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281 results for “Capabilities”
Wireless flow-powered miniature robot capable of traversing tubular structures
<p>Wireless millimeter-scale robots capable of navigating through fluid-flowing tubular structures hold substantial potential for inspection, maintenance, or repair use in nuclear, industrial, and medical applications. However, prevalent reliance on external power constrains their operational range and applicable environments. Alternatives with onboard powering must trade off size, functionality, and operation duration. Here, we propose a wireless millimeter-scale wheeled robot capable of using environmental flows to power and actuate its long-distance locomotion through complex pipelines. The flow-powering module can convert flow energy into mechanical energy, achieving an impeller speed of up to 9595 revolutions per minute, accompanied by an output power density of 11.7 watts per cubic meter and an efficiency of 33.7%. A miniature gearbox module can further transmit the converted mechanical energy into the robot's locomotion system, allowing the robot to move against water flow at an average rate of up to 1.05 meters per second. The robot's motion status (moving against/with flow or pausing) can be switched using an external magnetic field or an onboard mechanical regulator, contingent upon different proposed control designs. Additionally, we design kirigami-based soft wheels for adaptive locomotion. The robot can move against flows of various substances within pipes featuring complex geometries and diverse materials. Solely powered by flow, the robot can transport cylindrical payloads with a diameter of up to 55% of the pipe's diameter and carry devices, such as an endoscopic camera for pipeline inspection, a wireless temperature sensor for environmental temperature monitoring, and a leak-stopper shell for infrastructure maintenance.</p>
Gene expression data from clones of FAO cells with different glucose production capability
<div> <div> <div> <p>In response to energy and nutrient shortage, the liver triggers several catabolic processes to promote survival. Despite recent progresses, the precise molecular mechanisms regulating the hepatic adaptation to fasting remain incompletely characterized. Here, we report the identification of Hydroxysteroid dehydrogenase-like 2 (HSDL2) as a mitochondrial protein highly induced by fasting. We show that activation of PGC1a-PPARa and inhibition of the PI3K- mTORC1 axis stimulate HSDL2 expression in hepatocytes. We found that HSDL2 depletion decreases cholesterol conversion to bile acids (BAs) and impairs FXR activity. HSDL2 knockdown also reduces mitochondrial respiration, fatty acid oxidation, and TCA cycle activity. Bioinformatics analyses revealed that hepatic Hsdl2 expression positively associates with the postprandial excursion of various BA species in mice. Here, we show that liver-specific HSDL2 depletion impacts BA metabolism and decreases circulating cholesterol levels upon refeeding. Overall, our report identifies HSDL2 as a fasting-induced mitochondrial protein that links nutritional signals to BAs and cholesterol homeostasis.</p> </div> </div> </div>
Dataset for "Year-long optical time scale with sub-nanosecond capabilities"
<p>Dataset of the realization of an optical time scale (OTS) based on an hydrogen maser and an ytterbium optical lattice clock. Results discussed in Formichella et al., Year-long optical time scale with sub-nanosecond capabilities, <a href="https://doi.org/10.1364/OPTICA.509706">Optica 11, 4, 523 (2024)</a>.</p> <p> </p>
Test models and test results to evaluate CAD assembly modules capabilities to generate component interfaces
<p>Set of 3D CAD assembly models in STEP AP 203 format.</p> <p>Assembly test models are devoted to evaluations of interfaces between components. The interfaces can be of type surface, rectilinear contacts, circular contacts, or point contacts.</p> <p>Test results obtained from some commercially available CAD assembly modules are given as a set of tables organized in accordancce with contact categories (surface, rectilinear, circular, point).</p> <p>The content and use of the test models are described into the pdf document: Test models and test results to evaluate CAD assembly modules capabilities to generate component interfaces.</p> <p> </p>
Laboratory capability Survey - Pesticides, VSCHT
<p>Pesticide analysis capabilities at University of Chemistry and Technology (VSCHT), Prague, Czech Republic</p>
Laboratory capability Survey - POPs in Food and Feed, VSCHT
<p>POPs analysis capabilities at University of Chemistry and Technology (VSCHT), Prague, Czech Republic</p>
