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665 results for “foundation”
Solitary foundation or colony fission in ants: an intraspecific study shows that worker presence and number increase colony foundation success
<p>Datasets for the paper published in Oecologia entitled "Solitary foundation or colony fission in ants: an intraspecific study shows that worker presence and number increase colony foundation success"</p> <p>Script_article.R : R code to analyze the data</p> <p>Data.txt : Growth data</p> <p>donnees_survie.txt : Survival data</p> <p>donnees_presence_nid.txt : Data of the presence of queens in the nest</p>
Generation of a network slicing dataset: the foundations for AI-based B5G resource management
<p><span>This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the RAN infrastructure.</span></p> <p><span>Each sample consists of pairs of (network scenario, performance metrics). The network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of realistic scenarios without violating SLAs.</span></p> <p><span>This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.</span></p>
Gene embeddings used in GenePT: A Simple But Hard-to-Beat Foundation Model for Genes and Cells Built From ChatGPT
<p>These are the pulled NCBI (and UniProt, when applicable) summaries of genes, as well as the corresponding OpenAI text embeddings (text-embedding-ada-002 and text-embedding-3-large) computed on the summaries. See methods details in Chen and Zou (2024+).</p> <p>The unzipped folder contains four different files: </p> <ol> <li>NCBI_summary_of_genes.json (NCBI gene card summary of human genes)</li> <li>NCBI_UniProt_summary_of_genes.json (NCBI gene card and UniProt protein (when applicable) summary of human genes)</li> <li>GenePT_gene_embedding_ada_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-ada-002 embeddings of the summary in 1. are the values)</li> <li>GenePT_gene_protein_embedding_model_3_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-3-large embeddings of the summary in 1. are the values)</li> </ol> <p>Reference:</p> <p>Chen YT, Zou J. (2024+) GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT. bioRxiv preprint: <a href="https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1">https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1</a>.</p>
Tuning-less Object Naming with a Foundation Model - Data recorded during testing
<p>We implement a real-time object naming system that enables learning a set of named entities never seen. Our approach employs an existing foundation model that we consider ready to see anything before starting. It turns seen images into relatively small feature vectors that we associate with index to a gradually built vocabulary without any training of fine-tuning of the model. Our contribution is using the association mechanism known from transformers as attention. It has features that support generalization from irrelevant information for distinguishing the entities and potentially enable associating with much more than indices to vocabulary. As a result, the system can work in a one-shot manner and correctly name objects named in different contents. We also outline implementation details of the system modules integrated by a blackboard architecture. Finally, we investigate the<br>system's quality, mainly how many objects it can handle in this way.</p>
Characterization and morphometry of prone and affected watersheds by hydro-geomorphological processes in the Serra do Mar Mountain Range, southeastern Brazil: foundation for planning and mitigation actions.
<p>Data: shapefile, tables, and kmz files. </p> <ol> <li>SHAPEFILES</li> </ol> <p>- Dataset with watersheds mapped in the Serra do Mar Paulista Region in the follow cities:</p> <ul> <li>Ubatuba (Abbvr. WU)</li> <li>Caraguatatuba (Abbvr. WC)</li> <li>São Sebastião (Abbvr. WSS)</li> <li>Bertioga (Abbvr. WB)</li> <li>Santos (Abbvr. WS)</li> <li>Praia Grande (Abbvr. WPG)</li> <li>Cubatão (Abbvr. WCUB)</li> <li>São Vicente (Abbvr. WSV)</li> <li>Itanhaém (Abbvr. WITA)</li> <li>Peruíbe (Abbvr. WPERU)</li> <li>Iguape (Abbvr. WIGUA)</li> <li>Itariri (Abbvr. WITR)</li> <li>Pedro de Toledo (Abbvr. WPDT)</li> <li>Iporanga (Abbvr. WIPORA)</li> <li>Apiaí (Abbvr. WAPI)</li> <li>Itaoca Abbvr. WITAO)</li> </ul> <p>- Each shapefile contain information about altitude (min., max, and mean), area (km²), and length (km). </p> <p>- Debris-flow Inventory shapefile.</p> <p> 2. TABLES</p> <ul> <li>Tables for the watersheds mapped in each cities also contain information about the morphometric parameters (melton ratio, basin relief, and relief ratio).</li> <li>Debris-flow inventory information. </li> </ul> <p> </p>
High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany
<p>Open access publication under review.</p> <p>Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels"><strong>this Earth Engine app</strong></a> to explore the data interactively.</p> <p> </p> <p>Abstract:</p> <p>Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.</p> <p><br>We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),<br>Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass<br>models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.<br>Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.</p> <p><br>Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation<br>with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)<br>rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of<br>model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.<br>The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.</p> <p> </p> <p>Data:</p> <p>This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.</p> <p> </p> <p>Disclaimer:<br>Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.</p>
