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3,225 results for “Case of study”
F I G U R E 1 in Small and in-country herbaria are vital for accurate plant threat assessments: A case study from Peru
F I G U R E 1 The proportion of specimens of Peruvian Begonia and Solanum held in Peruvian institutions (blue) versus international institutions (orange) in (a) the Global Biodiversity Information Facility (GBIF); (b) the Begonia Resource Centre and Solanaceae Source (see text for doi references for all datasets).
F I G U R E 4 in Small and in-country herbaria are vital for accurate plant threat assessments: A case study from Peru
F I G U R E 4 The effect of including data from herbaria in size order upon estimated International Union for Conservation of Nature (IUCN) threat assessments. (a) Percentage of species assessed as DD (Data Deficient, grey), LC (Least Concern, green), VU (Vulnerable, yellow), EN (Endangered, orange), and CR (Critically Endangered, red); (b) the size (number of accessions) of herbaria added (orange) and number of specimen records included (blue); (c) the mean range of species considered threatened when all data are considered. Dashed, vertical lines indicate the limits of large (>1,000,000 sheets) and medium (>500,000 sheets) herbaria. Grey shading indicates Peruvian herbaria. Selected herbaria are indicated in C. Herbarium abbreviations in panels B and C follow Thiers (2001).
Artifact Description/Artifact Evaluation/Computational Artifact for "SPEChpc 2021 Benchmarks on Ice Lake and Sapphire Rapids Infiniband Clusters: A Performance and Energy Case Study"
<p>We provide reproducibility initiative dependencies (Artifact Description or Artifact Evaluation or Computational Results Analysis) appendix at https://github.com/RRZE-HPC/PMBS23-AD. To allow a third party to duplicate the findings, this article provides our extensive performance data artifact and describes further details regarding the software environments, experimental design, and methodology employed for the results shown in the paper, entitled "SPEChpc 2021 Benchmarks on Ice Lake and Sapphire Rapids Infiniband Clusters: A Performance and Energy Case Study". The computational artifacts will enable experienced performance engineers to reproduce and interpret the data shown in the paper in the appropriate way and to follow the conclusions we draw from it.</p>
Fishing activity hotspots in case study areas of the Mediterranean, Black and Atlantic Seas at 0.1°
<p>Statistically significant hotspots of fishing activities in specific case study areas (Adriatic Sea, Aegean Sea, Balearic Sea, Baltic Sea, Bay of Biscay, Black Sea, Levantine Sea and North Sea) were identified by the application of the Getis-Ord Gi statistic (Getis and Ord 2010) though the statistical software R using the globalG.test function (spdep package). The function computes a global test for spatial autocorrelation using a Monte Carlo simulation approach. It tests the null hypothesis of no autocorrelation against the alternative hypothesis of positive spatial autocorrelation. Then the local spatial autocorrelation was tested calculating the Gi statistic, using the local_g_perm function (dfdep package), which indicates the strength of the clustering.</p> <p>Categorization of hotspots was performed, according to the Gi value and the p-value of a folded permutation test obtained for each grid cell, as follows:</p> <ul> <li>Gi>0 and p_value <=0.01 as Very hot</li> <li>Gi>0 and p_value <=0.05 as Hot</li> <li>Gi>0 and p_value <=0.1 as Somewhat hot</li> <li>Gi<0 and p_value <=0.1 as Somewhat cold</li> <li>Gi<0 and p_value <=0.05 as Cold</li> <li>Gi<0 and p_value <=0.01 à Very cold</li> </ul> <p>Grid cells with a p-value > 0.1 were categorized as Insignificant.</p> <p>The analyses were performed on cumulative fishing activity data at 0.1° resolution of nine different gears separately for the eight case study areas.</p> <p>Trawling hotspot cells categorized as “Very hot” (highly pressured) and “Hot” and “Somewhat hot” (medium pressured) were intersected with repositories of stocks and species-observation data (Coro et al., 2023) in order to retrieve information of species potentially caught by those fishing activities.</p> <p>The dataset presented includes for each case study area tables of species potentially caught within the trawling hotspots, maps of each gear hotspot, spatial layers of the gears hotspots (.shp; .csv)</p>
FIGURE 1 in Range sizes of groundwater amphipods (Crustacea) are not smaller than range sizes of surface amphipods: a case study from Iran
FIGURE 1 Distribution map of analyzed gammarid and niphargid species from Iran. Red circles show gammarid distribution records. Black triangles show previous niphargid distributions. Blue triangles show new distribution of Niphargus populations in this study.
FIGURE 4 in Range sizes of groundwater amphipods (Crustacea) are not smaller than range sizes of surface amphipods: a case study from Iran
FIGURE 4 Violin plots, representing maximum linear extent for Gammarus and Niphargus species delimited using ABGD (A) and bPTP (B). The white dot in the middle indicates the median value in the range size, the thick black bar in the center represents the interquartile range and the thin black line shows 95% of all data. The distributions of surface species are not larger than the distributions of subterranean species (Mann-Whitney U test, p = 0.794 (A), p = 0.837 (B)). MOTUs found on a single localities are excluded.
