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1,705 results for “vector”
The relationship between vector species richness and the risk of vector-borne infectious diseases
<p>Infectious diseases can impact human welfare and impede wildlife management. Much recent research explores whether biodiversity increases or decreases infectious disease risk. Here we theoretically study the relationship between vector species richness and the risk of vector-borne diseases by an epidemiological model of a single host and multiple vectors. The model considers that vectors are involved in interspecific feeding interference that causes transmission interference and in interspecific recruitment competition that mediates susceptible vector regulation. The model reveals three possible shapes of the vector richness-disease risk relationship: monotonic amplification, hump-shaped, and monotonic dilution patterns. Monotonic amplification pattern occurs across a wide parameter region. Hump-shaped or monotonic dilution patterns are found when transmission interference is strong and recruitment competition is weak. Unexpectedly, susceptible vector regulation does not only promote dilution but can strengthen amplification if coupled with strong transmission interference. Our results suggest that vector richness might be more likely to cause amplification rather than dilution, and shifts in the community mean trait values of vectors could also affect disease risk along the vector richness gradient.</p>
Hematopoietic Tumors in a Mouse Model of X-linked Chronic Granulomatous Disease after Lentiviral Vector-Mediated Gene Therapy
<p>Chronic granulomatous disease (CGD) is a rare inherited disorder due to loss-of-function mutations in genes encoding the NADPH oxidase subunits. Hematopoietic stem and progenitor cell (HSPC) gene therapy (GT) using regulated lentiviral vectors (LVs) has emerged as a promising therapeutic option for CGD patients. We performed non-clinical Good Laboratory Practice (GLP) and laboratory-grade studies to assess the safety and genotoxicity of LV targeting myeloid specific Gp91phox expression in X-linked chronic granulomatous disease (XCGD) mice. We found persistence of gene-corrected cells for up to 1 year, restoration of Gp91phox expression and NADPH oxidase activity in XCGD phagocytes, and reduced tissue inflammation after LV-mediated HSPC GT.<br> Although most of the mice showed no hematological or biochemical toxicity, a small subset of XCGD GT mice developed<br> T cell lymphoblastic lymphoma (2.94%) and myeloid leukemia (5.88%). No hematological malignancies were identified in C57BL/6 mice transplanted with transduced XCGD HSPCs. Integration pattern analysis revealed an oligoclonal composition with rare dominant clones harboring vector insertions near oncogenes in mice with tumors. Collectively, our data support the long-term efficacy of LV-mediated HSPC GT in XCGD mice and provide a safety warning because the chronic inflammatory XCGD background may contribute to oncogenesis.</p>
Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
<p>Several types of all-sky viewing Fabry-Perot Interferometers (FPI) have been developed since the 1990s for ground-based remote sensing of thermospheric winds. The Scanning Doppler Imager (SDI) is one such instrument, which provides temporally simultaneous line-of-sight observations from hundreds of independent look directions per instrument exposure. A geographically distributed network of such instruments increases spatial coverage and, at many locations, also provides overlapping observations along multiple independent lines-of-sight. Together, these characteristics significantly increase the density and fidelity that is possible for reconstructed thermospheric vector wind fields, compared to a traditional narrow-field FPI, but at the cost of complexity and difficulty.<br><br></p> <p>Presently, we describe an application of inverse theory to reconstruct three-component vector thermospheric neutral wind fields using data from multiple SDI instruments. The salient features of the method used here are the ability to reconstruct three-component winds on a dense grid that is sampled regularly in latitude, longitude, and time, without assuming any a-priori underlying structure of the winds. This requires solving an inverse problem that does not in general yield a unique solution unless additional constraints are enforced. We describe this step, also known also as regularization, along with the strategy used to maximize the spatial resolution of the derived wind fields by automatically determining the minimum level of regularization that can produce stable inversions. We present example results obtained from applying this technique to one night of data from a network of SDIs in Alaska, and discuss the implications of these results for current understanding of thermospheric dynamics.</p>
Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"
<p>Electronic Supplementary Data to "Zika vector competence data reveals risks of outbreaks: the contribution of the European ZIKAlliance project"</p>
THREE-DIMENSIONAL VOLUMETRIC EVALUATION OF ROOT RESORPTION IN MAXILLARY ANTERIORS FOLLOWING EN-MASSE RETRACTION WITH VARYING FORCE VECTORS - A RANDOMIZED CONTROL TRIAL
<p>To assess the severity of root resorption (RR) during retraction of maxillary anteriors with and without skeletal anchorage (three different force systems); and analyze it comprehensively using cone-beam computed tomography (CBCT) superimpositions.</p>
Precalculated Results of Throughput Analysis in "Comprehensive Benchmarking of High-Performance Vector Field Representations"
<p>This repository contains results calculated by our throughput analysis. These results should be exactly and deterministically reproducible using the aforementioned software, but precomputed results are provided for the benefit of the reader, in case certain software is not available. Included in this artifact are the assembly files generated by the compilers, the throughput analysis of these aforementioned assembly files, and a collection of tables which summarize the results.</p> <p>These results were generated using <em>clang</em> version 14.0.6, <em>gcc</em> version 11.2.0, and <em>llvm-mca</em> version 14.0.6.</p>
I/Q measurements with 5G SRS signals and receiver 4-port 3D Vector Antenna for positioning studies
<p>This dataset contains the I\Q data of four received signals from a 4-port 3D Vector Antenna (3D VA) as well as *fig and *png examples of the angle of arrival (AoA)/azimuth angle estimation using the MUSIC algorithm based on the raw data. The data was collected from four ports (p5, p6, p7, p8) of a 3D VA provided by ENAC. A single Yagi antenna has been used as a transmitter at 2.1GHz carrier frequency and horizontal polarization.</p>
RDF Dataset for article: A confidence predictor for logD using conformal regression and a support-vector machine
<p>RDF dataset described in article: "A confidence predictor for logD using conformal regression and a support-vector machine" (Manuscript in preparation).</p> <p>The dataset contains conformal logD values at 90% confidence level, computed for 91M compounds from PubChem, in RDF format.</p> <p>The .hdt.gz version contains the dataset in RDF HDT format (http://www.rdfhdt.org/), compressed with tar and gzip. The archive contains both the .hdt file, and an index file, generated by the hdtSearch C++ tool.</p> <p>The .ttl.gz file is a gzipped file in RDF Turtle format (https://www.w3.org/TR/turtle/).</p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Fig. 1 in Implementing a community vector collection strategy using xenomonitoring for the endgame of lymphatic filariasis elimination
Fig. 1 Map showing lsmphatic filariasis studs areas from northern and southern districts, Ghana
Corine Land Cover 2018 V2020_20u1 vector
<p>Full coverage, downlodable copy of the European Union's Copernicus Land Monitoring Service information (CLMS) Corine Land Cover dataset in vector format (geopackage).</p> <ul> <li>Doi: https://doi.org/10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0</li> <li>Release / Major version: V2020_20u1</li> <li>Projection: EPSG:3035</li> <li>Spatial coverage: Europe</li> <li>Spatial resolution: 25 ha/100 m</li> <li>Spatial representation: Vector</li> <li>Temporal extent: 2017-2018</li> <li>Position accuracy: 100 m or better</li> <li>Thematic accuracy: ≥ 85%</li> <li>Format: Vector / Geopackage (gpkg)</li> <li>Size: 8.9 GB</li> </ul> <p><br><strong>Licence:</strong></p> <p>Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:</p> <ul> <li>When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services and shall acknowledge that the Copernicus Land Monitoring Service products and services were produced “with funding by the European Union”.</li> <li>Where the Copernicus Land Monitoring Service products and services have been adapted or modified by the user, the user shall clearly state this.</li> <li>Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the European Union. </li> </ul> <p>Important: the user has all intellectual property rights to the products he/she has created based on the Copernicus Land Monitoring Service products and services.</p>
Figure 2. Flow chart of the CoDOA (Kose & Arslan, 2015).0Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>The related algorithm steps can be visualized with a flow chart as shown in Figure 2 [26].</p>
Distribution of Heracleum sosnowskyi in Syktyvkar city. Vector (polygon) dataset in shapefile.
