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722 results for “use case”
Fig. 1 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery
Fig. 1. The cumulative number of Indonesian polychaete species described (the black solid line), projected to the end of the 21st century with a 95% probability. The upper and lower red dashed lines, as well as the red solid line, are the projected species discovery trends assuming great, little, and medium taxonomic efforts, respectively, being made to shift the pre-existing trend.
metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data - use case
<p>Data products returned by <a href="https://github.com/emo-bon/MetaGOflow">metaGOflow</a> (<a href="https://github.com/emo-bon/MetaGOflow/releases/tag/v1.0.0">v1.0.0</a>) and packed as a Research Object (RO) Crate, when performed with:</p> <ul> <li>a <strong>seawater metagenomic sample </strong>(TARA OCEAN, <a href="https://www.ebi.ac.uk/ena/browser/view/ERR599171">ERR599171</a>)</li> <li>a <strong>fish gut </strong>sample (<a href="https://www.ebi.ac.uk/ena/browser/view/ERR4765907">ERR4765907</a>)</li> <li>a<strong> human gut </strong>sample (<a href="https://www.ebi.ac.uk/ena/browser/view/SRR9654976">SRR9654976</a>)</li> </ul> <p>This Zenodo repo accompanies the metaGOflow paper and more about the analysis of this sample can be found there.</p> <p>You can also have a look at some visual components of the workflow at this <a href="https://data.emobon.embrc.eu/MetaGOflow/">GitHub page</a>. </p> <p>The source code of metaGOflow is available through <a href="http://github.com/emo-bon/MetaGOflow">GitHub</a>.</p>
oncoEnrichR use cases
<p><strong>Use case analyses - oncoEnrichR</strong></p> <p>This Zenodo repository hosts two use case output reports from <a href="https://github.com/sigven/oncoEnrichR">oncoEnrichR</a>, a dedicated tool for geneset interpretation in cancer.</p> <p>Directory structure:</p> <ul> <li><strong><em>reports </em></strong>- holds pre-computed HTML reports and Excel woorkbooks for two use cases <ul> <li>FGFR1 network (protein proximity screen) <ul> <li><em>fgfr1_network_oncoEnrichR.v1.4.1.html</em></li> <li><em>fgfr1_network_oncoEnrichR.v1.4.1.xlsx</em></li> </ul> </li> <li>EGFRi drug resistance drivers (CRISPR screen) <ul> <li><em>egfr_drug_resistance_oncoEnrichR.v1.4.1.html</em></li> <li><em>egfr_drug_resistance_oncoEnrichR.v1.4.1.xlsx</em></li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><em><strong>input </strong></em>- holds input data for the two use cases<br> 1. fgfr1_network_input.tsv<br> 2. egfr_drug_resistance_input.tsv</li> </ul> <p> </p> <ul> <li><em><strong>docker</strong></em> - holds input and code for re-running use case analyses through<br> a Docker container <ul> <li>Instructions <ul> <li><a href="https://docs.docker.com/get-docker/">Install Docker</a> on your local computer/client (OS X/Linux)</li> <li>Download the <em>input</em> and <em>output</em> directory of the <em>docker</em> directory to your local computer/client</li> <li>Pull the Docker image with the following Docker command on the command-line: <ul> <li><em>docker pull sigven/oeusecase:latest</em></li> </ul> </li> <li>Run the use case analyses with the following Docker command: <ul> <li><em>docker run --rm -ti -v <LOCAL_DIRECTORY>/input:/input -v /LOCAL_DIRECTORY/output:/output sigven/oeusecase:latest</em></li> <li><strong>NOTE</strong>: replace <em>LOCAL_DIRECTORY</em> with the full path on your local computer where you have downloaded the <em>input</em> and <em>output</em> folders</li> <li>The above command will generate HTML reports and Excel workbooks for the two use cases in the <em>output</em> directory</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Investigating the Use of AI-Generated Exercises for Beginner and Intermediate Programming Courses: A ChatGPT Case Study
<p>In recent years, artificial intelligence (AI) has been increasingly used in education and supports teachers in creating educational material and students in their learning progress. AI- driven learning support has recently been further strengthened by the release of ChatGPT, in which users can retrieve expla- nations for various concepts in a few minutes through chat. However, to what extent the use of AI models, such as ChatGPT, is suitable for the creation of didactically and content-wise good exercises for programming courses is not yet known. Therefore, in this paper, we investigate the use of AI-generated exercises for beginner and intermediate programming courses in higher education using ChatGPT. We created 12 exercise sheets with ChatGPT for a beginner to intermediate programming course focusing on the objects-first approach. We report our process, prompts, and experience using ChatGPT for this task and outline good practices we identified. The generated exercises are assessed and revised, primarily using ChatGPT, until they met the requirements of the programming course. We assessed the quality of these exercises by using them in our course as external teaching assignment at the University of Education Ludwigsburg and let the students evaluate them. Results indicate the quality of the generated exercises and the time-saving for creating them using ChatGPT. However, our experience showed that while it is fast to generate a good version of an exercise, almost every exercise requires minor manual changes to improve its quality.</p>