FIGURE 3 in Range sizes of groundwater amphipods (Crustacea) are not smaller than range sizes of surface amphipods: a case study from Iran
FIGURE 3 Species delimitations of new Niphargus populations. The figure shows the BEAST tree as inferred from COI gene sequences. Putative species, as a result of species delineation using bPTP and ABGD, are indicated with side bars. Please note that the figure does not contain five additional species for which we did not have COI sequences, known from a single locality, which proved to be morphologically and genetically distinct according to the more conservative 28S gene marker. New samples are presented in boldface.
Monitoring wildlife population trends with sample counts: A case study on the Alpine ibex (Capra ibex)
<p><span>Monitoring population dynamics is of fundamental importance in conservation but assessing trends in abundance can be costly, especially in large and rough areas. Obtaining trend estimations from counts performed in only a portion of the total area (sample counts) can be a cost-effective method to improve the monitoring and conservation of species difficult to count. </span></p> <p><span>We tested the effectiveness of sample counts in monitoring population trends of wild animals, using as a model population the Alpine ibex (<em>Capra ibex</em>) in the Gran Paradiso National Park (Italy), both with computer simulations and using historical count data collected over the last 65 years. Despite sample counts failed to correctly estimate the true population abundance, sampling half of the target area could reliably monitor the trend of the target population. In case of strong changes in abundance, an even lower proportion of the total area could be sufficient to identify the direction of the population trend. However, when there is a high yearly trend variability, the required number of samples increases and even counting in the entire area can be ineffective to detect population trends. The effect of other parameters, such as which portion of the area is sampled and detectability, was lower, but these should be tested case by case. </span></p> <p><span>Sample counts could therefore constitute a viable alternative to assess population trends, allowing for important, cost-effective improvements in the monitoring of wild animals of conservation interest. </span></p>
Datasets and R code associated with: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires
<p>The attached code and csv files accompany the manuscript titled: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires by Duncan et al. accepted for publication in the journal GeoHealth in September 2023. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Project%20wildfire%20data.csv">Project wildfire data.csv</a> contains a subset wildfire data obtained from:</p> <p>Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6</p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Modeled%20PM2.5%20data_clean.csv">Modeled PM2.5 data_clean.csv</a> includes modeled PM<sub>2.5</sub> concentrations used to make Figure 2. Model results can be obtained from <a href="https://www.epa.gov/hesc/rsig-related-downloadable-data-files">EPA's Fused Air Quality Surfaces Using Downscaling Tool</a>. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/plot_Modeled.R">plot_Modeled.R</a> contains the R code to generate this figure. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/ORs%20all%20Counties.csv">ORs all Counties.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figures 3, 4, and 5.<a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/PM2.5%2035ug-m3%20ORs.csv">PM2.5 35ug-m3 ORs.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figure S1. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Wildfire_Forest.R">Wildfire_Forest.R</a> contains the R code to generate these figures. </p>
Supplementary Material to 'Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria'
<p>This folder contains the supplementary materials to reproduce the results, tables, and figures from the following publication submitted to the Hydrogeology Journal: </p> <p>Kokimova A., Collenteur, R.A. & Birk, S. Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria.</p>
Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi Bay Seagrass meadows
<p><span>Assessing biodiversity in marine nearshore ecosystems is crucial for effective management, especially in the context of climate change and overexploitation of marine resources. Conventional methods often fall short in providing comprehensive information for managing seagrass ecosystems. However, the emergence of environmental DNA (eDNA) techniques has transformed the field by enabling non-invasive surveys that are cost-effective and provide detailed information with high resolution. In this study, we utilized eDNA to assess fish diversity and compared its effectiveness to conventional techniques such as catch assessment surveys and underwater surveys. </span>We sampled three habitats (A: mangrove-seagrass, B: seagrass only, and C: coral-seagrass) with 4 replicates. Site A recorded 8 fish species, site B had 16 species, and site C, characterized by coral and seagrass habitats, exhibited the highest fish diversity with 45 species (mean H' index = 2.455), underscoring its ecological importance. To ensure accurate taxonomic identification, we utilized an updated MiFish reference database containing a larger number of fish species compared to the initial library. This expanded reference database with 9,569 fish species, facilitated more precise identification and enhanced the reliability of our findings. Notably, the eDNA technique outperformed conventional methods by detecting 23 additional fish species that went undetected using traditional surveys. Moreover, our study documented five fish species previously unknown to occur within the study region, further emphasizing the value of eDNA analysis in uncovering hidden biodiversity. These findings strongly advocate for integrating eDNA techniques into the monitoring and assessment of biodiversity in shallow tropical habitats of the Western Indian Ocean. By leveraging eDNA surveys, we can gain valuable insights into fish diversity, discover hidden species, and make informed decisions for the conservation and management of these ecologically significant areas.</p>
Dataset: Challenges, Strengths, and Strategies of People with ADHD in Software Engineering: A Case Study
<p>This dataset accompanies the paper "Challenges, Strengths, and Strategies of People with ADHD in Software Engineering: A Case Study". It contains interview guides as well as anonymous interview transcripts for six interviewees who explicitly consented to the publication.</p> <p>Specifically:</p> <ul> <li>Interview transcripts are Excel files starting with "<strong>interview</strong>".</li> <li>The interview instruments used for the 19 interviews are named "<strong>interview_guide_professionals</strong>" followed by a version number. V1 was used for the first 3 interviews, V2 for the 9 following interviews, and V3 for the remaining 7.</li> <li><strong>manager_feedback_guide.pdf</strong> is the interview guide used for discussions with the 4 managers. The preliminary results we showed them are depicted in <strong>01_challenges_themes_relations_noStrat_v2</strong>, <strong>02_strengths_themes_relations_v1</strong> and <strong>02_strengths_themes_relations_v2</strong></li> <li><strong>consent_form.pdf</strong> is a PDF version of the online form used to handle consent and intake of interviewees.</li> </ul>
Figura 1 in IDENTIFICATION OF PRIORITY SEASONS AND PESTICIDES FOR WATER MONITORING: A CASE STUDY IN ESPÍRITO SANTO
Figura 1: Distribuição espacial da produção cafeeira no estado do Espírito Santo. Destaque para as bacias dos rios Barra Seca/Mucuri e Foz do Rio Doce, onde estão inseridos os cinco maiores municípios produtores.
