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1,291 results for “gap”
Figure 3. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 3. - Tyloridastriata, vulva, dorsal view, specimen from India (BNHS Sp. 139) CD- copulatory duct, FD- fertilization duct, S- spermatheca (Scale=0.2 mm).
Figure 4. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 4. - Map showing distribution of Tylorida species with T.striata-like globose abdomen.
Figure 1d. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 1d. - Metastriata, female syntype.Figure 1a.Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)Figure 1b.Abdomen of same syntype, lateral view (Scale=3 mm)Figure 1c.Vulva, ventral view (Scale=0.3 mm)Figure 1d.Labels in syntype vial <br> Labels in syntype vial
Figure 1a. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 1a. - Metastriata, female syntype.Figure 1a.Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)Figure 1b.Abdomen of same syntype, lateral view (Scale=3 mm)Figure 1c.Vulva, ventral view (Scale=0.3 mm)Figure 1d.Labels in syntype vial <br> Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)
Figure 1c. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 1c. - Metastriata, female syntype.Figure 1a.Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)Figure 1b.Abdomen of same syntype, lateral view (Scale=3 mm)Figure 1c.Vulva, ventral view (Scale=0.3 mm)Figure 1d.Labels in syntype vial <br> Vulva, ventral view (Scale=0.3 mm)
Figure 1b. from Bridging the distributional gap of Tylorida striata (Thorell, 1877) and new synonymy (Araneae: Tetragnathidae) - Biodiversity Data Journal 3: e4878 (26 March 2015) https://doi.org/10.3897/BDJ.3.e4878
Figure 1b. - Metastriata, female syntype.Figure 1a.Habitus (detached cephalothorax and abdomen placed together), dorsal view (Scale=5 mm)Figure 1b.Abdomen of same syntype, lateral view (Scale=3 mm)Figure 1c.Vulva, ventral view (Scale=0.3 mm)Figure 1d.Labels in syntype vial <br> Abdomen of same syntype, lateral view (Scale=3 mm)
Questionnaire on needs and requirements for energy performance gap reduction at the operational level of a building
<p>Questionnaires provided within the Hit2Gap H2020 project.</p> <p>Lsits needs and requirements of the different actors of the project; these actors are at different responsibilities of a building.</p>
Dataset from paper: "Mind the Gap: Reconciling tropical forest carbon flux estimates from Earth Observation and National Reporting requires transparency"
<p>This repository contains the processed data used in the publication of Heinrich et al., 2023 (Mind the Gap: Reconciling tropical forest carbon flux estimates from Earth Observation and National Reporting requires transparency). Carbon Balance and Management: https://cbmjournal.biomedcentral.com/articles/10.1186/s13021-023-00240-2 </p><p>When using this data, full reference to the raw data publications and the publication Heinrich et al., 2023 must be made.</p><p>The raw datasets used in this study are all available from their original sources.</p><p> </p>
Figure 4 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 4. Response graphs of habitat suitability (ordinate axis) according to the explanatory variables that intervened in the adjustment of the model for brown brocket deer (A, B, C). The temperature is expressed in degrees Celsius.Source of bioclimatic variables (bio), site https://www.worldclim.org/data/bioclim.html.
Figure 8 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 8. Species richness maps obtained using three algorithms, (A) "fuzzy union″, (B) "species richness″ and (C) "total beta″.
Figure 7 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 7. Response graphs of habitat suitability (ordinate axis) according to the explanatory variables that intervened in theadjustment of themodel for collared peccary (A,B, C).Temperature is expressed in degrees Celsius and altitude in meters.Source of bioclimatic variables (bio), site https://www.worldclim.org/data/bioclim.html.
Consumer value data for water quality improvement depending on the gap between perceived and expected condition
<p>This CSV file provides data from a survey of consumers about the gap between current and expected tap water quality. The WTP analysis of consumers' expectations for improved tap water quality was estimated with the R program. The R program is available at https://www.r-project.org.</p>
Air phyto-cleaning by an urban meadow – Filling the winter gap - DATA
<p>Database of article: Air phyto-cleaning by an urban meadow – Filling the winter gap.</p>
Forest gaps around wind turbines attract bat species with high collision risk
<p><span>Globally, renewable energy is highly demanded, leading to a recent increase in the number of wind turbines at forested sites. For the deployment of turbines, forest areas must be cleared, which results in significant habitat changes. To assess the ecological consequences of these changes for forest-associated bats, we quantified the activity of three bat foraging guilds at turbine clearings, adjacent forest edges, and nearby forest canopies. Specifically, open-space and edge-space foraging bats were more active at turbine clearings and forest edges than at forest canopies. Narrow-space foragers were also more often recorded at turbine clearings than at forest canopies. An increased activity of open- and edge-space foragers at turbine clearings may increase the risk for casualties. </span><span>Therefore, if turbines need to be placed in forests, strict curtailment schemes should be practiced to mitigate collision risk for these bats. This may impair the efficacy of wind energy production at forested sites.</span></p>
[SUPPLEMENTARY FILES] DIVERSITY PATTERNS AND KNOWLEDGE GAPS OF ATLANTIC FOREST EPIPHYLLOUS BRYOPHYTES: A HIGHLY NEGLECTED GROUP
<p>Epiphyllous bryophytes are a group of plants with complex adaptations to colonize the leaves of vascular plants and are considered one of the most specialized and sensitive plants groups to environmental changes. Despite their specificity and ecological importance, these plants represent a largely neglected group in scientific research and ecological data. This limited information directly affects the understanding of diversity patterns and compromises the conservation of this group in fragmented ecossystems, such as the Atlantic Forest. </p><p> </p><p> </p>
TINKER_WP4_data for gap filling - inkjet printing_221123
<p>Binary image data which were provided to the inkjet printing to do gap filling. The images are stored in folders, each folder correspond to one PCB. Inside each folder are six folders corresponding to 6 substrates. Within each substrate folder are the binary print images.</p>
Figure 2 in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Figure 2. – Diet of K. rupestris in northern Queensland (Pusey et al., 2004).
