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3,225 results for “Case studies”
Normal Retinotopy in Primary Visual Cortex in a Congenital Complete Unilateral Lesion of Lateral Geniculate Nucleus in Human: A Case Study
<p>The data set contains .nii files for each condition of retinotopic mapping in fMRI. (Meridians, Wedges and concentric rings). It also contains DTI data files with .bvec and .bval files. Psychophysics data is in two excel files for motion and orientation discrimination. </p>
GIS as a heuristic tool to interpret ancient historiography: A case study to reconstruct what could plausibly have happened according to the accounts in New Testament texts
<p>This presentation summarizes the research findings of the TiG article (doi 10.1111/tgis.12762) which examines how GIS can be used as a heuristic tool to reconstruct spatial–temporal events from narratives in order to examine whether a scenario is conceivable within the narrative world. The narrative about Paul's escape from Berea (Acts 17:14–15) is used as a case study. Several interpretive issues related to spatial and temporal questions surround these texts. In the case study, three methods are applied: (a) least-cost path analysis on elevation data to construct journeys and travel times for Roman roads; (b) network analysis to find seafaring routes valid for ancient times; and (c) the integration of spatial and temporal data in a space-time cube. Our main finding is that the method yields insights into the spatial–temporal dynamics of the narrative. This helps a modern reader to better understand the narrative conceivability of a story in the mind of a first-century reader.</p>
A case-study for improved reusability of plant phenotyping data with MIAPPE
<p>Accompanying datasets for manuscript, "A case-study for improved reusability of plant phenotyping data with MIAPPE".</p> <p>The ZIP archives in this repository contain the source files and the output files that this manuscript refers to.</p>
Genetic structure in patchy populations of a candidate foundation plant: a case study of Leymus chinensis using genetic and clonal diversity
<p><strong>PREMISE</strong>: The distribution of genetic diversity on the landscape has critical ecological and evolutionary implications. This may be especially the case on a local scale for foundation plant species since they create and define ecological communities, contributing disproportionately to ecosystem function.</p> <p><strong>METHODS</strong>: We examined the distribution of genetic diversity and clones, which we defined first as unique multilocus genotypes (MLG), and then by grouping similar MLGs into multilocus lineages (MLL). We used 186 markers from inter-simple sequence repeats (ISSR) across 358 ramets from 13 patches of the foundation grass <em>Leymus chinensis</em>. We examined the relationship between genetic and clonal diversities, their variation with patch-size, and the effect of the number of markers used to evaluate genetic diversity and structure in this species.</p> <p><strong>RESULTS</strong>: Every ramet had a unique MLG. Almost all patches consisted of individuals belonging to a single MLL. We confirmed this with a clustering algorithm to group related genotypes. The predominance of a single lineage within each patch could be the result of the accumulation of somatic mutations, limited dispersal, some sexual reproduction with partners mainly restricted to the same patch, or a combination of all three.</p> <p><strong>CONCLUSIONS</strong>: We found strong genetic structure among patches of <em>L. chinensis</em>. Consistent with previous work on the species, the clustering of similar genotypes within patches suggests that clonal reproduction combined with somatic mutation, limited dispersal, and some degree of sexual reproduction among neighbors causes individuals within a patch to be more closely related than among patches.</p>
Fig. 1 in A Case Study Of The Herb-Dwelling Spider Assemblages (Aranei) In A Meadow Under The Power Transmission Lines In Ukrainian Carpathians
Fig. 1. Number of individuals collected at the different distances from high voltage power line near Irliava village, August 2012 (SD — standard deviation).
Fig. 2 in A Case Study Of The Herb-Dwelling Spider Assemblages (Aranei) In A Meadow Under The Power Transmission Lines In Ukrainian Carpathians
Fig. 2. Total eudominants and dominants, recedents and subrecedents relative abundance, Shannon and Pielou indexes values at the different distances from high voltage power line near Irliava village (according to the two year samples).