Supplementary Materials: Laser-solid interaction studies enabled by the new capabilities of the iP2 BELLA PW beamline
<pre>Supplementary Materials ======================= Corresponding manuscript: "Laser-solid interaction studies enabled by the new capabilities of the iP2 BELLA PW beamline" by Sahel Hakimi, Lieselotte Obst-Huebl, Axel Huebl, Kei Nakamura, Stepan S. Bulanov, Sven Steinke, Wim P. Leemans, Zachary Kober, Tobias M. Ostermayr, Thomas Schenkel, Anthony J. Gonsalves, Jean-Luc Vay, Jeroen van Tilborg, Csaba Toth, Carl B. Schroeder, Eric Esarey, and Cameron G.R. Geddes (2022) Structure of this Archive ------------------------- This archive is structured as follows in sub-directories: - `WarpXsource/`: the simulation source code, build instructions/documentation and references - `WarpXDataAndInputs/`: the simulation inputs, submission scripts, and representative output - `FigureDataAndScripts/`: the analysis scripts and, where applicable, processed data for individual figures Note that raw data output of simulations is approximately 180 TByte per simulation and can thus not easily be shared in this data archive. Instead, we make all source code, documenation and build instructions, selected processed/reduced data, and relevant analysis scripts available to ensure reproducibility of the raw data basis. Please see the individual subdirectories for further `README.txt` files. Contacts -------- For general questions about the publication and data archive, please contact the corresponding authors. Please see the archive metadata and the publication manuscript for contacts.</pre>
Land capability for agriculture (partial cover)
<p>The Land capability for agriculture (partial cover) spatial dataset provides information on the types of crops that may be grown in different areas dependent on environmental and soil characteristics. This map covers much of the productive agricultural land in Scotland and it can be used to determine the areas most suited to growing crops or grazing livestock. </p> <p>The Land capability for agriculture map (partial cover) was originally mapped at 1:50 000 scale by field survey and was subsequently digitised. It shows the distribution of the different land classes across virtually all of Scotland’s cultivated agricultural land and adjacent uplands. The map should be cited as: 'Soil Survey of Scotland Staff (1984-87). Land Capability for Agriculture maps of Scotland at a scale of 1:50 000. Macaulay Institute for Soil Research, Aberdeen. 10.5281/zenodo.6322760'.</p> <p>The digital dataset contains information on the 'class' of soil. Soil classes range from Class 1 (land capable of producing a wide range of crops) to Class 7 (land of very little agricultural value). Land within Class 3 is subdivided to provide further information on potential yields; Classes 4 and 5 are further divided to provide information on grasslands; Class 6 is divided on the quality of the natural vegetation for grazing. Classes 1 to 3.1 are known as prime agricultural land.</p> <p>There is an accompanying booklet that describes the classification in more detail and set out the rules and guidelines to be used. This booklet should be referenced as: Bibby, J.S., Douglas, H.A., Thomasson, A.J. and Robertson, J.S. (1991) Land capability classification for agriculture. Soil Survey of Scotland Monograph. The Macaulay Institute for Soil Research. Aberdeen. ISBN -0-7084-0508-8.</p> <p>The spatial dataset is provided under the James Hutton Institute open data licence included within the zipped dataset.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download">https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download </a>or viewed at https://soils.environment.gov.scot.</p> <p>THE CLASSES<br> Class 1. Land capable of producing a very wide range of crops with high yields<br> Class 2. Land capable of producing a wide range of crops with yields less high than Class 1.<br> Class 3. Land capable of producing good yields from a moderate range of crops.<br> Class 4. Land capable of producing a narrow range of crops.<br> Class 5. Land suited only to improved grassland and rough grazing.<br> Class 6. Land capable only of use as rough grazing.<br> Class 7. Land of very limited agricultural value.</p>
Land Capability for Agriculture (LCA)