H2020 Platone German Demonstrator Use Case 1, 2, 3 and 4 Measurement Data
<p>This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). This dataset contains measurement data and processed data relevant for the evaluation of UseCases (UCs) applied in the field test side.</p> <p><strong>Background - Field Test Setup</strong></p> <p>The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single Point of Common Coupling (PCC). i.e., a secondary substation that includes a transformer with sensors on the LV busbar to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity. </p> <p><strong>Description of data set:</strong></p> <p>p_tei - arithmetic mean of measured power exchange at PCC (Total residual power exchange Export/Import) measured in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</p> <p>p_tcb - arithmetic mean of measured charging/discharging power of CBES in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</p> <p>p_tcb_set – triggered charging/discharging power of CBES</p> <p>p_tei_c - Computed power exchange at PPC. That value indicates the value p_tei if no UC would have been applied (baseline).</p> <p>e_im –cumulated measured energy import (from MV grid into LV grid)</p> <p>e_ex - cumulated measured energy export (from LV grid into MV grid)</p> <p>soc – State Of Charge of CBES</p> <p>soc_max – maximum permissible SOC of CBES</p> <p>soe - State Of Energy of CBES</p> <p>soc_min – minimum permissible SOC of CBES</p> <p>id – ID of UC that is active at point of time</p> <p>setpoint - Charging/discharging power for CBES triggered by EMS (ALF-C) during active an UC</p> <p>subtype - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End;</p> <p>type – Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4)</p> <p>bulk – (yes/no) – indicates whether bulk energy import or export is active. Only relevant for UC 3 and 4.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.</p>
Exploring the use of the South African Nest Record Scheme to detect changes in phenology: A case study using four well represented species
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Data from: Linking land use and the nutritional ecology of herbivores: a case study with the Senegalese locust
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Using geometric morphometrics to determine the ‘fittest’ floral shape: a case study in large-flowered buzz-pollinated Melastomataceae
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Data from: Improving population analysis using indirect count data: A case study of chimpanzees and elephants
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Genetic structure in patchy populations of a candidate foundation plant: a case study of Leymus chinensis using genetic and clonal diversity
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Data from: Beware of the impact of land use legacy on genetic connectivity: A case study of the long-lived perennial Primula veris
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Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
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Agriculture land-use change seasonally rewires stream food webs: A case study from headwater streams in the Lake Erie watershed
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Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer’s sparrow
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Supplementary material: How should functional relationships be evaluated using phylogenetic comparative methods? A case study using metabolic rate and body temperature
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SPEC RG Technical Report: A Review of Serverless Use Cases and their Characteristics - Dataset