<p>Dataset contain the borders of Heracleum sosnowskyi stands in Syktyvkar city, Komi Republic, Russia (61.669081º N, 50.822498º E). The dataset was prepared by hand recognition of sattelite images. Field verification was performed with geotagged photos from RIVR system (https://ib.komisc.ru/add/rivr/en).</p> <p>Spatial Reference System: +proj=utm +zone=39 +datum=WGS84 +units=m +no_defs (EPSG: 32639)</p> <p>Extents in spatial reference system units: xMin,yMin 482975.07,6832549.56 : xMax,yMax 494553.23,6857424.73<br> </p>
ConceptNet Vector Ensemble 16.04 input data
<p>This is the data required to build the paper "An Ensemble Method to Build High-Quality Word Embeddings", by Robyn Speer and Joshua Chin.</p> <p>The input data itself comes from:</p> <ul> <li> <p><a href="http://conceptnet5.media.mit.edu/">ConceptNet 5.4</a>, which contains data from Wiktionary, WordNet, and many contributors to Open Mind Common Sense projects, edited by Robyn Speer</p> </li> <li> <p><a href="http://nlp.stanford.edu/projects/glove/">GloVe</a>, by Jeffrey Pennington, Richard Socher, and Christopher Manning</p> </li> <li> <p><a href="https://code.google.com/archive/p/word2vec/">word2vec</a>, by Tomas Mikolov and Google Research</p> </li> <li> <p><a href="http://www.cis.upenn.edu/~ccb/ppdb/">PPDB</a>, by Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch</p> </li> </ul>
Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient
<b>Description: </b><p>Mosquito larval survey and thermotolerance data</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/172"><b>Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=82">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Field microclimate data</b> (Worksheet Microclimate)</p><p>Dimensions: 6917 rows by 11 columns</p><p>Description: Data was recorded using EasyLog USB dataloggers. They were put out either hung on a tree, or suspended off the ground (in the absense of trees) and covered to avoid direct sunlight.</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample location (Field type: ID)</li><li><b>DATE</b>: Date of the measurement (Field type: Date)</li><li><b>TIME</b>: Time of the measurement (Field type: Time)</li><li><b>Temp</b>: Air temperature (Field type: Numeric)</li><li><b>RelHumid</b>: Relative humidity (Field type: Numeric)</li><li><b>DewPoint</b>: The temperature at which the air would condense and dew would form (Field type: Numeric)</li><li><b>SerialNumber</b>: Serial number of the microclimate datalogger used to collect the data (Field type: ID)</li></ul><br></li><li><p><b>Thermal tolerance</b> (Worksheet CTmax)</p><p>Dimensions: 317 rows by 6 columns</p><p>Description: Thermal tolerance experiments on mosquito larvae</p><p>Fields: </p><ul><li><b>Date</b>: The Date the CT max value was taken (Field type: Date)</li><li><b>VialNumber</b>: the vial that the individual came from, and how it is referred to in my field notebook (Field type: ID)</li><li><b>LandType</b>: Habitat type from which the individual was collected (Field type: Categorical)</li><li><b>CriticalMax</b>: The temperature in celcius that the individual became unresponsive to stimulus (Field type: Numeric)</li><li><b>GivenSpecies</b>: Identity of the individual being tested (Field type: Taxa)</li></ul><br></li><li><p><b>Site x species data</b> (Worksheet SpeciesData)</p><p>Dimensions: 192 rows by 9 columns</p><p>Description: Field observations of field mosquito communities</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Replicate</b>: is the replicate of sampling each data collection took place. There were three weeks of sampling so the only values are 1, 2, or 3. (Field type: Replicate)</li><li><b>CollectionType</b>: Method of collection (Field type: Categorical)</li><li><b>Count</b>: Total number of individuals sampled (Field type: Abundance)</li><li><b>GivenSpecies</b>: Identity of the individual(s) (Field