<p>The National scale land capability for agriculture spatial dataset provides information on the types of crops that may be grown in different areas dependent on environmental and soil characteristics. This map covers the entire country and it can be used to determine the areas most suited to growing crops or grazing livestock. </p> <p>The digital dataset contains information on the 'class' of soil. Soil classes range from Class 1 (land capable of producing a wide range of crops) to Class 7 (land of very little agricultural value). Land within Class 3 is subdivided to provide further information on potential yields; Classes 4 and 5 are further divided to provide information on grasslands; Class 6 is divided on the quality of the natural vegetation for grazing. Classes 1 to 3.1 are known as prime agricultural land.</p> <p>The Land Capability for Agriculture assessment was carried out in 1981 using data collected between 1978 and 1981. The National scale land capability for agriculture map was then created in 1983 at a scale of 1:250 000. The map should be cited as: 'Soil Survey of Scotland Staff (1981). Land Capability for Agriculture maps of Scotland at a scale of 1:250 000. Macaulay Institute for Soil Research, Aberdeen.10.5281/zenodo.6322683'.</p> <p>There is an accompanying booklet that describes the classification in more detail and set out the rules and guidelines to be used. This booklet should be referenced as: Bibby, J.S., Douglas, H.A., Thomasson, A.J. and Robertson, J.S. (1991) Land capability classification for agriculture. Soil Survey of Scotland Monograph. The Macaulay Institute for Soil Research. Aberdeen. ISBN -0-7084-0508-8.</p> <p>The spatial dataset is provided under the James Hutton Institute open data licence included within the zipped dataset.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at https://www.hutton.ac.uk/soil-maps/ or viewed at https://soils.environment.gov.scot.</p> <p>THE CLASSES<br>Class 1. Land capable of producing a very wide range of crops with high yields<br>Class 2. Land capable of producing a wide range of crops with yields less high than Class 1.<br>Class 3. Land capable of producing good yields from a moderate range of crops.<br>Class 4. Land capable of producing a narrow range of crops.<br>Class 5. Land suited only to improved grassland and rough grazing.<br>Class 6. Land capable only of use as rough grazing.<br>Class 7. Land of very limited agricultural value.</p>
Land Capability for Forestry (LCF)
<p>The Land Capability Classification for Forestry is based on an assessment of the increasing degree of limitation imposed by the physical factors of soil, topography and climate on the growth of trees and on silvicultural practices. The system described is designed for use at several levels depending on the amount and accuracy of the available data and on the purpose for which the information is required. The main use of the Land Capability Classification for Forestry is as an aid to decision-making at broad planning levels, as a guide for land managers and as a statement of the natural resources of the land of Britain in terms of forestry potential for educational and general interest purposes. The system is an interpretation derived from several sources and, as with all such approaches, will be subject to some degree of arbitrary decision.</p> <p>The digital dataset contains information on the 'class' of soil. Classes range from F1 (land offering excellent flexibility for growth and management) to F7 (land unsuitable for producing trees) with seven types of limitations, these being climate, wind throw, nutrients, topography, droughtiness, wetness and soil.</p> <p>The map was based on a reclassification of the 580 soil map units of the National soil map of Scotland with limited field validation. A set of rules were applied to each of the soil map units combined with information on climate and relief to assign the map units to a capability class for forestry.</p> <p>The dataset should be cited as Soil Survey of Scotland Staff. (1988). Land Capability for Forestry of Scotland at a Scale of 1:250 000. Macaulay Land Use Research Institute, Aberdeen. 10.5281/zenodo.6322608.</p> <p>There is an accompanying handbook which describes the classification system in detail (Bibby, J.S., Heslop, R.E.F. and Hartnup, R. 1988. Land Capability Classification for Forestry in Britain. The Macaulay Land Use Research Institute, Aberdeen. ISBN-0-7084-0467-7.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/soil-maps/">https://www.hutton.ac.uk/soil-maps/ </a>or viewed at https://soils.environment.gov.scot.</p> <p>Class F1. Land with excellent flexibility for the growth and management of tree crops<br>Class F2. Land with very good flexibility for the growth and management of tree crops<br>Class F3. Land with good flexibility for the growth and management of tree crops<br>Class F4. Land with moderate flexibility for the growth and management of tree crops<br>Class F5. Land with limited flexibility for the growth and management of tree crops<br>Class F6. Land with very limited flexibility for the growth and management of tree crops<br>Class F7. Land unsuitable for producing tree crops</p>
Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities
<p>Dataset associated to the paper "Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities"</p>
Data from: Bistable soft jumper capable of fast response and high take-off velocity
<p>In contrast to jumping robots made from rigid materials, soft jumpers composed of compliant and elastically deformable materials exhibit superior impact resistance and mechanically robust functionality. However, recent efforts to create stimuli-responsive jumpers from soft materials are limited in their response speed, take-off velocity, and travel distance. Here, we report a magnetic-driven, ultrafast bistable soft jumper that exhibits the highest jumping capability (jumping over 108 body heights with a take-off velocity of over 2 m/s) and the fastest response time (less than 15 ms) compared to previous soft jumping robots. The snap-through transitions between bistable states form a repeatable loop that harnesses the ultrafast release of stored elastic energy. Based on the dynamic analysis, the multimodal locomotion of the bistable soft jumper can be realized: the interwell mode of jumping and the intrawell mode of hopping. These modes are controlled by adjusting the duration and strength of the magnetic field, which endows the bistable soft jumper with robust locomotion capabilities. In addition, it is capable of jumping omnidirectionally with tunable heights and distances. To demonstrate its capability in complex environment, a realistic pipeline with amphibious terrain was established. The jumper successfully finished the simulative task of cleansing polluted water through the pipeline. The design principle and actuating mechanism of the bistable soft jumper can be further extended for other flexible systems.</p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
Dataset of Listed A-Share Manufacturing Companies in China (2011-2022) for ESG and Innovation Capability Analysis
<p>This dataset was constructed for a study on the relationship between Environmental, Social, and Governance (ESG) scores and innovation capability among listed A-share manufacturing companies in China. The data were sourced from the CMSAR database, focusing on companies within the equipment manufacturing industry, while financial industry samples were excluded. The final dataset comprises 851 samples from 84 listed companies spanning the period from 2011 to 2022. Continuous variables in the dataset were winsorized at the 1% level to mitigate the influence of outliers.</p>
Geoparsing with Large Language Models: Leveraging the linguistic capabilities of generative AI to improve geographic information extraction
<h2>Geoparsing with Large Language Models</h2> <p>The .zip file included in this repository contains all the code and data required to reproduce the results from our paper. Note, however, that in order to run the OpenAI models, users will required an OpenAI API key and sufficient API credits.</p> <div> <h3>Data</h3> <p>The data used for the paper are in the <code>datasetst</code> and <code>results</code> folders.</p> <ul> <li> <p>**Datasets: **This contains the XML files (LGL and Geovirus) and Json files (News2024) used to benchmark the models. It also contains all the data used to fine-tune the gpt-3.5 model, the prompt templates sent to the LLMs, and other data used for mapping and data creation.</p> </li> <li> <p>**Results: **This contains the results for the models on the three datastes. The folder is separated by dataset, with a single <code>.csv</code> file giving the results for each model on each dataset separately. The <code>.csv</code> file is structured so that each row contains either a predicted toponym and an associated true toponym (along with assigned spatial coordinates), if the model correctly identified a toponym; otherwise the true toponym columns are empty for false positives and the predicted columns are empty for false negatives.</p> </li> </ul> <h3>Code</h3> <p>The code is split into two seperate folders <code>gpt_geoparser</code> and <code>notebooks</code>.</p> <ul> <li>**GPT_Geoparser: **this contains the classes and methods used process the XML and JSON articles (<code>data.py</code>), interact with the Nominatim API for geocoding (<code>gazetteer.py</code>), interact with the OpenAI API (<code>gpt_handler.py</code>), process the outputs from the GPT models (<code>geoparser.py</code>) and analyse the results (<code>analysis.py</code>).</li> <li><strong>Notebooks</strong>: This series of notebooks can be used to reproduce the results given in the paper. The file names a reasonably descriptive of what they do within the context of the paper.