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538 results for “ECS”
Differential Evolution data from eCS (EVONANO)
<p>Data-set produced by and depicted in https://doi.org/10.1007/978-3-030-76928-4_17</p>
Novelty search data from eCS (EVONANO)
<p>Data-set produced and depicted in https://doi.org/10.1016/j.imu.2020.100347</p>
Metameric evolutionary data from eCS (EVONANO) including application times
<p>Data-set produced by and depicted in <a href="https://doi.org/10.1016/j.cmpb.2020.105886">https://doi.org/10.1016/j.cmpb.2020.105886</a></p>
Data set EC Age
<p>Individual raw data for stages of entheseal changes for 30 appendicular entheses in four identified collections. </p> <p>For the method used to record the changes, see Villotte 2006</p> <p>For a presentation of the collections, see Villotte 2009, Villotte et al. 2010.</p> <p>Villotte S. 2006. Connaissances médicales actuelles, cotation des enthésopathies : nouvelle méthode. <em>Bulletins et Mémoires de la Société d’Anthropologie de Paris</em> <strong>n.s., 18</strong>: 65–85.</p> <p>Villotte S. 2009. <em>Enthésopathies et activités des hommes préhistoriques Recherche méthodologique et application aux fossiles européens du Paléolithique supérieur et du Mésolithique</em>. BAR International Series 1992, Archaeopress: Oxford, UK.</p> <p>Villotte S, Castex D, Couallier V, Dutour O, Knüsel CJ, Henry-Gambier D. 2010. Enthesopathies as occupational stress markers: evidence from the upper limb. <em>American Journal of Physical Anthropology</em> <strong>142</strong>: 224–234.</p>
EC Ferramentas Forja 01
Conjunto de ferramentas de oficina e forja do Museu da Cana (Engenho Central) - Pontal - SP - Brasil Source: Objaverse 1.0 / Sketchfab
ec-filter dataset
<p>This contains two datasets used for demonstrate the dangers and solutions for post search filter in crosslinking mass-spectrometry. A dataset of search results for mycoplasma pneumonia acquisitions and escherichia coli. Both where searched against a combined database of all e.coli and mycoplasma pneumonia proteins.</p> <p>These are results without any cut-off and are the base of testing if a filter or processing step is actually affecting decoys differently then target false positives. The idea being that these search results provide an decoy independent set of known false positive matches; all matches involving e.coli peptides to mycoplasma spectra and all matches involving mycoplasma pneumonia peptides to e.coli spectra. In the original use case the data where used to detect if a filter, that uses match external information to filter individual matches, results in an underrepresentation of decoys when compared to these secondary known false positives and how at least no contradiction was found when applying teh ec-filter style of applying the information.</p> <p> </p> <p>The second dataset is a set of FDR results for 2% unique residue pair FDR of an yeast 26S Proteasome acquisition run with and without using the ec-filter and each of tzhese with and without xiFDR in built boosting. The spectra where searched against increasingly larger databases to show the effect of filtering the results depending on the database size – both in terms of present and assumed non-present proteins.</p> <p> </p>
Eastern Canada's Unified Heritage Building Database (EC-UHBD)
<p>This database merges details on heritage-listed buildings from multiple Eastern Canadian registers. A focus on structural characteristics available were able to be harmonized in this database including: construction date, number of stories, building materials, location, use type and links to the orginal building.</p> <p><strong>Data are still preliminary and in review.</strong></p> <p>The building registers are listed here: </p> <ul> <li>Parks Canada Federal Database of Historic Buildings (Parks Canada, 2018)</li> <li>Historic Places (<em>Parks Canada</em>, 2001)</li> <li>Répertoire du patrimoine du Québec (Québec, 2013.</li> <li>Grande Répertoire du patrimoine bâti de Montréal (<em>Montreal.ca</em>, 2021a)</li> <li>L’inventaire des propriétés municipales d’intérêt patrimonial (<em>Montreal.ca</em>, 2021b)</li> <li>Le patrimoine du Vieux-Montréal en détail (<em>Montreal.ca</em>, 2021c)</li> <li>Répertoire du patrimoine bâti (<em>Ville de Québec</em>, 2023)</li> <li>Ontario Trust Database (<em>Ontario Heritage Trust</em>, 2023)</li> <li>Lieux de culte du Québec (Conseil du patrimoine religieux du Québec, 2011)</li> </ul>
Job Shop Scheduling instances (SS + RD + EC)
