we identified 114 cell types affected by 1,140 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.</p>

opencc-zeroJan 2024View details →

Qualitative raw data and behavioral analysis for understanding VMMC policy decision-making

<p>Faced with declining donor funding for HIV, low- and middle-income countries must identify efficient and cost-effective ways to integrate HIV prevention programs into public health systems for long-term sustainability. In Zambia, donor support to the voluntary medical male circumcision (VMMC) program, which previously funded non-governmental organizations as implementing partners, is increasingly being directed through government structures instead. We developed a framework to understand how the behaviors of individual decision-makers within the government could be barriers to this transition. We interviewed key stakeholders from the national, provincial, and district levels of the Ministry of Health, and from donors and partners funding and implementing Zambia's VMMC program, exploring the decisions required to attain a sustainable VMMC program and the behavioral dynamics involved at personal and institutional levels. Using pattern identification and theme matching to analyze the content of the responses, we derived three core decision-making phases in the transition to a sustainable VMMC program: 1) developing an alternative funding strategy, 2) developing a policy for early-infant (0-2 months) and early-adolescent (15-17 years) male circumcision, which is crucial to sustainable HIV prevention; and 3) identifying integrated and efficient implementation models. We formulated a framework showing how, in each phase, a range of behavioral dynamics can form barriers that hinder effective decision-making among stakeholders at the same level (e.g., national ministries and donors) or across levels (e.g., national, provincial and district). Our research methodology and the resulting framework offer a systematic approach for in-depth investigations into organizational decision-making in public health programs, as well as development programs beyond VMMC and HIV prevention. It provides the insights necessary to map organizational development and policy-making transition plans to sustainability, by explaining tangible factors such as organizational processes and systems, as well as intangibles such as the behaviors of policymakers and institutional actors.</p>

Experimental data for: "Experimental evaluation of the use of cruciform specimens for biaxial stability analysis"

<p>Dataset containing the raw data, in Microsoft Excel format, of a biaxial compression-compression test (C-C-3) used as representative test analysed in the manuscript "Experimental evaluation of the use of cruciform specimens for biaxial stability analysis".</p> <p>The dataset includes:</p> <ul> <li>Raw data from the biaxial testing machine (force/displacement for the 4 electromechanical actuators).</li> <li>Raw data from the strain gauge rosette placed in the biaxially-loaded region.</li> <li>Partially analyzed data, obtained using a 3D Digital Image Correlation facility and postprocessed using LaVision Davis software to obtain the strain and transversal displacement in the gauge region.</li> </ul>

Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry: Source and Supplementary Data

<p><em><strong>Source and Supplementary data for AANRI manuscript</strong></em></p>

opencc-by-4.0Feb 2024View details →

Data from: "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells"

<h1><strong>Intro</strong></h1> <p>Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185</p> <p>Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.</p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C,  for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.</p> <table> <tbody> <tr> <td> <div> <p><strong>Experiment</strong></p> </div> </td> <td> <div> <p><strong>SOC Window</strong></p> </div> </td> <td> <div> <p><strong>Cycles per ageing set</strong></p> </div> </td> <td> <div> <p><strong>Current</strong></p> </div> </td> <td> <div> <p><strong>Temperature</strong></p> </div> </td> <td> <div> <p><strong>Number of Cells</strong></p> </div> </td> </tr> <tr> <td> <div> <p>1</p> </div> </td> <td> <div> <p>0-30%</p> </div> </td> <td> <div> <p>257</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>2,2</p> </div> </td> <td> <div> <p>70-85%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> <div> <p>3</p> </div> </td> <td> <div> <p>85-100%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>4</p> </div> </td> <td> <div> <p>0-100% (drive-cycle)</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / noisy D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>5</p> </div> </td> <td> <div> <p>0-100%</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> </tbody> </table> <p>Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.</p> <p>The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.</p> <p>An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.</p> <h1>Data</h1> <p>Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.</p> <h2>Summary Data</h2> <p>This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.</p> <p>It contains:    </p> <h3>Performance Summary</h3> <p>A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.</p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. See our <a href="https://doi.org/10.1021/acsaem.2c02047">ACS publication</a> for more details.</p> <p>Data includes:</p> <p>·       Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>·       Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.</p> <p>·       Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>·       Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>·       Age set average temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps).</p> <p>·       Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).</p> <p>·       Energy throughput: as with "charge throughput", but for energy.</p> <p>·       C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.</p> <p>·       C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.</p> <p>·       0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition).</p> <p>·       Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.</p> <p>·       Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>·       DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>·       RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.</p> <h3>Ageing Sets Summary</h3> <p>Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.</p> <h2>Processed Timeseries data</h2> <p>Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.</p> <p>Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.</p> <h2>Raw Data</h2> <p>These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.</p> <p>The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be multiple datafiles for each cell in a given ageing set; during analysis, these should be concatenated according to the descriptor in the filename (e.g., ‘cycling7’ + ‘cycling7 (part 2)').</p> <p>Similarly, the unprocessed raw data from the performance checks (i.e. RPTs) is stored in the 'Performance Checks' folder, and structured in the same way.</p> <p>The raw data are saved in the .mpr format produced by the Biologic battery cycler. This is a binary format which is storage-efficient but can be more difficult to process for analysis purposes. We have therefore also exported the data into .txt files (called .mpt) for the performance checks (RPTs) which make analysis easier. However, the exported .mpt files could not be included for the degradation cycling files due to their larger size. If you require access these degradation cycle data, the .mpr binary file can be parsed using the <a href="https://github.com/echemdata/galvani">Galvani</a> package in python, or you can use Biologic’s (proprietary) BT-Lab software to export the data into .txt files.</p> <h3>File Naming Convention</h3> <p>The raw datafiles are named with a standard format. This is:</p> <p>            <em>NDK - LG M50 deg - exp 1 - rig 1 - 10degC - cell A - RPT1_01_MB_CB1</em></p> <p>            {NDK - LG M50 deg} - {exp 1} – {rig 1} – {10degC} – {cell A} – {RPT1}_{01}_{MB}_{CB1}</p> <p>{Standard prefix} – {experiment number} – {ID of test rig} – {control temperature} – {Cell ID} – {RPT number <em>or</em> aging cycle number}_{step number for the characterisation procedure (see above)}_{experimental technique name (will always be “MB”)}_{battery cycler channel ID used (always the same for a particular cell/experiment)}</p> <p> </p>