</li> </ul> <h3>Code/software</h3> <h3>Requirements</h3> <ul> <li>Numpy</li> <li>Pandas</li> <li>Geopy</li> <li>Scitkit-learn</li> <li>lxml</li> <li>openai</li> <li>matplotlib</li> <li>Contextily</li> <li>Shapely</li> <li>Geopandas</li> <li>tqdm</li> <li>huggingface_hub</li> <li>Gnews</li> </ul> <h3>Access information</h3> <p>Other publicly accessible locations of the data:</p> <ul> <li>The LGL and GeoVirus datasets can also be obtained <a href="https://github.com/milangritta/Pragmatic-Guide-to-Geoparsing-Evaluation" target="_blank" rel="noopener">here<span> (opens in new window)</span></a>.</li> </ul> <h3>Abstract</h3> <div> <p>Geoparsing- the process of associating textual data with geographic locations - is a key challenge in natural language processing. The often ambiguous and complex nature of geospatial language make geoparsing a difficult task, requiring sophisticated language modelling techniques. Recent developments in Large Language Models (LLMs) have demonstrated their impressive capability in natural language modelling, suggesting suitability to a wide range of complex linguistic tasks. In this paper, we evaluate the performance of four LLMs - GPT-3.5, GPT-4o, Llama-3.1-8b and Gemma-2-9b - in geographic information extraction by testing them on three geoparsing benchmark datasets: GeoVirus, LGL, and a novel dataset, News2024, composed of geotagged news articles published outside the models' training window. We demonstrate that, through techniques such as fine-tuning and retrieval-augmented generation, LLMs significantly outperform existing geoparsing models. The best performing models achieve a toponym extraction F1 score of 0.985 and toponym resolution accuracy within 161 km of 0.921. Additionally, we show that the spatial information encoded within the embedding space of these models may explain their strong performance in geographic information extraction. Finally, we discuss the spatial biases inherent in the models' predictions and emphasize the need for caution when applying these techniques in certain contexts.</p> </div> <h3>Methods</h3> <div> <p>This contains the data and codes required to reproduce the results from our paper. The LGL and GeoVirus datasets are pre-existing datasets, with references given in the manuscript. The News2024 dataset was constructed specifically for the paper. </p> <p>To construct the News2024 dataset, we first created a list of 50 cities from around the world which have population greater than 1000000. We then used the GNews python package <a href="https://pypi.org/project/gnews/" target="_blank" rel="noopener">https://pypi.org/project/gnews/<span> (opens in new window)</span></a> to find a news article for each location, published between 2024-05-01 and 2024-06-30 (inclusive). Of these articles, 47 were found to contain toponyms, with the three rejected articles referring to businesses which share a name with a city, and which did not otherwise mention any place names.</p> <p>We used a semi autonmous approach to geotagging the articles. The articles were first processed using a Distil-BERT model, fine tuned for named entity recognicion. This provided a first estimate of the toponyms within the text. A human reviewer then read the articles, and accepted or rejected the machine tags, and added any tags missing from the machine tagging process. We then used OpenStreetMap to obtain geographic coordinates for the location, and to identify the toponym type (e.g. city, town, village, river etc). We also flagged if the toponym was acting as a geo-political entity, as these were reomved from the analysis process. In total, 534 toponyms were identified in the 47 news articles. </p> </div> </div>
Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study
<p>Este conjunto de dados foi gerado como parte do estudo "Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study". O estudo focou na avaliação do desempenho das ferramentas Copilot, ChatGPT e Gemini, utilizando problemas do LeetCode em quatro linguagens de programação: Python, Java, JavaScript e C.</p> <p>O conjunto de dados atualizado está organizado nas seguintes pastas:</p> <ol> <li> <p><strong>c_programs</strong>: Esta pasta contém os scripts Python utilizados para calcular a complexidade ciclomática e a complexidade cognitiva do código C gerado pelas ferramentas.</p> <ul> <li><code>calculate_cyclomatic_complexity.py</code>: Script para calcular a complexidade ciclomática.</li> <li><code>calculate_cognitive_complexity.py</code>: Script para calcular a complexidade cognitiva.</li> </ul> </li> <li> <p><strong>codes_suggested_by_the_tools</strong>: Esta pasta contém as sugestões de código geradas pelo Copilot, ChatGPT e Gemini para cada problema do LeetCode.