Balancing carnivore conservation and sustainable hunting of a key prey species: a case study on the Florida panther and white-tailed deer
<p>1. Large carnivore restoration programs are often promoted as capable of providing ecosystem services. However, these programs rarely measure effects of successful restoration on other economically and ecologically important species. In South Florida, while the endangered Florida panther (Puma concolor coryi) population has increased in recent years due to conservation efforts, the population of its main prey, the white-tailed deer (Odocoileus virginianus), has declined in some regions. The extent to which panther predation has affected deer populations has been difficult to assess because several other factors have changed during this period, including hydrology and hunting regulations.</p> <p>2. We collected known-fate survival data on 241 GPS-collared adult deer (156 females and 85 males) from 2015 to 2018 in the Florida Panther National Wildlife Refuge and the Big Cypress National Preserve in Florida, USA, to assess effects of panther predation on the deer population, while also evaluating the impacts of hunting and hydrology.</p> <p>3. Predation was the primary cause of death (110 of 134 mortalities), and 87% of predation events were attributed to panthers, a much greater rate than reported by studies conducted before the panther genetic restoration effort initiated in 1995. One deer was legally harvested, and two were likely killed by poachers. Increasing water depth decreased female survival but had little impact on male survival, and drowning was never a cause of mortality.</p> <p>4. Females had greater survival probability than males, except during fawning season. From 2015 to 2018, annual survival rates increased from 0.61 (0.52-0.70) to 0.86 (0.79-0.91) for females, and from 0.45 (95% CI: 0.33-0.58) to 0.79 (0.69-0.86) for males.</p> <p>5. Synthesis and applications – High predation rates, coupled with previous evidence of low recruitment of deer in South Florida, suggest that it will be challenging to meet society's competing demands for large predator restoration and sustainable deer harvest. Deer hunting in the area must remain tightly controlled, for now, if it is to be sustainable, and managers should seek to mitigate effects of high waters and improve deer habitat quality to increase deer population viability. Future work should closely monitor the deer population to assess if management actions can increase vital rates and abundance in the context of high predation rates.</p>
Simulation file for "Titanium dioxide and silver nanoparticles air emissions risk assessment for spray coating processes in Witek, Italy – A case study"
<p>Underlying data for “Nanosized titanium dioxide particle emission potential from a commercial indoor air purifier photocatalytic surface – A case study”</p>
Dataset: What are the characteristics of highly-used packages? A case study on the npm ecosystem
<p>With the popularity of software ecosystems, the number of open source components (a.k.a. “packages”) has been growing rapidly. Identifying high-quality and well-maintained packages from a large pool of packages to depend on is a basic and important problem, as it is beneficial for various applications, such as package recommendation, package search, etc. However, there is no systematic and comprehensive work so far that focuses on addressing this problem except in online discussions or in informal literature and interviews. To fill this gap, in this paper, we conduct a mixed qualitative and quantitative analysis to understand how developers identify and select relevant open source packages. In particular, we start by surveying 118 JavaScript developers from the npm ecosystem to qualitatively understand the factors that make a package to be highly-used within the ecosystem. The survey results show that JavaScript developers believe that highly-used packages are well-documented, receive a high number of stars on GitHub, have a large number of downloads, and do not suffer from vulnerabilities. Then, we conduct an experiment to quantitatively validate the developers' perception of the factors that make a highly-used package. In this analysis, we collect and mine historical data from 2,427 packages divided into highly-used and low-used packages. For each package in the dataset, we collect quantitative data to present the factors studied in the developers' survey. Next, we use regression analysis to quantitatively explain which of the studied factors are the most important. Our regression analysis support developers' believe about highly-used packages. In particular, the results show that highly-used packages tend to be impacted by the number of downloads, stars, and how larger is readme file of the package.</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The code to generate and analyze the data is available on GitHub in the scripts at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p>