Moss functional trait ecology: Trends, gaps, and biases in the current literature
<p>Functional traits are critical tools in plant ecology for capturing organism-environment interactions based on trade-offs as well as making links between organismal and ecosystem processes. While broad frameworks for functional traits have been developed for vascular plants, we lack the same for bryophytes, despite an escalation in the number of bryophyte functional trait studies conducted in the last 45 years and an increased recognition of the ecological roles bryophytes play across ecosystems. In this review, we compiled data from 282 published articles (10005 records) focusing on functional traits measured in mosses, and sought to (a) examine trends in types of traits measured, (b) capture taxonomic and geographic breadth of trait coverage, (c) reveal biases in coverage in the current literature, and (d) develop a bryophyte-function index (BFI) to describe completeness of current trait coverage and identify global gaps to focus research efforts. The most commonly measured response traits (those related to growth/reproduction in individual organisms) and effect traits (those that directly affect community/ecosystem scale processes) fell into the categories of morphology (e.g. leaf area, shoot height) and nutrient storage/cycling, and our BFI revealed that these data were most commonly collected from temperate and boreal regions of Europe, North America and east Asia. However, fewer than 10% of known moss species have available functional trait information. Our synthesis revealed that there is a need for research on traits related to ontogeny, sex, and intraspecific plasticity, and on co-measurement of traits related to water-relations and bryophyte-mediated soil processes. </p>
Data: Non-utopian optical properties computed of a tomographically reconstructed real photonic band gap crystal
<p>This repository contains the scripts and data used for the manuscript "Non-utopian optical properties computed of a tomographically reconstructed real photonic band gap crystal'' by LJ Corbijn van Willenswaard, S Smeets, N Renaud, M Schlottbom, JJW van der Vegt and WL Vos</p> <p>This dataset contains the following:</p> <ul> <li>The starting slice of the X-ray holotomagraphy dataset</li> <li> All data generated that is used in the paper including: <ul> <li>X-ray processing results</li> <li>Raw results from the computations</li> <li>Post processed results that are the basis of the manuscript</li> </ul> </li> <li>All scripts that were used to automate this process</li> <li>A copy of: <ul> <li>Nanomesh repository (for x-ray processing)</li> <li>hpgem & DGMax source code (for the computations)</li> </ul> </li> </ul> <p>A more detailed readme is included with the data.</p>
ALAMEDA Data: Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases (Parkinson's Disease, Multiple Slerosis and Stroke)
<p><strong>ALAMEDA</strong> is an Horizon 2020 Research and Innovation project that aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions. Its vision is to research and prototype new generation Artificial Intelligence (AI) systems to support brain disorders patients' healthcare, focusing on Parkinson's Disease (PD), Multiple Sclerosis (MS) and Stroke.</p> <p>To this end, three (one for each disease) small scale validation pilots were performed in real world settings. Throughout these pilots, various types of data, such as accelerometer, gyroscopic, heart rate, etc., were collected via smart wearable sensors from the patients enrolled. The smart devices that were employed include: a Fitbit smartwatch, a GENEActiv smart bracelet, Novel Loadsol insole sensors and a prototype smart belt with triaxial accelerometers and gyroscopes embedded. Moreover, the patients underwent several clinical assessments and filled in numerous both disease-specific and non-disease-specific questionnaires.</p> <p>In this record, both raw and processed sensory data are combined with both clinical and patient reported outcomes (PROs) to form different disease-specific datasets. More specifically:</p> <ul> <li>For <strong>Parkinson's disease</strong>: Three datasets are provided (one for tremor detection, one for dyskinesia detection, and one for Hoehn & Yahr score estimation) alongside the vertical ground reaction force recordings.</li> <li>For <strong>Multiple Sclerosis</strong>: Two datasets are provided (one for Expanded Disability Status Scale (EDSS) scores classification and one that accumulates clinical data and individual scores from various MS-related questionnaires) alongside the vertical ground reaction force and the smart belt recordings.</li> <li>For <strong>Stroke</strong>: Two datasets are provided (one for rehabilitation exercises' recognition and one for walking classification, both with and without manual annotations) alongside the smart belt recordings.</li> </ul> <p>More information about the datasets provided can be found in the respective READ ME files that are included in the current record.</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.