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766 results for “Baseline”
Raw data for 'Long-baseline Quantum Sensor Network as Dark Matter Haloscope'
Open the record for dataset details and reuse information.
InnovAfrica project baseline survey data for Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania
<p><span>A data set was generated thorugh surveys to establish a baseline inforamtion for a project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym - InnovAfrica)". The InnovAfrica is a consortium of 16 institutions comprising five institutions from Europe and eleven institutions from Africa and the project was implemented in six countries of eastern and southern Africa namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania from June 2017 to November 2021.</span></p>
fg-baseline-model
<p>fg-baseline-model</p>
QOL Scores, prostaglandin-D2, and eosinophil levels at baseline and follow up period
<p>QOL Scores, prostaglandin-D2, and eosinophil levels at baseline and follow up period</p>
Figure 1 from: Cortés J (2017) Marine biodiversity baseline for Área de Conservación Guanacaste, Costa Rica: published records. ZooKeys 652: 129-179. https://doi.org/10.3897/zookeys.652.10427
Figure 1 - Map of the Área de Conservación Guanacaste (ACG) in the northern Pacific coast of Costa Rica with indication of the sites mentioned in the text. See Table 2 for the codes of the sites. Stars = beaches, triangle = mangrove forests, circle = bays; green = protected area; blue circles = shoals.
Baseline Marine Debris Data (2015-2023) - Proposed Chumash Heritage National Marine Sanctuary
<p>This dataset is synthesized coastline marine debris data from three existing sources and primary collected data. It covers the Central California region from Cambria to Naples for the years 2015-2023. This file contains 40 marine debris item type categories, 6 marine debris material type categories (plastic, glass, metal, cloth, paper and wood, mixed), and 7 marine debris source activity categories (personal hygiene, recreation, smoking, eating and drinking, fishing, dumping, and various). This data set is part of the broader research project conducted by students at the Bren School of Environmental Science & Management. See the research project's abstract for more information: </p> <p>California is both a major source of anthropogenic marine debris and an area particularly vulnerable to its damaging impacts. However, little is known about the quantities and impacts of marine debris in the proposed Chumash Heritage National Marine Sanctuary (CHNMS) along the central coast of California. This project, conducted by graduate students through the Bren School of Environmental Science & Management at the University of California, Santa Barbara, creates a baseline assessment of marine debris in the proposed CHNMS. It aims to inform the National Oceanic and Atmospheric Administration (NOAA) Sanctuaries West Coast Regional Office, along with sanctuary management partners and local communities, about local marine debris and potential management measures. Existing community science beach cleanup data and primary collected data were analyzed to understand spatial patterns in quantities and types of marine debris. We found that plastic debris is the most common material type; areas with the greatest debris densities are likely the Morro Bay, Avila Beach, Five Cities, and Gaviota Coast areas. Smoking, eating, and drinking are major activities that contribute to coastal debris in this region. Alongside this quantitative analysis, analyses of policies and interviews with agencies, local organizations, research institutes, and Indigenous communities revealed that current policies may not be effective at reducing marine debris, despite strong concern for marine debris and its impacts on the coastal environment in this region. Based on these findings, we recommend streamlining debris collection protocols with standardized debris categories and effort metrics, implementing innovative policies to reduce marine debris sources, ensuring co-stewardship of the CHNMS to include and prioritize Indigenous perspectives, and conducting additional research on marine and land-based sources of debris. These recommendations will enhance monitoring and mitigation of marine debris in the CHNMS.</p>
Baseline and Future Habitat Suitability Maps for Tree Species Prioritized for Food Tree Portfolios for Zambia