First Street Foundation Flood Model Hazard Layers V1.3
<p>Up to 15 different hazard layers are available, representing 3 different time periods (2021, 2036, 2051) and 4-5 different return periods from the 2-year (coastal only) to the 500-year intervals.</p> <p>Data is delivered in GeoTIFF format and at a 3 meter resolution with each pixel representing depth of flooding in centimeters. This high resolution dataset allows you to visualize flood extents at multiple return periods both today and in the future.</p> <p>The hazard inundation layers are emailed through a clickable link that automatically starts the download of the datasets. The Version 1.3 hazards are available for the contiguous United States.</p> <p>You can download a sample of the hazard layers generated from First Street's Flood Model on this page. You can request access to the hazard layers for areas within the contiguous United States on the First Street website<a href="https://firststreet.org/data-access/paid-access/?utm_source=Hazard_Layers&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component"> here</a>. You can find the data dictionary which breaks down the data that is available with each hazard layer purchase<a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Hazard_Layers&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo"> here</a>. If you are also interested in the flood risk statistics, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Hazard_Layers&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>.</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>
Evidence of climate-driven selection on tree traits and trait plasticity across the climatic range of a riparian foundation species
<p>Selection on quantitative traits by heterogeneous climatic conditions can lead to substantial trait variation across a species range. In the context of rapidly changing environments, however, it is equally important to understand selection on trait plasticity. To evaluate the role of selection in driving divergences in traits and their associated plasticities within a widespread species, we compared molecular and quantitative trait variation in <em>Populus fremontii</em> (Fremont cottonwood), a foundation riparian distributed throughout Arizona. Using SNP data and genotypes from 16 populations reciprocally planted in three common gardens, we first performed Q<sub>ST</sub>-F<sub>ST</sub> analyses to detect selection on traits and trait plasticity. We then explored the environmental drivers of selection using trait-climate and plasticity-climate regressions. Three major findings emerged: 1) There was significant genetic variation in traits expressed in each of the common gardens and in the phenotypic plasticity of traits across gardens, both of which were heritable. 2) Based on Q<sub>ST</sub>-F<sub>ST</sub> comparisons, there was evidence of selection in all traits measured; however, this result varied from no effect in one garden to highly significant in another, indicating that detection of past selection is environmentally dependent. We also found strong evidence of divergent selection on plasticity across environments for two traits. 3) Traits and/or their plasticity were often correlated with population source climate (R<sup>2</sup> up to 0.77 and 0.66, respectively). These results suggest that steep climate gradients across the Southwest have played a major role in shaping the evolution of divergent phenotypic responses in populations and genotypes now experiencing climate change.</p>
Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)
<p>This video is the seventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Remote Immune Monitoring: Need, Opportunities and Challenges - Professor Kourosh Saeb-Parsy (University of Cambridge & Cambridge University Hospitals NHS Foundation Trust)</p> <p>Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing. I matriculated as a medical student at Fitzwilliam College in 1993. My interest in biomedical research was developed during my Part II year studying Anatomy A (neurosciences and developmental biology) and I subsequently enrolled on the MB-PhD programme. I completed my doctoral thesis in neurophysiology of circadian rhythms in 2000 and qualified as a medical doctor in 2001. While studying for my PhD, I pursued an active interest in teaching and started supervising undergraduates at Fitzwilliam (and other colleges) in 1998. I served as MCR President in 1999, became a Fellow in 2003 and Director of Studies in Clinical Medicine in 2004. I pursued a career in surgery after graduation and was appointed as a University Lecturer in Transplant Surgery in 2012.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ANZKGxj87E0</p>
Text-fig. 1. The pioneers of scientific palaeobotany whose ideas laid the foundations of how we now name plant fossil-taxa. a: Ernst von Schlotheim (1764 – 1821); b: Kaspar Maria von Sternberg (1761 – 1837), reproduced by permission from J. Kvaček (National Museum, Prague); c: Adolphe Brongniart (1801 – 1876). Adapted from Cleal and Thomas (2019: fig. 2.1). in Naming Of Parts: The Use Of Fossil-Taxa In Palaeobotany
Text-fig. 1. The pioneers of scientific palaeobotany whose ideas laid the foundations of how we now name plant fossil-taxa. a: Ernst von Schlotheim (1764 – 1821); b: Kaspar Maria von Sternberg (1761 – 1837), reproduced by permission from J. Kvaček (National Museum, Prague); c: Adolphe Brongniart (1801 – 1876). Adapted from Cleal and Thomas (2019: fig. 2.1).
