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11,177 results for “identify”
The STRINGS queries to identify documents related to the SDGs (+ country-SDG data)
<div>This page contains three documents related to the work the STRINGS team has developed on mapping research related to the Sustainable Development Goals (SDGs): <br>1. A document explaining the methodology used to create search queries for identifying documents related to SDGs 1-16. <br>2. An Excel file containing the search queries themselves. <br>3. An additional Excel file containing country-SDG level data used in the paper "Countries’ research priorities in relation to the Sustainable Development Goals".<br><br>The procedure for creating SDG queries was developed for the STRINGS project. For both the project and the paper, the procedure to identify SDG-related publications does not rely exclusively on search queries. We apply each SDG query to research areas obtained from a publication-level clustering algorithm, based on direct backward and forward citations. This enables us to select research areas related to SDGs and include all the publications contributing to that area. This approach has several advantages, including the ability to include publications that do not use SDG-related language in their abstract or title but still contribute to SDG-related research. <br><br>For further insights, the platform, data, and thresholds used to understand which research areas are associated with an SDG are openly available <a href="https://public.tableau.com/profile/ed.noyons#!/vizhome/UKStringsSDGtocommunities/Dashboard1">here</a>.<br><br>In the <a href="https://strings.org.uk/">STRINGS report</a>, you can explore applications to map and characterize publications and patents related to the SDGs. <br><br>Additionally, the paper "Countries’ research priorities in relation to the Sustainable Development Goals" provides a country-level analysis of the alignment between research priorities and SDG challenges. </div>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Joan Dingley, <a href="http://www.wikidata.org/entity/Q6205005">http://www.wikidata.org/entity/Q6205005</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Identifying Interaction Location in SuperCDMS Detectors
<p>The Super Cryogenic Dark Matter Search (SuperCDMS) experiment uses silicon and germanium particle detectors operated at temperatures of ∼ 30 mK to search for Weakly Interacting Massive Particles (WIMPs), which are candidate dark matter particles that interact weakly with nuclei in the detectors. In operating these detectors, it is required not only to measure the energy of the interaction between the WIMP and the nuclei, but also to reconstruct where the interaction occurred, as the location can be used to separate background interactions from signal and to correct for variations with the location of the energy response.</p> <p>In this project, we, as a team from the University of Minnesota, aim to address the problem of accurately reconstructing the locations of interactions in the SuperCDMS detectors using machine learning methods.</p> <p>The dataset we provided here includes interactions at thirteen different locations from test data taken at the University of Minnesota. For each interaction, a set of parameters was extracted from the signals from each of the five sensors. These parameters represent information known to be sensitive to interaction location, including the relative timing between pulses in different channels, and features like the pulse shape. The relative amplitudes of the pulses are also relevant but due to instabilities in amplification during the test, this data is not included. The parameters included for each interaction are described in our <a href="https://github.com/FAIR-UMN/FAIR-UMN-CDMS/blob/main/doc/FAIR%20Document%20-%20Identifying%20Interaction%20Location%20in%20SuperCDMS%20Detectors.pdf" target="_blank" rel="nofollow noopener noreferrer">project document</a>.</p> <p>For more details, feel free to check our Github page: <a href="https://fair-umn.github.io/FAIR-UMN-CDMS/" target="_blank" rel="nofollow noopener noreferrer">https://fair-umn.github.io/FAIR-UMN-CDMS/</a></p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Vilém Filkuka, <a href="http://www.wikidata.org/entity/Q74052895">http://www.wikidata.org/entity/Q74052895</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Josef Jedlička, <a href="http://www.wikidata.org/entity/Q21516972">http://www.wikidata.org/entity/Q21516972</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Karl Olov Hedberg, <a href="http://www.wikidata.org/entity/Q5795297">http://www.wikidata.org/entity/Q5795297</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Charles Wilkes, <a href="http://www.wikidata.org/entity/Q119329">http://www.wikidata.org/entity/Q119329</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Ludwig Hermann Plate, <a href="http://www.wikidata.org/entity/Q88702">http://www.wikidata.org/entity/Q88702</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Stanislav Staněk, <a href="http://www.wikidata.org/entity/Q71875229">http://www.wikidata.org/entity/Q71875229</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Lucy Beamer, <a href="http://www.wikidata.org/entity/Q81216977">http://www.wikidata.org/entity/Q81216977</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Karl Patterson Schmidt, <a href="http://www.wikidata.org/entity/Q1397402">http://www.wikidata.org/entity/Q1397402</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection'. </p> <p>The following files are included:</p> <ul> <li>best_algo.csv : labels for the classification and median performance of algorithms;</li> <li>ML_models.ipyb : jupyter notebook with the definition of the neural networks for both classifiers;</li> <li>pickle.zip : pickled models for the hardness classification and the algorithm selector;</li> <li>trajectories.zip : raw data files containing parts of the trajectories of each algorithm;</li> <li>Results.zip : results obtained using the approach on the stream of instances.</li> </ul>
Dataset for: "Parameter identifiability and model selection for partial differential equation models of cell invasion"
