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6,467 results for “process”
GLR Best Practices Process and Results
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ARCHITECTURAL FORMATION PROCESSES OF KARSHI FORTRESS AREAS
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BSC Post-processed Sub-seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the subeasonal climate prediction system NCEP-CFSv2.</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term, while a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: National Centers for Environmental Prediction (NCEP) CFSv2, post-processed by BSC (create a lagged ensemble, downscaling and calibration).</p> </li> <li> <p>Issue frequency: Weekly (Initialization every Thursday, post-processed prediction every Friday).</p> </li> <li> <p>Lead times: weeks 1 to 4 (e.g. For a forecast issued on Friday 4th November, forecasts will be weekly averages starting the following Monday-Thursday and the 4 following weeks (e.g. week 1 will be 8th-15th November). The initialization date is indicated in the name of each file (e.g. 20211104). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, and incoming solar radiation.</p> </li> <li> <p>Ensemble size: 48 members</p> </li> <li> <p>Postprocessing: Create a lagged ensemble of 48 ensemble members, downscaling from the original (1°x 1°) resolution to 0.1°x 0.1° for the three domains and weekly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ‘douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p>The specific latitude and longitude indices to extract the predictions corresponding to each vitiGEOSS site are indicated in Table 2.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20211104.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (Always a Thursday).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20211104.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (Always a Thursday).</p> <p>It contains the 48 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania_percentiles_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33rd percentile is the boundary between below-normal and normal, and the 66th percentile is the boundary between the normal and above-normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania_skill_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the week of the year for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization and lead time (valid week).</p>
Factors Influencing Process Improvement in Chinese Multinational Corporations: A Delphi Method Study
<p>Delphi Round 1 Data</p>
processed-csn-dataset
<p>This is the dataset of CMDeSum.</p>
MySQL dump for finding optimal parameters for data augmentation techniques in publication "Leveraging Data Augmentation for Process Information Extraction"
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SOCIO-ECONOMIC MECHANISM OF INCREASING THE EFFICIENCY OF INNOVATION PROCESSES AT INDUSTRIAL ENTERPRISES
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Processed mass spectrometry data - systematic identification of allosteric effectors in Escherichia coli metabolism
<p>MATLAB files of processed mass spectrometry data, i.e. full data table after peak picking, annotation and quantification. Additionally, for each of the tested enzymes, the relevant ion traces of substrates and products are extracted, sorted by timepoint and replicate and saved in separate tables.</p>
List of Figures for the planned paper entitled "The influence of surface chemistry of activated carbons on adsorption and freezing/melting processes"
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THE ROLE OF THE TEACHER'S PERSONALITY IN THE MODERN EDUCATIONAL PROCESS
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Supplementary data to: Unravelling driving conditions of rock and ice avalanches and resulting cascading processes in High Mountain Asia
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Chalcohalide Antiperovskite Thin Films with Visible Light Absorption and High Charge-Carrier Mobility Processed by Solvent-Free and Low-Temperature Methods
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data for "Importance of Strains in Kinetic Energy Conversion for Submesoscale Processes from an Anisotropic Perspective "
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Luminal STIM1 Mutants that Cause Tubular Aggregate Myopathy Promote Autophagic Processes
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Plant-environment feedback processes in drylands
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HISTORY AND CURRENT PROCESS OF FORENSIC PSYCHOLOGICAL EXPERTISE
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USING THE BASIS OF PEDAGOGICAL SKILLS IN ORGANIZING THE EDUCATIONAL PROCESS
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Process Behavior Corpus and Benchmarking Datasets