</p> <ul> <li>Subpastas: <code>ChatGPT</code>, <code>Copilot</code>, <code>Gemini</code>, cada uma contendo as sugestões de código correspondentes nos formatos das linguagens.</li> </ul> </li> <li> <p><strong>complexity_of_codes</strong>: Esta pasta contém dois arquivos CSV que fornecem os resultados da análise de complexidade para o código gerado.</p> <ul> <li><code>AI analysis results table - Cognitive.csv</code>: Resultados da complexidade cognitiva do código gerado.</li> <li><code>AI analysis results table - Cyclomatic.csv</code>: Resultados da complexidade ciclomática do código gerado.</li> </ul> </li> </ol> <p>Este conjunto de dados atualizado oferece insights valiosos sobre o desempenho das ferramentas de geração de código com IA e pode ser utilizado para análises futuras ou estudos de replicação.</p>
EXPLORING THE CAPABILITIES AND LIMITATIONS OF DEEP LEARNING IN HEALTHCARE SECTORS IN INDIA: A REVIEW OF CURRENT TRENDS, page 1-10
<p> </p> <p><strong>Learnet Publishing ( Publisher) </strong></p> <p>Conference Volume : ARTIFICIAL INTELLIGENCE (AI) – APPLICATIONS & MISAPPLICATIONS 2024" (ISBN No.: 978-81-957591-7-0) </p> <p>Paper title : <em><strong>EXPLORING THE CAPABILITIES AND LIMITATIONS OF DEEP LEARNING IN HEALTHCARE SECTORS IN INDIA: A REVIEW OF CURRENT TRENDS</strong></em></p> <p><strong>Authors </strong><br>Suvendu Narayan Roy<br>Director, Knowgen Education Services Private Limited<br>Researcher, Faculty of Finance & Management<br>Orcid id : https://orcid.org/0000-0002-2801-0740<br>Soham Roy<br>Researcher & Partner, E & O Learnet and Learnet Manpower Solutions<br>Orcid id : https://orcid.org/0000-0002-3127-608</p>
Dataset for "Comparative Analysis of Machine Learning Models to Forecast Flaring Capability of Solar Active Regions: A Parameter Based Approach"
<p>This CSV file contains the values of 14 selected magnetic features along with the active region class for all the regions used in our study. As discussed in the paper, these 14 magnetic features characterize the properties of active regions. All these magnetic features are obtained from the HMI SHARP data series which provides open-sourced vector magnetic field information of solar active regions. the column named 'AR_class' carries information about the class of active regions i.e., 1 for flaring regions and 0 for non-flaring regions.</p>
Living Standards Capabilities, Health-Related Quality of Life and Life Satisfaction among Older Adults
<p>This study attempts to analyse the Structural Relationship between Living Standards Capabilities, Health-Related Quality of Life and Life Satisfaction among Older Adults</p>
Multi-colony tracking of two pelagic seabirds with contrasting flight capability illustrates how windscapes shape migratory movements at an ocean-basin scale
<p>Migration is a common trait among many animals allowing the exploitation of spatiotemporally variable resources. It often implies high energetic costs to cover large distances, for example between breeding and wintering grounds. For flying or swimming animals, the adequate use of winds and currents can help reduce the associated energetic costs. Migratory seabirds are good models because they dwell in habitats characterized by strong winds while undertaking very long migrations. We tested the hypothesis that seabirds migrate through areas with favourable winds. To that end, we used a multi-colony geolocator tracking dataset of two North Atlantic seabirds with contrasting flight capabilities, the black-legged kittiwake (<em>Rissa</em> <em>tridactyla</em>) and the Atlantic puffin (<em>Fratercula</em> <em>arctica</em>), and wind data from the ERA5 climate reanalysis model. Both species had on average positive wind support during migration. Their main migratory routes were similar and followed seasonally prevailing winds. The general migratory movement had a loop-shape at the scale of the North Atlantic, with an autumn route (southward) along the east coast of Greenland, and a spring route (northward) closer to the British Isles. While migrating, both species had higher wind support in spring than in autumn. Kittiwakes migrated farther and benefited from higher wind support than puffins on average. The variation in wind conditions encountered while migrating was linked to the geographical location of the colonies. Generally, northernmost colonies had better wind support in autumn while the southernmost colonies had a better wind support in spring, with some exceptions. Our study helps in understanding how the physical environment shapes animal migration, which is crucial to further predict how migrants will be impacted by ongoing environmental changes.</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.