Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
518
datasets available to search
ShareScore release 0.9.0
Dataset results
518 results for “cycling data”
Data set for "Cyclophospholipids enable a protocellular life cycle"
<p>Publication in ACS Nano can be found <a href="https://doi.org/10.1021/acsnano.3c07706">here</a>.</p><p>Toparlaka ÖD, Sebastianelli L, Egas Ortunoc V, Karkic M, Szostak JW, Krishnamurthy R, Mansy SS (2023) Cyclophospholipids enable a protocellular life cycle. ACS Nano 17, 23772–23783. DOI: 10.1021/acsnano.3c07706</p>
data for publication "Dynamics of soil nitrogen and N-cycling-related genes following the application of biobased fertilizers"
<p>Dataset for the scientific publication titled "Dynamics of soil nitrogen and N-cycling-related genes following the application of biobased fertilizers" in the Journal <a href="https://www.sciencedirect.com/journal/applied-soil-ecology">Applied Soil Ecology</a>. </p><p><a href="https://doi.org/10.1016/j.apsoil.2023.105033">https://doi.org/10.1016/j.apsoil.2023.105033</a> </p>
Data from: Fog controls biological cycling of soil phosphorus in the Coastal Cordillera of the Atacama Desert
<p>In this study, we collected topsoil samples (0‒10 cm) from each of 54 subsites, including sites in direct adjacency (< 10 cm) and in 1 m distance to plants, along an aridity gradient across the Coastal Cordillera in the Atacama Desert. The soluble salts anions (NO<sup>3</sup><sup>‒</sup>, Cl<sup>‒</sup>, and SO<sub>4</sub><sup>2</sup><sup>‒</sup>) and cations (Ca<sup>2+</sup>, Na<sup>+</sup>, Mg<sup>2+</sup> and K<sup>+</sup>) were tested. And we performed soil sequential P fractionation and the oxygen isotope values of HCl-extractable P<sub>i</sub> (δ<sup>18</sup>O<sub>HCl</sub>-<sub>Pi</sub>). </p>
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>
Graded Incremental Test Data (Cycling, Running, Kayaking, Rowing): an open access dataset
<p><strong>Section 1: Introduction</strong></p> <p> </p> <p>Brief overview of dataset contents:</p> <ul> <li>Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland. </li> <li>835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)</li> <li>Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)</li> <li>Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )</li> <li>Test data (HR, VO<sub>2</sub> and lactate data) at rest and across a range of exercise intensities</li> <li>Derived physiological indices quantifying each individual’s endurance profile</li> </ul> <p> </p> <p>Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.</p> <p> </p> <p>All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.</p> <p> </p> <p><strong>Section 2: Testing protocols </strong></p> <p> </p> <p><strong>2.1: Cycling</strong></p> <p> </p> <p>A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min<sup>−1 </sup>within a permitted absolute range of 75 to 95 rev.min<sup>−1</sup>) and the test was terminated when a participant was no longer able to maintain a constant cadence.</p> <p> </p> <p>Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser<strong> </strong>(YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L<sup>−1</sup>. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L<sup>−1</sup>) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L<sup>−1</sup>) and analyser linearity was confirmed using either a 15 or 30 mmol.L<sup>-1</sup> standard solution (YSI, OH, USA).</p> <p> </p> <p>Gas exchange variables including respiration rate (Rf in breaths.min<sup>-1</sup>), minute ventilation (VE in L.min<sup>-1</sup>), oxygen consumption (VO<sub>2 </sub>in L.min<sup>-1</sup> and in mL.kg<sup>-1</sup>.min<sup>-1</sup>) and carbon dioxide production (VCO<sub>2 </sub>in L.min<sup>-1</sup>), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed<strong>,</strong> Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O<sub>2</sub>, 5% CO<sub>2</sub> and 79% N<sub>2</sub> (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.</p> <p> </p> <p>Following testing mean HR and mean VO<sub>2</sub> data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO<sub>2</sub> and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO<sub>2</sub> peak in L.min<sup>-1</sup> (absolute) and in mL.kg<sup>-1</sup>.min<sup>-1</sup> (relative) and VE peak in L.min<sup>-1</sup> were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.