<h1>Instance Structure</h1> <h2>📁 Definition:</h2> <p>For example, the filename `5_5_0_2_5_0.json` is defined as follows: 5 represents the number of jobs, 5 represents the number of machines, 0 indicates the type of distribution (0 = exponential, 1 = normal, 2 = uniform), 2 indicates the type of release and due date (0 = no restriction, 1 = by job, 2 = by operations), 5 denotes the quantity of speed scaling options for each machine, and 0 is the instance number.</p> <h2>📊 Job IDs: </h2> <p>An array of integers representing the job IDs (from 0 to 4).</p> <p><code>"nbJobs": [0, 1, 2, 3, 4]</code></p> <h2>🛠 Number of Machines: </h2> <p>An array of integers representing the number of machines (from 0 to 4). Each machine refeer an operation of a job that should be procedeed.</p> <p><code>"nbMchs": [0, 1, 2, 3, 4]</code></p> <p> </p> <h2>⏱️ Time and Energy:</h2> <p>An array of objects, each containing information about a job processed on a machine, including multiple speed-scaling options. Each job object includes the job ID, operations (with operation IDs as keys), and details such as processing time, energy consumption, release date, and due date.</p> <p><code>"timeEnergy": [</code><br><code> {</code><br><code> "jobId": 0,</code><br><code> "operations": {</code><br><code> "1": {</code><br><code> "speed-scaling": [</code><br><code> {"procTime": 209, "energyCons": 12},</code><br><code> ...</code><br><code> ],</code><br><code> "release-date": 0,</code><br><code> "due-date": 31</code><br><code> },</code><br><code> ...</code><br><code> }</code><br><code> },</code><br><code> ...</code><br><code>]</code></p> <h2>📅 Due Dates and Release Dates:</h2> <p>Within each operation in the `timeEnergy` array, the `release-date` represents the release date of the operation (in milliseconds) and the `due-date` represents the due date of the operation (in milliseconds).</p> <h2>🔄 Speed Scaling Options:</h2> <p> Each operation contains multiple speed-scaling options, providing different combinations of processing times and energy consumption levels.</p> <p><code>"speed-scaling": [</code><br><code> {"procTime": 209, "energyCons": 12},</code><br><code> {"procTime": 52, "energyCons": 59},</code><br><code> {"procTime": 40, "energyCons": 67},</code><br><code> {"procTime": 32, "energyCons": 72},</code><br><code> {"procTime": 30, "energyCons": 74}</code><br><code>]</code><br><br></p> <h2>🧩 Example:</h2> <p>Here is an example of how the data is structured for a specific job and its operations:</p> <p><br><code>{</code><br><code> "nbJobs": [0, 1, 2, 3, 4],</code><br><code> "nbMchs": [0, 1, 2, 3, 4],</code><br><code> "timeEnergy": [</code><br><code> {</code><br><code> "jobId": 0,</code><br><code> "operations": {</code><br><code> "1": {</code><br><code> "speed-scaling": [</code><br><code> {"procTime": 209, "energyCons": 12},</code><br><code> {"procTime": 52, "energyCons": 59},</code><br><code> {"procTime": 40, "energyCons": 67},</code><br><code> {"procTime": 32, "energyCons": 72},</code><br><code> {"procTime": 30, "energyCons": 74}</code><br><code> ],</code><br><code> "release-date": 0,</code><br><code> "due-date": 31</code><br><code> },</code><br><code> "3": {</code><br><code> "speed-scaling": [</code><br><code> {"procTime": 135, "energyCons": 25},</code><br><code> {"procTime": 40, "energyCons": 67},</code><br><code> {"procTime": 30, "energyCons": 74},</code><br><code> {"procTime": 28, "energyCons": 75},</code><br><code> {"procTime": 23, "energyCons": 79}</code><br><code> ],</code><br><code> "release-date": 32,</code><br><code> "due-date": 72</code><br><code> },</code><br><code> ...</code><br><code> }</code><br><code> },</code><br><code> ...</code><br><code> ]</code><br><code>}</code><br><br></p>
EC-MS dataset of electrocatalytic transformations of butane on Pt
<p>This distribution provides the code and reference data for the manuscript of "Understanding the interplay between electrocatalytic C(sp3)‒C(sp3) fragmentation and oxygenation reactions".</p> <p>The code can be excecuted using Python version 3.8. </p> <p><strong>Data</strong></p> <p>The distribution includes an <code>.xlsx</code> file with reference mass spectra data and experimental data in <code>.tsv</code> format.</p> <p> </p> <p><strong>Usage</strong></p> <ol> <li>Ensure Python 3.8 and Jupyter Notebook are installed.</li> <li>Execute each cell in the notebook <code>example.ipynb</code>. A pop-up window will prompt you to upload your experimental mass spectra data; select your file accordingly. The cells are organized as: <ol> <li><code>Load Data</code>: This step loads the reference spectra data.</li> <li><code>Preprocess Data</code>: This step removes background signals and smooths the signal.</li> <li><code>Optimization</code>: This step uses constrained least squares optimization to reconstruct spectra and predict flux.</li> <li><code>Plot</code>: This step displays the spectra reconstruction and flux prediction.</li> <li><code>File Output</code>: This step saves the predicted flux and reconstructed spectra to files.</li> </ol> </li> </ol>
Downscaled 11 km CESM2 data used in CESM2 Greenland SMB evaluation paper (HIST-EC)