type: Taxa)</li></ul><br></li><li><p><b>Forest canopy measurements</b> (Worksheet Densiometer)</p><p>Dimensions: 36 rows by 12 columns</p><p>Description: Densiometer estimates of tree canopy cover</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Val1</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val2</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val3</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val4</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>AveVal</b>: Average densiometer reading (max = 96) (Field type: Numeric)</li><li><b>AdjustedVal</b>: Average canopy openness (Field type: Numeric)</li><li><b>CanopyCover</b>: Average canopy cover (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2017-03-20 to 2017-09-04</p><p><b>Latitudinal extent: </b>4.6314 to 4.7436</p><p><b>Longitudinal extent: </b>117.4556 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Diptera<br> -  -  -  - Culicidae<br> -  -  -  -  - <i>Aedes</i><br> -  -  -  -  -  - <i>Aedes albopictus</i><br> -  -  -  -  - <i>Anopheles</i><br> -  -  -  -  -  - [Anoph]<br> -  -  -  -  - <i>Armigeres</i><br> -  -  -  -  -  - [Armigeres]<br> -  -  -  -  - <i>Culex</i><br> -  -  -  -  -  - [Culex1]<br> -  -  -  -  -  - [Culex2]<br> -  -  -  -  -  - [Culex3]<br> -  -  -  -  -  - [CulexOP]<br> -  -  -  -  - [Species7]<br> -  -  -  -  - [Species8]<br> -  -  -  -  - [Species9]<br> -  -  -  -  - <i>Uranotaenia</i><br> -  -  -  -  -  - [Urano1]<br> -  -  -  -  -  - [Urano2]<br> -  -  -  -  - <i>Zeugnomyia</i><br> -  -  -  -  -  - <i>Zeugnomyia gracilis</i><br></div><p></p>
Vector shapefiles of the rural-urban interface in Portugal estimated for different periods (1990, 2000, 2006, 2012)
<p>Vector shapefiles of the rural-urban interface (RUI) in Portugal estimated for different periods (1990, 2000, 2006, 2012). RUI was mapped considering the first and the second level hierarchy of CORINE Land Cover (CLC). A specific geospatial approach was designed to extract the area of intersection between a buffer around the artificial surfaces and the area resulting from the sum of the forest and semi natural areas plus the heterogeneous agricultural areas. We adopted a buffer width of 1 km, corresponding to two times the spatial resolution of CLC inventories. </p> <p>The field “code_yy” refers to CLC class level 3. More about the Corine Land Cover (CLC) programme and datasets can be found at <a href="http://www.eea.eu">http://www.eea.eu</a></p> <p>More about the the RUI maps elaboration can be found at: <a href="https://doi.org/10.5194/nhess-18-1-2018">https://doi.org/10.5194/nhess-18-1-2018</a>. Please, cite data and the related paper if you use them. (cite as: Tonini., M., Parente., J., Pereira., M., Global assessment of rural-urban interface in Portugal related to land cover changes; Nat. Hazards Earth Syst. Sci., 18, 1–18, 2018)</p>
Microclimate and the development rate of mosquito vectors
<b>Description: </b><p>This data sets includes microclimate data, mosquito development rate and mosquito wing size measurements collected from primary forest, logged forest and oil palm plantations. Microclimate data was recorded using Ibutton data loggers which measured soil temeprature at given sample sites. All mosquito eggs collected were reared under field conditions. Each mosquito sample was monitored daily in order to record the proportion emerging at each developemnt stage (larva, pupa and adult) along with the numebr of transition days between each development stage. Adult wing length (of each adult mosquito collected) was used as a simple proxy to measure adult vectorial capacity. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/21"><b>The impact of altered forest microclimate on the development rate of mosquito vectors</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=110">here</a></p><p><b>Files: </b>This consists of 1 file: template_PsomosMosquitoes.xlsx</p><p><b>template_PsomosMosquitoes.