</p> <p> </p> <p><strong>2.2: Running protocol</strong></p> <p> </p> <p>A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h<sup>-1</sup>) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h<sup>-1</sup> every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.</p> <p> </p> <p>Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.</p> <p> </p> <p><strong>2.3: Rowing / kayaking protocol</strong></p> <p> </p> <p>A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload. </p> <p>Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.</p> <p> </p> <p><strong>3.1: Data analysis</strong></p> <p> </p> <p>Constructed graphical plots (HR, VO<sub>2</sub> and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at T<sub>Lac</sub>, HR at T<sub>Lac</sub>, [Lac] at T<sub>Lac</sub>, % of VO<sub>2</sub> peak at T<sub>Lac</sub>, % of HRmax at T<sub>Lac</sub>, load / velocity and HR at a nominal [Lac] of 2 mmol.L<sup>-1</sup>, load / velocity, VO<sub>2</sub> and [Lac} at a nominal HR of 160 beats.min<sup>-1</sup>. Load at T<sub>Lac</sub> was determined using segmental regression analysis. Two linear segments were plotted that minimised the squared sum of the residuals between the plotted points and best fit lines. The intersection of these the two linear segments was defined as the relevant breakpoint or threshold, (Raleigh <em>et al. </em>2018<em>. Int J Exerc Sci</em>, <strong>11</strong>, 391-403.</p> <p> </p> <p><strong>4.1: Terms of Use</strong></p> <p> </p> <p>The attached database is provided as a research or educational asset / tool for coach, athlete and exercise science / exercise medicine education and usage only.</p>
Data set for the study "Interplay between climate and carbon cycle feedbacks could substantially enhance future warming"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Interplay between climate and carbon cycle feedbacks could substantially enhance future warming" <br><a href="https://iopscience.iop.org/article/10.1088/1748-9326/adb6be" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.1088/1748-9326/adb6be</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo repository is organized as follows inside of <code>results.zip</code>:</p> <ul> <li>Figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment/emission scenario</strong>: <code>hist-aer</code>, <code>ssp126</code>, <code>ssp434</code>, and <code>ssp245</code><br><br> <ul> <li><strong>Equilibrium climate sensitivity</strong>: <code>ecs_2.0K</code>, <code>ecs_2.5K</code>, <code>ecs_3.0K</code>, <code>ecs_3.5K</code>, <code>ecs_4.0K</code>, <code>ecs_4.5K</code>, and <code>ecs_5.0K</code><br><br> <ul> <li><strong>Experiment: </strong><code>comp</code>, <code>comp_fix_ch4</code>, <code>comp_fix_co2_ch4</code>, <code>comp_ssp_co2_ch4</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), ocean (<code>ocn</code>), land (<code>lnd</code>), sea ice (<code>sic</code>), carbon dioxide (<code>co2</code>), methane (<code>ch4</code>)<br><br></li> <li><strong>File type</strong>: for some experiments, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data (<code>*.nc</code>)<br><br></li> <li>Note: <code>comp_ssp_co2_ch4</code> are the CLIMBER-X runs which used prescribed concentrations (rather than emissions) and is only available for ECS 3°C<br><br></li> <li>Note: <code>comp_fix_ch4</code> and <code>comp_fix_co2_ch4</code> is only available for ECS 2°C, 3°C, and 5°C (as shown in Fig. 4 in the manuscript)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Code and Data for Li et al. Characterizing the Speed of Chemical Cycling in the Atmosphere
<p>This Zenodo archive contains the code and data used in the manuscript "Characterizing the Speed of Chemical Cycling in the Atmosphere"</p>
Research Data Management Life Cycle
<p>An overview of the research data management life cycle with proper licensing.</p> <p> </p>
Data-base for : 'Partitioning carbon sources between wetland and well-drained ecosystems to a tropical first-order stream - Implications to carbon cycling at the watershed scale (Nyong, Cameroon)'
<p>Dataset of carbon (pCO2, TA, DIC, DOC, POC) and ancillary parameters (water temperature, oxygen saturation, pH, specific conducitivity) in ground and surface waters of the Nyong watershed (Cameroon). The dataset covers one entire year (in 2016) and thus allows describing the varability of carbon and ancillary paramaters concentrations induced by seasons.</p>