Fig. 6 in Overview of Psychotria in Madagascar (Rubiaceae, Psychotrieae), and of Bremekamp's foundational study of this group
Fig. 6. – Distribution of Psychotria L. in Madagascar. Each circle represents a specimen locality for a specimen
Fig. 2. – Psychotria L in Overview of Psychotria in Madagascar (Rubiaceae, Psychotrieae), and of Bremekamp's foundational study of this group
Fig. 2. – Psychotria L. in Madagascar. A. Long-styled flower of P. macrochlamys (Baker) A.P. Davis & Govaerts; B. Fruits of P. macrochlamys; C. Habit of P. lantzii (Bremek.) Razafim. & B. Bremer.
Fig. 3. – Psychotria L in Overview of Psychotria in Madagascar (Rubiaceae, Psychotrieae), and of Bremekamp's foundational study of this group
Fig. 3. – Psychotria L. in Madagascar. A. Leaves with bacterial nodules of P. pachygrammata Bremek.; B. Leaves with retuse apices
Fig. 4 in Overview of Psychotria in Madagascar (Rubiaceae, Psychotrieae), and of Bremekamp's foundational study of this group
Fig. 4. – Some morphological features of Psychotria L. in Madagascar. A–B. Apomuria, Psychotria biloba (Bremek.) A.P. Davis & Govaerts: A. Fruiting branch; B. Cross-section of seed (removed from pyrene). C– D. Apomuria, Psychotria punctata Vatke: C. Fruit; D. Cross-section of seed. E. Psychotria kirkii Hiern, cross-section of seed. F–G. Psathura, Psychotria borbonica (J.F. Gmel.) Razafim. & B. Bremer: F. Fruit; G. Cross-section of fruit, with four pyrenes. H–I. Psathura, Psychotria batopedina (Verdc.) Razafim. & B. Bremer: H. Fruit; I. Cross-section of fruit, with five pyrenes. J. Unusual stipules in Malagasy Psychotria L. (TAYLOR, in press). K–N. Trigonopyren, Psychotria tsiandroi Razafim. & B. Bremer.: K. Node near stem apex with developed stipule, unbroken and with glandular projections; L. Node below the node shown in K, with aging stipule that is falling off in fragments; M. Node in lower part of stem, with old stipule that has fallen off except for persistent basal part of sheath; N. Cross-section of pyrene. O–P. Trigonopyren, Psychotria bealanensis Razafim. & B. Bremer: O. Fruit; P. Cross-section of fruit, with two pyrenes.