Hyperglycemia and steroid use increase the risk of rhino-orbito-cerebral mucormycosis regardless of COVID-19 hospitalization: Case-control study, India
<p><strong>Abstract</strong></p> <p><strong><em>BACKGROUND</em></strong></p> <p>In the context of the ongoing COVID-19 pandemic increased incidence of ROCM was noted in India, among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multi-centric, hospital-based, unmatched case-control study across India.</p> <p><strong><em>METHODS</em></strong></p> <p>We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, comorbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with p-value for crude OR of less 0·20 were considered for the regression model.</p> <p><strong><em>RESULTS</em></strong></p> <p>Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45-59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM.</p> <p><strong><em>CONCLUSIONS</em></strong></p> <p>Hyperglycemia irrespective of having diabetes mellitus and steroid use was associated with increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID.</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The scripts to generate and analyze the data are available at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p> <p><strong>README file for All_data directory</strong></p> <p><strong>Directory structure</strong></p> <p>The All_data directory consists of this README file and 500 sub-directories named DatasetX, for X=1 to 500. Within each DatasetX sub-directory are further sub-directories named alt and null containing files named pop_data.RData and sample_data.RData.</p> <p><strong>alt <em>versus</em> null directories</strong></p> <p>The files in the alt and null directories contain the same variant data but different phenotype data. In particular, under the null hypothesis, disease status is simulated at random according to a 5% prevalence in the population, whereas under the alternative hypothesis disease status is simulated according to a penetrance model that depends on causal SNVs. The R script to simulate data<br> under the alternative hypothesis is in the file 1_SimulateData.R in the Github repository <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>.</p> <p><strong>pop_data.RData and sample_data.RData files</strong></p> <p>The data structures contained in the pop_data.RData and sample_data.RData files are described below. The structure is the same under both the null and alternative hypothesis.</p> <p><strong>pop_data.RData</strong></p> <p>From R, load("pop_data.RData") loads a list named pop_data whose elements describe the population’s haplotype and phenotype data. The list elements are as follows.</p> <ul> <li>Variants: a matrix of variants for the population of 6200 haplotypes <ul> <li>rows are SNVs,</li> <li>columns are sequences</li> </ul> </li> <li>Positions: a data frame of SNV positions <ul> <li>rows are SNVs,</li> <li>column 1 is the SNV name and column 2 is the SNV position in base pairs</li> </ul> </li> <li>Population.Mapping: a data frame telling us how the sequences are paired into individuals <ul> <li>rows are individuals</li> <li>First column 1 is an individual ID from 1,…,3100; columns 2 and 3 are the sequence IDs of the first and second sequence for that individual where the sequence IDs are the column names of the Variants matrix.</li> </ul> </li> <li>Genotype.Matrix: a matrix of genotypes (i.e. variant counts) for the 3100 individuals <ul> <li>rows are SNVs</li> <li>columns are the individuals</li> </ul> </li> <li>causal_region: a vector containing the lower- and upper-limit of the causal region in base pairs.</li> <li>cSNV: a vector containing the IDs of the causal SNVs, where the SNV IDs are the row names of the Variants matrix.</li> <li>DISCRETE: a list with the following elements. <ul> <li>CaseIndividuals: vector of IDs of the affected individuals in the population.</li> <li>ControlIndividuals: vector of IDs of the unaffected in the population.</li> <li>BinaryTrait: a vector of trait status (0=unaffected, 1=affected) for each individual.</li> </ul> </li> </ul> <p><strong>Note:</strong> Within the same DatasetX directory, the only difference between the pop_data data structures under the null and alternative hypothesis is the phenotype information contained in their respective DISCRETE list elements. Both the null and alternative pop_data data structure share list elements: Variants, Positions, Population.Mapping, Genotype.Matrix, causal_region and cSNV.