Data and code to explore annual cycle schedule adjustments in a long distance migrant
<p>Matching the timing of annual cycle events with the required resources can have crucial consequences for individual fitness. But as the annual cycle is comprised of sequential events, a delay at any point may be carried over to the subsequent stage (or more, in a domino effect) and negatively influence individual performance. To investigate how migratory animals navigate their annual schedule, and where and when it may be adjusted, we used full annual cycle data of 38 Icelandic whimbrels <em>Numenius phaeopus islandicus</em> tracked over 7 years – a subspecies that typically performs long-distance migrations to West Africa. We found that individuals apparently used the wintering sites to compensate for delays that mostly arose due to previous successful breeding, and a domino effect was observed from spring departure to laying date, with the potential to affect breeding output. However, the total time saved during all stationary periods is apparently enough to avoid interannual effects between breeding seasons. These findings highlight the importance of preserving good quality non-breeding sites in which individuals may adjust annual schedules and avoid potentially adverse effects of arriving late at the breeding grounds.</p>
Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data
<p>We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.</p>
Triaxial cycling loading of Westerly granite: Mechanical and Ultrasonic data
<p>Data obtained from triaxial cyclic loading experiments conducted on dry Westerly granite at confining pressure ranging from 40 to 120 MPa.</p> <p>Text files (.txt) are raw, unprocessed time series of mechanical data including load, shortening and radial and axial strain gauge measurements. Except noted, units are in Volts. Strain gauge voltages can be converted into strain using gauge factor of 2.11 and amplification factor of 40. External shortening can be converted to sample shortening using a machine stiffness of 480 kN/mm.</p> <p>Raw ultrasonic data are combined in folders named BSF*. Data format is binary, and can be read using ASC ltd. software "Insite".</p> <p>Files "sensors.txt" contain ultrasonic sensor positions and time offset corrections.</p>
Data from: Vibrational Transportation on a Platform Subjected to Sinusoidal Displacement Cycles Employing Dry Friction Control
<p>Data from the paper "Vibrational Transportation on a Platform Subjected to Sinusoidal Displacement Cycles Employing Dry Friction Control", <a href="https://doi.org/10.3390/s21217280">https://doi.org/10.3390/s21217280</a></p> <p>Currently used vibrational transportation methods are usually based on asymmetries of geometric, kinematic, wave, or time types. This paper investigates the vibrational transportation of objects on a platform that is subjected to sinusoidal displacement cycles, employing periodic dynamic dry friction control. This manner of dry friction control creates an asymmetry, which is necessary to move the object. The theoretical investigation on functional capabilities and transportation regimes was carried out using a developed parametric mathematical model, and the control parameters that determine the transportation characteristics such as velocity and direction were defined. To test the functional capabilities of the proposed method, an experimental setup was developed, and experiments were carried out. The results of the presented research indicate that the proposed method ensures smooth control of the transportation velocity in a wide range and allows it to change the direction of motion. Moreover, the proposed method offers other new functional capabilities, such as a capability to move individual objects on the same platform in opposite directions and at different velocities at the same time by imposing different friction control parameters on different regions of the platform or on different objects. In addition, objects can be subjected to translation and rotation at the same time by imposing different friction control parameters on different regions of the platform. The presented research extends the classical theory of vibrational transportation and has a practical value for industries that operate manufacturing systems performing tasks such as handling and transportation, positioning, feeding, sorting, aligning, or assembling.</p>