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>Soil temperature</b> (described in worksheet Soiltemp)</p><p>Description: Datalogger records of soil temperature time series as sample sites</p><p>Number of fields: 4</p><p>Number of data rows: 875</p><p>Fields: </p><ul><li><b>Site</b>: SAFE Project sample site (Field type: Location)</li><li><b>Day</b>: Day of measurement; each site had records collected over seven days (Field type: Numeric)</li><li><b>Time</b>: Time of measurement (Field type: Time)</li><li><b>Soil Temperature</b>: Soil temperature (Field type: Numeric)</li></ul></li><li><p><b>Mosquito size</b> (described in worksheet Mosquito_wing_length)</p><p>Description: Wing measurements on individual mosquitoes</p><p>Number of fields: 3</p><p>Number of data rows: 119</p><p>Fields: </p><ul><li><b>Site</b>: SAFE Project sample site (Field type: Location)</li><li><b>SampleNumber</b>: Each replicate within a site represents a different mosquito that was measured (Field type: Replicate)</li><li><b>WingSize</b>: Adult mosquito wing length (Field type: Numeric Trait)</li></ul></li><li><p><b>Mosquito life history stages</b> (described in worksheet Development_data)</p><p>Description: Abundance and time frame for mosquito development</p><p>Number of fields: 8</p><p>Number of data rows: 26</p><p>Fields: </p><ul><li><b>Site</b>: SAFE Project sample site (Field type: Location)</li><li><b>Eggs</b>: Number eggs (Field type: Abundance)</li><li><b>Larvae</b>: Number larvae (Field type: Abundance)</li><li><b>Pupae</b>: Number pupae (Field type: Abundance)</li><li><b>Adults</b>: Number adults (Field type: Abundance)</li><li><b>egg-larvae</b>: Number of days to develop from egg to larvae (Field type: Numeric Trait)</li><li><b>larvae-pupae</b>: Number of days to develop from larvae to pupae (Field type: Numeric Trait)</li><li><b>pupae-adult</b>: Number of days to develop from pupae to adult (Field type: Numeric Trait)</li></ul></li></ol><p><b>Date range: </b>2015-05-01 to 2015-07-13</p><p><b>Latitudinal extent: </b>4.6532 to 4.7520</p><p><b>Longitudinal extent: </b>116.9635 to 117.5932</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Diptera<br> -  -  -  - Culicidae<br></div><p></p>
Data and Source Codes used in "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"
<p>Data and Source Codes used in the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>Advection Test (ADV): East-West A_TST (100km, Cube), C_TST (25km, Cube), E_TST (5km, Cube),</p> <p> North-South K_TST (100km, Cube), M_TST (25km, Cube), O_TST (5km, Cube) </p> <p>Barotropic Test (BAR): A_TST5 (100km, Cube), Y_TST4 (100km, RLL), C_TST3 (5km, Cube), C_TST1 (5km, RLL)</p> <p>Baroclinic Test (BCL): J_TST30 (100km, Cube), J_TST20 (100km, RLL)</p>
Data set associated with the paper "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"
<p>New data set associated with the revision of the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: <br> I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>The title of the paper has been changed to "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"</p> <p>New simulated data set of the advection test is in the folder ADVEC_NEW; New simulated data set of the barotropic instability test is in the folder BARO_NEW; New simulated data set of the baroclinic instability test is in the folder BCL_NEW</p>
mTagBFP2-expressing vectors for electroporation of marine protists
<p>Plasmid maps and genbank sequence files for the plasmids used for electroporation of model organism <em>Nannochloropsis oceanica</em> and environmental samples following the protocol <a href="https://www.protocols.io/view/fabrication-of-dna-constructs-by-gibson-assembly-a-7r8hm9w">Matute et al.</a></p> <p>EMS initiative from Gordon and Betty Moore foundation.</p>
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.