</p> <p><strong>sample_data.RData</strong></p> <p>From R, load("sample_data.RData") loads a list whose elements describe the sequences and phenotypes of the sample of 50 affected individuals (cases) and 50 unaffected individuals (controls) from the population.</p> <ul> <li>Haps: a list with two elements. <ul> <li>sample_haps: a matrix of 200 sequences for the 50 cases and 50 controls. Rows are SNVs and columns are sequences, with the sequences of sampled cases appearing first (i.e. first 100 columns), followed by the sequences of sampled controls (i.e. last 100 columns). Sequences include only those SNVs that are polymorphic in the sample.</li> <li>ccStatus: a vector indicating the case/control status of the individual to which the sequence belongs, with case=1 and control=0.</li> </ul> </li> <li>Genos: a list with two elements. <ul> <li>sample_genos: a matrix of 100 genotypes for the 50 cases and 50 controls. Rows are SNVs and columns are genotypes, with genotypes of cases appearing first, followed by genotypes of controls.</li> <li>ccStatus: a vector indicating the case/control status of each individual, with case=1 and control=0.</li> </ul> </li> <li>Posn: a data frame of SNV positions for each SNV that is polymorphic in the sample. The first column is the SNV name and the second is the SNV position in base pairs. Posn is a subset of pop_data$Positions.</li> <li>poly_cSNV: a vector of IDs for causal SNVs that are polymorphic in the sample.</li> <li>CaseIND: a vector of individual IDs for the case individuals (see pop_data$Population.Mapping).</li> <li>ControlIND: a vector of individual IDs for the control individuals (see pop_data$Population.Mapping).</li> <li>CaseHapID: a vector of IDs for the sequences that belong to cases (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> <li>ControlHapID: a vector of IDs for the sequences that belong to controls (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> </ul> <p> </p>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file. </p> <p>This supplementary data package includes the following files: </p> <p> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
Assessing suspended sediment fluxes with acoustic doppler current profilers: case study from large rivers in Russia
<p>The dataset contains measurements of water discharge by Teledyne RDInstruments RioGrande WorkHorse ADCP unit with a working frequency of 600kHz mounted on a moving boat in 6 areas over large rivers of Russia. The dataset comprises the four largest Arctic Siberian rivers and included continuous ADCP measurements done in 2018-2020 at constant crossection at each river located upper from the impact of recipient seas (tides, surges) near the cities of Salekhard (Ob River), Igarka (Yenisey River), Zhigansk (Lena river) and Chersky (Kolyma River). Another area includes ADCP measurements over 20 transects (named S1…S26, fig. 2) in the lower 200 km of the river Selenga on 27-31July 2018. Additionally, the dataset contains ADCP measurements at 38 points along the Moskva River (named M1, M2…) and 17 tributaries (named T01, T02…) done during 2019-2020.</p> <p>This is a supporting material to a manuscript submitted to «Big Earth Data» journal</p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action
<p>This data set pertains to the following research article: Purnell, P.J. (2022) <em>A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action</em>. arXiv:2201.02006</p>
Simulation results for ProSSE and datafile for case study
<p>This repository includes the data files for the simulation study and the case study in the publication.</p> <p>The simulation_result.Rdata is an R data file that stores simulated data and results. The data is used to generate Figures 2-7, S1, and S2.</p> <p>The casestudy_trees.Rdata is an R data file that stores the posterior samples of the species tree of the Australian rainbow skinks and the model fitting on these trees. The data file is used to generate Figure 8.</p>
Golden Agents - Occasional Poetry (case study)
<p>Scripts and data accompanying the Semantics2022 paper (TBA).</p>
INFOGRAPHICS ON COUNTRY CASE STUDIES - ROADMAP H2020 project
<p>We are happy to introduce you the ROADMAP infographics on country case studies!<br> <br> During our 3rd ROADMAP Annual Meeting, members and stakeholders discussed their goals and challenges on every country involved in our project and we used the technique of message houses in order to obtain and gather all the information.<br> <br> Right after, we translated this information into visuals in order to make it easier to convey and analyse. The infographics are ready to download!</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.