DATA - Modern manufacturing enables magnetic field cycling experiments and parahydrogen induced hyperpolarization with a benchtop NMR
<p>Datasets and software for the publication "Modern manufacturing enables magnetic field cycling experiments and parahydrogen induced hyperpolarization with a benchtop NMR"</p>
Student Qualitative Data_FoodFactory-4-Us Cycles 1-4
<p>Qualitative data from students’ initial understanding, contributions and expectations of their competences and compare these with their final understanding of own contributions and competence development as participants of <a href="https://www.iseki-food.net/foodfactory-4-us">FoodFactory-4-Us</a> from 2018-2022.</p>
Process simulation-based inventory data for the perovskite single-junction, Silicon (PERC) and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles
<p>Process simulation-based inventory data (mass and energy balances) for the perovskite single-junction, silicon (PERC architecture), and four-terminal perovskite/silicon tandem solar photovoltaic system life cycles. The file "0 Overview of simulation flowsheets.xlsx" contains images of the 11 flowsheets that constitute the perovskite/silicon tandem simulation model, which encompasses the perovskite single-junction and silicon (PERC) simulation models. For each unit process shown in each of the flowsheet images, the corresponding Excel file in this repository (with the same name) contains the detailed mass and energy balances, as well as full compositions and thermochemical properties of all streams and the compounds in them. That is, streams are not assumed to consist of pure elements simply moving through the system together, but rather taking into account that streams consist of compounds in solution, which have different thermochemical properties than simple mixtures of the elements involved.</p> <p>Nine additional data files, the names of which start with "Inventory - " contain summarized inventory data for the production of 1000 perovskite single-junction, silicon (PERC), and silicon/perovskite tandem PV modules, each with no Si recycling (i.e. zero circularity), 50% Si recycling, and 100% Si recycling (i.e. full Si circularity).</p>
Earthquake Cycle Deformation Associated with the 2021 Mw 7.4 Maduo (Eastern Tibet) Earthquake: An Intrablock Rupture Event on a Slow-Slipping Fault from Sentinel-1 InSAR and Teleseismic Data
<p>Coseismic slip models of the 2021 Mw 7.4 Maduo (eastern Tibet) earthquake derived from Sentinel-1 InSAR and teleseismic data.</p> <p>Interseismic eastward and vertical velocity and maximum shear strain rate fields.</p> <p>Citations:</p> <p>Fang, J., Ou, Q., Wright, T. J., Okuwaki, R., Amey, R. M. J., Craig, T. J., et al. (2022). Earthquake cycle deformation associated with the 2021 M<span>W </span>7.4 Maduo (eastern Tibet) earthquake: An intrablock rupture event on a slow-slipping fault from Sentinel-1 InSAR and teleseismic data. Journal of Geophysical Research: Solid Earth, 127, e2022JB024268. <span>https://</span>doi.org/10.1029/2022JB024268</p> <p>Fang, J., Ou, Q., Wright, T. J., Okuwaki, R., Amey, R. M. J., Craig, T. J., et al. (2022). Earthquake cycle deformation associated with the 2021 M<span>W </span>7.4 Maduo (eastern Tibet) earthquake: An intrablock rupture event on a slow-slipping fault from Sentinel-1 InSAR and teleseismic data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7215161<span>.</span></p>
Data from: Extensive epigenetic reprogramming during the life cycle of Marchantia polymorpha
<p>This GFF contains M. polymorpha genes (taken from the official release v3.1, Phytozome 11, phytozome.jgi.doe.gov) and transposable elements and repeats identified in the study (see Materials and Methods).</p>
Dataset: Problem-centred interviews results for Matching Data Life Cycle and Research Processes in Engineering Sciences
<p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Measuring the Total Photon Economy of Molecular Species through Fluorescent Optical Cycling: Raw Data
<p>Data acquired in the the paper titled: <br><br><a name="_Hlk150935558"></a><strong><span>Measuring the Total Photon Economy of Molecular Species through Fluorescent Optical Cycling </span></strong></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.