<p>This is the dataset accompanying the paper "Parameter identifiability and model selection for partial differential equation models of cell invasion" (https://arxiv.org/abs/2309.01476). It consists of a series of images taken of a barrier assay experiment to study tissue expansion of MDCK cells, along with cell density data in MATLAB format.</p> <p>File structure: the contents of the four zip files should be combined (they were split into four files for practical reasons regarding file size). The data corresponds to eight experiments, four with circular initial conditions, and four with triangular initial conditions, the associated data are located in 04-05-22 exp1/Circle and 04-05-22 exp1/Triangle respectively, each labeled "xy<n>", where <n> from 1 to 8 is an identifier for the experiment. The images under the xy<n>_Phase folders are the raw images taken of the experiment, those under the xy<n>_mask folder are processed images indicating the extend of the spread of the cell population. The DensityCellcyleFraction folder contain data files in MATLAB format. The most relevant is the "density" variable, which is a rank-3 tensor of size 150x150x77 such that density(i,j,k) corresponds to the cell density at location (x_i,y_j) and time t_k. The process for calculating the cell density is described in the paper.</p> <p>Alternatively, the density data is also provided in csv format. In the csv_data folder, xy<n>/t<k>.csv encodes a matrix representing cell density for experiment <n> at time t_k.</p> <p>The code for processing and analysing these data are provided in the "code" folder. It is also available at https://github.com/liuyue002/woundhealing .</p> <p>Abstract of the paper:</p> <p>When employing a mechanistic model to study biological systems, practical parameter identifiability is important for making predictions in a wide range of scenarios, as well as for understanding the mechanisms driving the system behaviour. We argue that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection. To demonstrate, we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and require more data to be practically identifiable. The results from identifiability analysis can inform model selection, as well as data collection and experimental design.</p>
Identifying climatic drivers of hybridization with a new ancestral niche reconstruction method
<p>Applications of molecular phylogenetic approaches have uncovered evidence of hybridization across numerous clades of life, yet the environmental factors responsible for driving opportunities for hybridization remain obscure. Verbal models implicating geographic range shifts that brought species together during the Pleistocene have often been invoked, but quantitative tests using paleoclimatic data are needed to validate these models. Here, we produce a phylogeny for Heuchereae, a clade of 15 genera and 83 species in Saxifragaceae, with complete sampling of recognized species, using 277 nuclear loci and nearly complete chloroplast genomes. We then employ an improved framework with a coalescent simulation approach to test and confirm previous hybridization hypotheses and identify one new intergeneric hybridization event. Focusing on the North American distribution of Heuchereae, we introduce and implement a newly developed approach to reconstruct potential past distributions for ancestral lineages across all species in the clade and across a paleoclimatic record extending from the late Pliocene. Time calibration based on both nuclear and chloroplast trees recovers a mid- to late-Pleistocene date for most inferred hybridization events, a timeframe concomitant with repeated geographic range restriction into overlapping refugia. Our results indicate an important role for past episodes of climate change, and the contrasting responses of species with differing ecological strategies, in generating novel patterns of range contact among plant communities and therefore new opportunities for hybridization. The new ancestral niche method flexibly models the shape of niche while incorporating diverse sources of uncertainty and will be an important addition to the current comparative methods toolkit.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Eduard Pechuël-Loesche, <a href="http://www.wikidata.org/entity/Q111780">http://www.wikidata.org/entity/Q111780</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Roy Snelling, <a href="http://www.wikidata.org/entity/Q7373383">http://www.wikidata.org/entity/Q7373383</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Hugo Baum, <a href="http://www.wikidata.org/entity/Q107858">http://www.wikidata.org/entity/Q107858</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Newly identified nematodes from the Great Salt Lake are associated with microbialites and specially adapted to hypersaline conditions
<p>Extreme environments enable the study of simplified food-webs and serve as models for evolutionary bottlenecks and early Earth ecology. We investigated the biodiversity of invertebrate meiofauna in the benthic zone of the Great Salt Lake (GSL), UT, one of the most hypersaline lake systems in the world. The hypersaline bays within the GSL are currently thought to support only two multicellular animals: brine fly larvae and brine shrimp. Here, we report the presence, habitat, and microbial interactions of novel free-living nematodes. Nematode diversity drops dramatically along a salinity gradient from a freshwater river into the south arm of the lake. In Gilbert Bay, nematodes primarily inhabit reef-like organosedimentary structures built by bacteria called microbialites. These structures likely provide a protective barrier to UV and aridity, and bacterial associations within them may support life in hypersaline environments. Notably, sampling from Owens Lake, another terminal lake in the Great Basin that lacks microbialites, did not recover nematodes from similar salinities. Phylogenetic divergence suggests that GSL nematodes represent previously undescribed members of the family Monhysteridae – one of the dominant fauna of the abyssal zone and deep-sea hydrothermal vents. These findings update our understanding of halophile ecosystems and the habitable limit of animals.</p>
Identifying Unexpected Neurotoxicity Drivers with Acetylcholinesterase Inhibition by Virtual Effect-Directed Analysis in Nationwide Estuarine Waters
<p><span>Neurotoxicity is frequently observed in the global aquatic environment, </span><span>threatening aquatic ecosystems and human health</span><span>. </span><span>However, </span><span>a very limited proportion of neurotoxic effects (~1%) has been explained by known chemicals of concern. Here, we integrated</span><span> machine learning, nontargeted analysis, and <em>in vitro</em> biotesting</span><span> to identify neurotoxic drivers of acetylcholinesterase (AChE) inhibition in estuarine waters along the coastline of China. Machine learning was used as a virtual fractionation tool to reduce the complexity of chemical mixtures, thus guiding nontargeted screening of AChE inhibitors. Ultimately, sixty chemicals with diverse </span><span>known and presently unknown</span><span> structures were identified, explaining 82.1% of the observed AChE inhibition </span><span>in estuarine water samples</span><span>. Polyunsaturated fatty acids were unexpectedly found to be neurotoxic drivers, accounting for 80.5% of the overall effect. This proof-of-concept study demonstrates that our approach enables rapid and comprehensive screening of </span><span>causative organic pollutants</span><span> </span><span>associated with various <em>in vitro</em> endpoints </span><span>for large-scale monitoring of water quality</span><span>.</span></p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Eugène Poilane, <a href="http://www.wikidata.org/entity/Q1373543">http://www.wikidata.org/entity/Q1373543</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
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.