A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on Parkinson's Disease. We provide data extracted via web scraping, along with metadata resulting from the extraction process using the NCBI API. The data pertains to the article titled 'A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation'.</p> <h2>Metadata Description</h2> <ul> <li> <h3>Analisys_MJFF_05_04_2024.xlsx</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>AU</td> <td>List of authors in abbreviated format.</td> <td>Text</td> </tr> <tr> <td>AF</td> <td>List of authors with full names.</td> <td>Text</td> </tr> <tr> <td>TI</td> <td>Full title of the article.</td> <td>Text</td> </tr> <tr> <td>SO</td> <td>Name of the journal or publication.</td> <td>Text</td> </tr> <tr> <td>SO_CO</td> <td>Country of origin of the publication.</td> <td>Text</td> </tr> <tr> <td>LA</td> <td>Language of the article.</td> <td>Text</td> </tr> <tr> <td>DT</td> <td>Type of document, such as "Journal Article".</td> <td>Text</td> </tr> <tr> <td>DE</td> <td>Keywords or descriptors associated with the article.</td> <td>Text</td> </tr> <tr> <td>MESH</td> <td>MeSH terms that describe the content of the article.</td> <td>Text</td> </tr> <tr> <td>DI</td> <td>Digital Object Identifier (DOI).</td> <td>Text</td> </tr> <tr> <td>PG</td> <td>Number of pages or page range.</td> <td>Numeric</td> </tr> <tr> <td>GRANT_ID</td> <td>Identification of funding, when available.</td> <td>Text</td> </tr> <tr> <td>GRANT_ORG</td> <td>Organization that provided the funding.</td> <td>Text</td> </tr> <tr> <td>UT, PMID</td> <td>Unique identifiers of the article.</td> <td>Numeric</td> </tr> <tr> <td>DB</td> <td>Name of the database where the article is indexed.</td> <td>Text</td> </tr> <tr> <td>AU_UN</td> <td>Information about the academic unit or institution of the authors.</td> <td>Text</td> </tr> </tbody> </table> <ul> <li> <h3>References_MJFF_v2_Final_Corrected.csv</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>Title</td> <td>Name of the article or publication.</td> <td>Text</td> </tr> <tr> <td>Authors</td> <td>List of authors who contributed to the article.</td> <td>Text</td> </tr> <tr> <td>Journal Name</td> <td>Name of the journal or periodical where the article was published.</td> <td>Text</td> </tr> <tr> <td>Publisher</td> <td>Name of the publisher who published the article.</td> <td>Text</td> </tr> <tr> <td>Volume</td> <td>Volume number of the journal in which the article appears.</td> <td>Numeric or Text</td> </tr> <tr> <td>Edition Number</td> <td>Number of the edition of the journal in which the article is found.</td> <td>Numeric or Text</td> </tr> <tr> <td>Starting Page</td> <td>Number of the first page of the article in the publication.</td> <td>Numeric</td> </tr> <tr> <td>Ending Page</td> <td>Number of the last page of the article.</td> <td>Numeric</td> </tr> <tr> <td>Publication Date</td> <td>Date on which the article was published.</td> <td>Date</td> </tr> <tr> <td>Open Access Status</td> <td>Indicates whether the article is available in open access.</td> <td>Text</td> </tr> <tr> <td>License</td> <td>Type of license under which the article was published.</td> <td>Text</td> </tr> <tr> <td>DOI (Digital Object Identifier)</td> <td>Unique identifier for the article that provides a permanent link to the online access.</td> <td>Text</td> </tr> <tr> <td>OA Location URL</td> <td>Direct URL to the article, if available in open access.</td> <td>Text</td> </tr> <tr> <td>Citation Count</td> <td>Number of times the article has been cited by other publications.</td> <td>Numeric</td> </tr> </tbody> </table> <p> </p>
Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region
<p>Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national-scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. The data in this repository supports an assessment of the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S.. For the analysis, FSF data was compared to PRIMo-Drain, a flood hazard model that resolves drainage infrastructure and fine resolution drainage dynamics.</p> <p>In the linked journal manuscript, using the case of Los Angeles, California, we find that FSF and PRIMo-Drain estimates of population and property value exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of governance, for example by 4- to 18-fold at the municipal scale. FSF and PRIMo-Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ~1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national-scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models.</p>
Figure 3 in Implementation of the QR Code system in the Medical Malacology Collection of the René Rachou Institute, Oswaldo Cruz Foundation
Figure 3. Use of the QR Code in Fiocruz-CMM. Carrying a cell phone with a camera and internet access, the user photographs the QR Code and has access to a spreadsheet with all the data related to the specimen of interest.
Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images
<p>Codes, trained model, and datasets for the paper "Semi-Supervised Pre-trained Foundation Model for 3D Structural Feature Analysis of Seismic Images".</p>
Figure 9 in Not just a taxonomist, but a naturalist! The foundations of "Froehlich's Autonomous Stonefly Republic"
Figure 9 CGH on his 90th birthday, Ribeirão Preto, São Paulo, Brazil, 2017 (photograph by Augusto Froehlich).
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.