<p>This archive includes habitat suitability maps for six native food tree species (<em>Anisophyllea boehmii</em> Engl., <em>Parinari curatellifolia</em> Planch. ex Benth., <em>Strychnos cocculoides</em> Baker, <em>Tamarindus indica </em>L., <em>Thespesia garckeana </em>F.Hoffm. [syn. <em>Azanza garckeana </em>(F.Hoffm.) Exell & Hillc.] and <em>Uapaca kirkiana</em>|Müll.Arg.|) and four exotic food tree species (<em>Carica papaya</em> L., <em>Mangifera indica</em> L., <em>Persea americana</em> Mill. and <em>Psidium guajava</em> L.). These species had been prioritized by the project. Species suitability modelling followed the BIOCLIM algorithm whereby a climate suitability score is calculated that reflects the core distribution (5% - 95%) and the marginal distribution (0% - 5% or 95% or 100%) of a species in environmental space. The BIOCLIM algorithm was expanded to reflect the middle of the environmental range (25% - 75%).</p> <p>Species suitability was inferred through bioclimatic ranges documented in the Tree Globally Observed Environmental Ranges database (TreeGOER; Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>). Baseline suitability maps used the historical climate documented by WorldClim 2.1 (Fick & Hijmans <a href="https://doi.org/10.1002/joc.5086">2017</a>). Future suitability maps correspond to a low emissions scenario (Shared Socio-Economic Pathway [SSP] 1-2.6) and a high emissions scenario (SSP 3-7.0), both calculated as medians from Global Climate Models (GCM) projections for the 2050s (2041-2060) available from WorldClim 2.1 (respectively from 23 and 18 GCMs).</p> <p>For larger sets of tree species native to Zambia, it has recently become possible to filter tree species by bioclimatic conditions of the planting site with the GlobalUsefulNativeTrees database (GlobUNT; Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>). The GlobUNT database internally uses TreeGOER environmental ranges and the same BIOCLIM algorithm that was used the generate the habitat suitability maps. Included in this archive is a list of native tree species to Zambia that were filtered for the use category of human food.</p> <p> </p> <p>When using these maps, cite the following:</p> <p>· Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></p> <p>· Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. <em>Global Change Biology</em>, 29, 6303–6318. <a href="https://doi.org/10.1111/gcb.16914">https://doi.org/10.1111/gcb.16914</a></p> <p> </p> <p>Information in this archive were generated for the project of <strong>Piloting incentive-based agricultural portfolios for nutrition and resilience in Zambia</strong>. For more information about this project, check the website: <a href="https://www.worldagroforestry.org/project/piloting-incentive-based-agricultural-portfolios-nutrition-and-resilience-zambia">Piloting incentive-based agricultural portfolios for nutrition and resilience in Zambia | World Agroforestry | Transforming Lives and Landscapes with Trees</a></p>
EasIFA and baseline results
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Figure 2 from: Jażdżewska AM, Corbari L, Driskell A, Frutos I, Havermans C, Hendrycks E, Hughes L, Lörz A-N, Stransky B, Tandberg AHS, Vader W, Brix S (2018) A genetic fingerprint of Amphipoda from Icelandic waters – the baseline for further biodiversity and biogeography studies. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 55–73. https://doi.org/10.3897/zookeys.731.19931
Figure 2 Neighbour-joining (NJ) tree of COI sequences (Suppl. material 1) based on Kimura 2-parameter. Triangles indicate the relative number of individuals studied (height) and sequence divergence (width). The asterisk (*) symbolizes taxa having already published sequences in BOLD/GenBank identified to species level. The numbers in front of the nodes indicate bootstrap support (1000 replicates, only values higher than 50% are presented). The vertical bars represent species delimitations taxonomies obtained from morphology and different species delimitation methods. The same colour indicates the same nominal species. Only the cases where incongruence between different delimitation methods were observed are shown. Note that this tree is not the reconstruction of evolutionary history of presented taxa.
Figure 1 from: Jażdżewska AM, Corbari L, Driskell A, Frutos I, Havermans C, Hendrycks E, Hughes L, Lörz A-N, Stransky B, Tandberg AHS, Vader W, Brix S (2018) A genetic fingerprint of Amphipoda from Icelandic waters – the baseline for further biodiversity and biogeography studies. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 55–73. https://doi.org/10.3897/zookeys.731.19931
Figure 1 Sampling stations. Depth contours are the following: 500 m, 1000 m, 1500 m, 2000 m, 2500 m, 3000 m. Station details are in Suppl. material 1.
BASELINE
<p>BASELINE</p>
ViVoLab_Baseline
<p>Baseline system ViVoLab</p>
Baseline
<p>Test submission</p>
BASELINE_dev
<p>BASELINE_dev 4</p>
VivoLab Baseline
<p>VivoLab Baseline</p>
Baseline
<p>Baseline T2</p>
ViVoLab_Baseline
<p>ViVoLab_Baseline</p>
Baseline
<p>Baseline hybridiv v2</p>
ws-cr-baseline
<p>DIHARD track1 ws-cr system (with R), baseline</p>
test1_baseline
<p>test1_baseline</p>
ScienceDex guides
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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.