<p>A corpus of process behaviors and benchmarking datasets for semantics-aware process mining tasks.</p> <p>Files:</p> <ul> <li><strong>process_behavior_corpus.csv</strong>: the text corpus, which contains the behavior allowed by process models as sequences of activities (column <em>string_traces)</em>.</li> <li><strong>T_SAD.csv:</strong> A benchmark dataset generated from the corpus to assess the following task: Given a trace σ, decide if σ is a valid execution of the underlying process or not, without knowing the behavior allowed in the process.<br>Each row contains a trace (column <em>trace</em>) with a corresponding label (column <em>anomalous</em>) indicating whether the trace represents a valid execution of the underlying process. The set of activities that can occur in the process are also given (column <em>unique_activities</em>).</li> <li><strong>A_SAD.csv:</strong> A benchmark dataset generated from the corpus to assess the following task: Given an eventually-follows relation ef = a ≺ b of<br>a trace σ, decide if ef represents a valid execution order of the two activities a and b that are executed in a process or not, without knowing the behavior allowed in the process.<br>Each row contains an eventually-follows relation (column <em>eventually_follows</em>) with a corresponding label (column <em>out_of_order</em>) indicating wether the two activities of the relation were executed in an invalid order (TRUE) or in a valid order (FALSE) according to the underlying process (model). The set of activities that can occur in the process are also given (column <em>unique_activities</em>).</li> <li><strong>S_NAP.csv:</strong> A benchmark dataset generated from the corpus to assess the following task: Given an event log L and a prefix p_k of length k, with 1 < k, predict the next activity a_k+1<br>Each row contains a trace prefix (column <em>prefix</em>) with a corresponding next activity (column <em>next</em>) indicating the activity that should be performed next after the last activity of the prefix according to the trace from which the prefix was generated. The set of activities that can occur in the process are also given (column <em>unique_activities</em>).</li> <li><strong>S-PMD.csv:</strong> A benchmark dataset generated from the corpus to assess the following tasks: <ul> <li>Given a set of possible activities (column <em>unique_activities</em>), generate a difectly follows graph (column <em>dfg</em>) that captures the trace semantics of the process model. </li> <li>Given a set of possible activities (column <em>unique_activities</em>), generate a simple process tree (column <em>pt</em>) that captures the trace semantics of the process model.</li> </ul> </li> </ul> <p>Reference and legal info:</p> <p>The corpus and the benchmark datasets are generated using the SAP-SAM dataset:</p> <p>Kampik, T., Warmuth, C., Sola, D., Schäfer, B., Axworthy, L., Ivarsson, E., Ouda, K., & Eickhoff, D. (2022). SAP Signavio Academic Models (0.5.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7012043" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7012043</a><br><br>The SAP-SAM dataset is published with a specific license (see "Rights"), which, therefore, also applies to the data published in this record.</p> <p><strong>THE DATASETS AND ASSOCIATED EVALUATION EXPERIMENTS ARE DESCRIBED IN <a href="https://arxiv.org/pdf/2407.02310">THIS</a> PAPER.</strong></p> <p><strong>IN <a href="https://github.com/a-rebmann/llms4pm">THIS</a> REPOSITORY YOU FIND THE CODE AND RAW RESULTS OF EVALUATION EXPERIMENTS USING VARIOUS OPEN SOUCE LLMs TO SOLVE THE TASKS</strong></p>
Process Mining Algorithm for Online Intrusion Detection System
<p>The pre-processed data we used in the paper.</p>
Processed GPS tracks for breeding Herring Gulls from four colonies in the eastern Gulf of Maine, Canada
<p>Opportunist gulls use anthropogenic food subsidies, which can bolster populations, but negatively influence sensitive local ecosystems and areas of human settlement. In the eastern Gulf of Maine, Canada, breeding herring gulls <em>Larus argentatus </em>have access to resources from aquaculture, fisheries, and mink farms, but the relative influence of industry on local gull populations is unknown. In 2014, 2015, and 2019, we acquired and processed tracking data from GPS devices on 39 incubating herring gulls at four colonies with access to resources within the Canadian portion of the eastern Gulf of Maine marine and watershed ecosystem: three island colonies in Nova Scotia: Bon Portage (43.47°N, 65.75°W), Whitehead (43.66°N, 65.87°W), Brier (44.26°N, 66.38°W), and one island colony in New Brunswick: Kent (44.58°N, 66.76°W). The data in this repository were processed according to the methods provided in the article indicate below, and were used to address three main objectives (a) assess use of natural and anthropogenic habitats by herring gulls from multiple colonies, (b) evaluate variation among colonies in use of distinct resource types within these habitats, and (c) highlight areas of high gull:industry interaction. The results are published in the journal Wildlife Biology (doi: 10.2981/wlb.00804).</p>
ScienceDex guides
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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.