Figura 2 in IDENTIFICATION OF PRIORITY SEASONS AND PESTICIDES FOR WATER MONITORING: A CASE STUDY IN ESPÍRITO SANTO
Figura 2: Distribuição temporal dos estágios fenológicos do café, das condições climáticas, épocas de maior incidência das pragas e doenças, durante os 12 meses do ano no estado do Espírito Santo. No degradê de cores, as mais escuras representam períodos de maior ocorrência das pragas e doenças, assim como maiores pluviosidades e temperaturas, torando-se mais claras conforme se reduz a incidência e intensidade dos eventos.
Figura 3 in IDENTIFICATION OF PRIORITY SEASONS AND PESTICIDES FOR WATER MONITORING: A CASE STUDY IN ESPÍRITO SANTO
Figura 3: Distribuição temporal das condições climáticas e épocas de aplicação dos ingredientes ativos autorizados classificados entre prioritários e não prioritários no Estado do Espírito Santo. No degradê de cores para as condições climáticas, a cor mais escura representa o período de altas temperaturas e pluviosidades, se tornando mais clara conforme estas condições se tornam menos intensas.
Figure 4 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 4. The SVM structure used in application one approach. The weights determined during the superoised part of the training are {w0, w1, ..., wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.
Figure 2. The paraconsistent plane where the axes G1 and G2 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 2. The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectioely. P = (G1, G2) = (α ̅ β, α + β ̅ 1), drawn in blue just to exemplify, is an important element for our analysis: The closer it is to the corner (1,0), the weaker the classifier associated with the features oector can be. The oalues of α and β are derioed from intra-class and inter-class analyses, respectioely, as detailed in [17].
Figure 1 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns
Figure 1. Quesada gigas. On the left, male emitting acoustic signals. On the right, lateral oiew of male resting.
Implications of headwater contact zones for the riverine barrier hypothesis: a case study of the Blue-capped Manakin (Lepidothrix coronata)
<p>Rivers frequently delimit the geographic ranges of species in the Amazon Basin. These rivers also define the boundaries between genetic clusters within many species, yet river boundaries have been documented to break down in headwater regions where rivers are narrower. To explore the evolutionary implications of headwater contact zones in Amazonia, we examined genetic variation in the Blue-capped Manakin (<em>Lepidothrix coronata</em>), a species previously shown to contain several genetically and phenotypically distinct populations across the western Amazon Basin. We collected restriction site-associated DNA sequence data (RADcap) for 706 individuals and found that spatial patterns of genetic structure indicate rivers, particularly the Amazon and Ucayali, are major dispersal barriers for <em>L. coronata</em> along a distance of more than 3000 km. We also found evidence that genetic connectivity is elevated across several headwater regions, highlighting the importance of headwater gene flow for models of Amazonian diversification. The headwaters of the Ucayali River provide a notable exception to findings of headwater gene flow by harboring non-admixed populations of <em>L. coronata</em> on opposite sides of a <1 km-wide river channel with a known dynamic history, potentially suggesting that additional prezygotic barriers are limiting gene flow in this region. </p>
Annotations for NP13 proceedings paper of Banzina & Niebuhr: How to pause in charismatic speeches: A case study of Barack Obama
<p>TextGrid file used in the above specified study as well as a readme document that includes a link to the corresponding/analyzed YouTube video and the time stamps from where to where the audio was analyzed. Both should enable users to (a) extract the audio from the YouTube video and (b) time align the extracted audio with the TextGrid file. Note that the PRAAT software tool (praat.org) is required to open TextGrid files properly.</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.