<p>Monthly output from CESM2 simulation HIST-EC over the period 1960-1999, downscaled to the 11 km RACMO grid using elevation class output.</p> <p>Variables: EFLX_LH_TOT, FGR, FIRA, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSR, QICE, QICE_MELT, QRUNOFF, QSNOFRZ, QSNOMELT, QSOIL, RAIN, RAIN_FROM_ATM, RH2M, SNOW, SNOW_FROM_ATM, TG, TSA, TSKIN, U10</p>
Native CESM2 data used in CESM2 Greenland SMB evaluation paper (HIST-EC)
<p>Monthly output from CESM2 simulation HIST-EC over the period 1960-1999, from both CAM and CLM model components.</p> <p>CAM variables: FLDS, FLNS, FSDS, FSNS, LHFLX, PRECSC, PRECSL, PRECT, QFLX, SHFLX, TREFHT, TS, U10</p> <p>CLM variables: EFLX_LH_TOT, FGR, FIRA, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSR, QICE, QICE_MELT, QRUNOFF, QSNOFRZ, QSNOMELT, QSOIL, RAIN, RAIN_FROM_ATM, SNOW, SNOW_FROM_CAM, TG, TSA, TSKIN, U10</p>
Parental Engagement and Relationships (PEAR) in Early Childhood (EC). Implementation study: Parents' views
<p>Data and codebook files related to collecting parents' views within the implementation study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC). The following files are shared below.</p> <p>- Full quantitative data and selected qualitative data (Microsoft Office Excel file)</p> <p>- Codebook for coding the qualitative data (pdf file)</p> <p>Files are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.</p>
Equilibrium climate sensitivity experiments using EC-Earth3-LR model — Surface Air Temperature data
<p>Three experiments was conducted using a EC-Earth model with the EC-Earth3-LR configuration (REF), which couples atmosphere, land, ocean and sea-ice components. First, we performed a pre-industrial (PI) control simulation (E280) using pre-industrial forcing, holding atmospheric constituents constant at 1850 levels (e.g., CO<sub>2</sub> concentration at 280 ppm). This simulation was initialized by a pre-run steady restart file (from a 500-year pre-industrial control simulation) and ran for 2000 years. We also conducted two sensitivity experiments (E400 and E560) by adjusting the CO<sub>2</sub> concentration to 400 ppm and 560 ppm, respectively, at the start year of the E280 experiment, and continued for over 3000 years (3069 years for E400, and 3013 years for E560). For our statistical analysis, we only considered the integration periods after the spin-up, using the last 2000-year outputs from the three simulations.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2023).</p> <p>Cao, N., Zhang, Q., Wang, Z., Power, K.E., & Liu, C. (2023). The non-negligible impact of internal multi-centennial climate variability on estimating equilibrium climate change. Submitted to <em>Geophysical Research Letters</em>.</p> <p> </p> <p><strong>Model configuration</strong><br> Time periods: 2000-year time slice for all three experiments<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for Surface Air Temperature data.</p>
Simulations for manuscript Unravelling the forcing and feedbacks contributing to Pliocene Arctic warming in EC-Earth simulations
<p>Nine PlioMIP2 experiments were conducted to investigate the mid-Pliocene Arctic climate. These experiments considered three CO2 levels (280, 400 and 560 ppm), modern or Pliocene ice sheet conditions in Greenland and Antarctica and either prescribed or dynamic vegetation. Simulations were performed by the EC-Earth3-LR Veg climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations. Simulations were run for 1000 years. The last 200 years were used for analysis after the model reached an equilibrium as measured by global surface air temperature trend less than 0.05 K per century. </p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).</p>
EC data for "Temperature extremes of 2022 reduced carbon uptake by forests in Europe"
<p>Used Eddy Covariance data following the methods described in "Temperature extremes of 2022 reduced carbon uptake by forests in Europe"</p> <p> </p>
EC-MS data accompanying the EC-MS quantification tutorial
<p>EC-MS data accompanying the EC-MS quantification tutorial published on ixdat/tutorials: https://github.com/ixdat/tutorials</p>
Efficacy and Safety of Everolimus+EC-MPS After Early CNI Elimination vs EC-MPS +Tacrolimus in Renal Transplant Recipients
ClinicalTrials.gov study NCT00965094. IPD Sharing: Not stated. Countries: 1. Publications: 1.
EC PK in Women With Normal and Obese BMI
ClinicalTrials.gov study NCT02689804. IPD Sharing: NO. Countries: 1. Publications: 3.
RAPID EC - Rct Assessing Pregnancy With Intrauterine Devices for EC
ClinicalTrials.gov study NCT02175030. IPD Sharing: NO. Countries: 1. Publications: 4.
Same Day Oral EC and Implant Initiation
ClinicalTrials.gov study NCT04678817. IPD Sharing: NO. Countries: 1. Publications: 2.
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