<p><strong><em>PrettyDataset.xlsx</em></strong></p> <p>Our data set of 89 serverless use cases and their mapping to 24 characteristics. In form of an Excel file to simplify manual inspection. Columns represent characteristics, rows represent use cases.</p> <p><em><strong>Dataset.csv</strong></em></p> <p>Raw version of our data set of 89 serverless use cases and their mapping to 24 characteristics as a CSV file for automated analysis. Columns represent characteristics, rows represent use cases.</p> <p><em><strong>Initial Characterizations.csv</strong></em></p> <p>Reviewer characterizations prior to the discussion and consolidation phase. Required for the calculation of the fleiss kappa score.</p> <p><em><strong>CalculateKappa.py</strong></em></p> <p>Python script for the calculation of the fleiss kappa score shown in the technical report. Requires Python 3.6.</p> <p><em><strong>GenerateFigures.py</strong></em></p> <p>Python script for the generation of all figures shown in the technical report. Requires Python 3.6.</p>
plan4res - public dataset for case study 1 part MIM-1: time series used for multi-modal investment pathway modelling
<p>Public data set which is used within the plan4res project for performing case study 1 "Multi-modal European energy concept for achiving COP21" - Multi-modal Investment modelling (MIM) Part 1: Time series for the reference year 2015</p> <p>The related documentation is included in plan4res' deliverable D4.5 chapter 3.2 (see 10.5281/zenodo.3785010) </p> <p>The data set includes the following data:</p> <p>a) characteristic annual load profiles for large industrial heat demand for chemical, iron & steel, food & beverage and pulp & paper industries for the reference year 2015</p> <p>HOTMAPS__TD_OUT_D_CHEM__20200608T160653__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_FOOD__20200608T160724__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_IRON__20200608T160705__20200422T120000Z__v01.csv HOTMAPS__TD_OUT_D_PAPER__20200608T160715__20200422T120000Z__v01.csv </p> <p>b) characteristic demand profiles for road-side car passenger transport and availability of cars for charging while (home) parking for the reference year 2015 </p> <p>SIEMENS__TD_OUT_D_RoadCar__20200608T160627__20200401T120000Z__v01.csv SIEMENS__TD_CAP_CarPark__20200608T160637__20200401T120000Z__v01.csv </p> <p>c) load profiles for exogeneous demand of electricity for the reference year 2015. The exogenous demand includes all electricity consumptions not explicitly modeled within MIM modeling.</p> <p>HRE4__TD_OUT_ElectricityExo__20200608T160732__20200401T120000Z__v01.csv </p> <p>c) regionally resolved demand profiles for (individual) space heating and space cooling for the reference year 2015</p> <p>HRE4__TRD_CAP_Cool_2015__20200608T160051__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_HeatInd_2015__20200608T155849__20200401T120000Z__v01.csv</p> <p>d) regionally resolved generation profiles of electricity from photovoltaic, wind onshore, wind offshore, hydro run-of-river, and for heat generation from solar thermal for the reference year 2015</p> <p>NINJA__TRD_CAP_PV_2015__20200608T160440__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindOFF_2015__20200608T155422__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindON_2015__20200608T155251__20191104T120000Z__v01.csv HRE4__TRD_CAP_HydroRoR_2015__20200608T155550__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_SolarThermal_2015__20200608T155718__20200401T120000Z__v01.csv </p> <p>e) regionally resolved generation profile of electricity from wind offshore transformed in a way to represent potential capacity factors in future as stated by doi:10.2760/041705. Data based on reference year 2015</p> <p>SIEMENS__TRD_CAP_WindOFF_2040__20200608T155127__20200401T120000Z__v01.csv </p> <p>x) A list of geographical description of the zone hierarchy data used in MIM for the EU33 region set.:</p> <p>SIEMENS__ZoneHierarchy_MIM_EU33__20181231T120000Z___20200131T1200000Z__v001.csv </p> <p>Further info:</p> <p>Time series are based on historical data for the reference year 2015. </p> <p>Values are normalized over one reference year in a way that either the maximum = 1 (CAP) or the integral = 1 (OUT).</p> <p>All values are listed in arbitrary units. </p> <p>All country names are according to ISO 3166-1 alpha-2.</p>
ARTICONF Use Case Focus Group -2 Dataset
<p>This dataset corresponds to focus group discussion for ARTICONF use cases. The members participating in this discussion are from various sectors and industries.</p>
Confronting sources of systematic error to resolve historically contentious relationships: a case study using gadiform fishes (Teleostei, Paracanthopterygii, Gadiformes)