Data and Analysis Files for Simulations of Radiatively-Cooled Magnetic Reconnection on the Z machine

<p>This dataset contains processed simulation data, representative simulation output files, and Python code for analysis of simulations presented in Datta, Rishabh, et al. "Simulations of Radiatively Cooled Magnetic Reconnection Driven by Pulsed Power." J. Plasma Phys. (2024).</p> <p>The simulations were run using GORGON, a radiative resistive MHD code with van Leer advection.<br><br>Details on GORGON can be found in Chittenden et al. (2004) 10.1063/1.1643756, and Ciardi et al. (2007) 10.1063/1.2436479.</p>

Data for "Laundry to Laboratory: Automated Image Analysis for the Characterization of Fibrous Microplastics"

<p>This repository contains a representative subset of filter paper images used to evaluate the various experimental conditions in the manuscript "Laundry to Laboratory: Automated Image Analysis for the Characterization of Fibrous Microplastics." </p>

opencc-by-4.0Feb 2024View details →

Examples for running TraceGroomer: format and normalise your labeled metabolomics data for DIMet analysis

<p>Examples to test our tool <a href="https://github.com/cbib/TraceGroomer">TraceGroomer</a>, to prepare your files for DIMet (Differential analysis of Isotope-labeled Metabolomics data).</p> <p>Each example represents one type of input supported by TraceGroomer. Please download the entire .zip and find inside the example that best suits your case.</p> <p>The new version v2 contains four types of input:</p> <ol> <li>IsoCor .tsv generated file: <em>example-isocor_data</em></li> <li>The rule of "three .tsv files" , i.e. sampleMetadata, variableMetadata and dataMatrix: <em>example-ruletsv_data</em></li> <li>custom or generic .xlsx file: <em>example-sheet_data</em></li> <li>VIB MEC .xlsx file: <em>example-vib_data</em></li> </ol> <p>Visit the <a href="https://github.com/cbib/TraceGroomer/wiki">TraceGroomer Wiki page</a> for further information and how to run the tool on the provided examples. </p> <p><strong>For users of the Galaxy </strong>version of Tracegroomer: please only use the 'data/' folder (ignore the 'groom_files/' folder)</p>

Digisonde Data files used for JGR-Space Physics paper "A simplified method of true height analysis to estimate the real height of sporadic E layers"

<p>paper submitted for publication in JGR-Space Physics.</p> <p>Digisonde Data files used for analysis</p> <p> </p> <p>A simplified method of true height analysis to estimate the real height of sporadic E layers</p> <p> </p> <p>Christos Haldoupis</p> <p>Department of Physics, University of Crete, Heraklion, Greece</p> <p>Haris Haralambous</p> <p>Frederick University and Frederick Research Center, Nicosia, Cyprus</p> <p>Chris Meek</p> <p>Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada</p>

Data for non-parametric analysis_Cumaceans in a shallow water

<p>Data for a non-parametric analysis of Cumaceans distribution collected in a shallow water hydrothermal system of Punta Mita. They coming from three vent sites.</p>

opencc-zeroMar 2024View details →

Data from: Range-wide genetic analysis of an endangered bumble bee (Bombus affinis) reveals population structure, isolation by distance, and low colony abundance

<p>Declines in bumblebee species ranges and abundances are documented across multiple continents and have prompted the need for research to aid species recovery and conservation. The rusty patched bumblebee (<em>Bombus affinis</em>) is the first federally-listed bumblebee species in North America. We conducted a range-wide population genetics study of <em>B. affinis</em> from across all extant conservation units to inform conservation efforts. To understand the species' vulnerability and help establish recovery targets, we examined population structure, patterns of genetic diversity, and population differentiation. Additionally, we conducted site-level analysis of colony abundance to inform prioritizing areas for conservation, translocation, and other recovery actions. We find substantial evidence of population structuring along an east-to-west gradient. Putative populations show evidence of isolation by distance, high inbreeding coefficients, and a range wide male diploidy rate of ~15%. Our results suggest the Appalachians represents a genetically distinct cluster with high levels of private alleles and substantial differentiation from the rest of the extant range. Site-level analyses suggest low colony abundance estimates for <em>B. affinis</em> compared to similar datasets of stable, co-occurring species. These results lend genetic support to trends from observational studies suggesting B. affinis has undergone a recent decline and exhibits substantial spatial structure. The low colony abundances observed here suggest caution in overinterpreting the stability of populations even where <em>B. affinis</em> is reliably detected interannually. These results help delineate informed management units, provide context for the potential risks of translocation programs, and can help set clear recovery targets for this and other threatened bumblebee species.</p>

Data Analysis for "Effects of an Entomopathogenic Fungus on the Reproductive Potential of Drosophila males"

<p>Dataset and code used for data analyses and visualization in our manuscript:</p> <p>"Effects of an Entomopathogenic Fungus on the Reproductive Potential of Drosophila Males"</p> <p>by Aijuan Liao, Fanny Cavigliasso, Loriane Savary and Tadeusz J. Kawecki</p> <p>Published in Ecology and Evolution</p>