<p>Reliable estimation of phylogeny is central to avoid inaccuracy in downstream macroevolutionary inferences. However, limitations exist in the implementation of concatenated and summary coalescent approaches, and Bayesian and full coalescent inference methods may not yet be feasible for computation of phylogeny using complicated models and large datasets. Here, we explored methodological (e.g., optimality criteria, character sampling, model selection) and biological (e.g., heterotachy, branch length heterogeneity) sources of systematic error that can result in biased or incorrect parameter estimates when reconstructing phylogeny by using the gadiform fishes as a model clade. Gadiformes include some of the most economically important fishes in the world (e.g., Cods, Hakes, and Rattails). Despite many attempts, a robust higher-level phylogenetic framework was lacking due to limited character and taxonomic sampling, particularly from several species-poor families that have been recalcitrant to phylogenetic placement. We compiled the first phylogenomic dataset, including <span>14,208 loci (>2.8 M bp) from 58 species representing all recognized gadiform families, to infer a time-calibrated phylogeny for the group. </span>Data were generated with a gene-capture approach targeting coding DNA sequences from single-copy protein-coding genes. Species-tree and concatenated maximum-likelihood analyses resolved all family-level relationships within Gadiformes. While there were a few differences between topologies produced by the DNA and the amino acid datasets, most of the historically unresolved relationships among gadiform lineages were consistently well resolved with high support in our analyses regardless of the methodological and biological approaches used. However, at deeper levels, we observed inconsistency in branch support estimates between bootstrap and gene and site coefficient factors (gCF, sCF). Despite numerous short internodes, all relationships received unequivocal bootstrap support while gCF and sCF had very little support, reflecting hidden conflict across loci. Most of the gene-tree and species-tree discordance in our study is a result of short divergence times, and consequent lack of informative characters at deep levels, rather than incomplete lineage sorting (ILS). We use this phylogeny to establish a new<span> higher-level classification of Gadiformes as a way of clarifying the evolutionary diversification of the order.</span> We recognize 17 families in five suborders: Bregmacerotoidei, Gadoidei, Ranicipitoidei, Merluccioidei, and Macrouroidei (including two subclades). A time-calibrated analysis using 15 fossil taxa suggests that Gadiformes evolved ~79.5 million years ago (Ma) in the late Cretaceous, but that most extant lineages diverged after the Cretaceous-Paleogene (K-Pg) mass extinction (66 Ma)<span>. </span>Our results reiterate the importance of examining phylogenomic analyses for evidence of systematic error that can emerge as a result of unsuitable modeling of biological factors and/or methodological issues, even when datasets are large and yield high support for phylogenetic relationships.</p>
Investigating morphological complexes using informational dissonance and bayes factors: A case study in corbiculate bees
<p>It is widely recognized that different regions of a genome often have different evolutionary histories and that ignoring this variation when estimating phylogenies can be misleading. However, the extent to which this is also true for morphological data is still largely unknown. Discordance among morphological traits might plausibly arise due to either variable convergent selection pressures or else phenomena such as hemiplasy. Here we investigate patterns of discordance among 282 morphological characters, which we scored for 50 bee species particularly targeting corbiculate bees, a group that includes the well-known eusocial honeybees and bumblebees. As a starting point for selecting the most meaningful partitions in the data, we grouped characters as morphological modules, highly integrated trait complexes that as a result of developmental constraints or coordinated selection we expect to share an evolutionary history and trajectory. In order to assess conflict and coherence across and within these morphological modules, we used recently developed approaches for computing Bayesian phylogenetic information allied with model comparisons using Bayes factors. We found that despite considerable conflict among morphological complexes, accounting for among-character and among-partition rate variation with individual gamma distributions, rate multipliers, and linked branch lengths can lead to coherent phylogenetic inference using morphological data. We suggest that evaluating information content and dissonance among partitions is useful step in estimating phylogenies from morphological data, just as it is with molecular data. Furthermore, we argue that adopting emerging approaches for investigating dissonance in genomic datasets may provide new insights into the integration and evolution of anatomical complexes.</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.