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914 results for “filter”
CNN-Filter-DB
<p><strong>A diverse database of over 1.4B 3x3 convolution filters extracted from CNN models trained for various tasks in diverse image domains.</strong></p> <p>We collected a total of 647 publicly available CNN models that have been pre-trained for various 2D visual tasks. In order to provide a heterogeneous and diverse representation of convolution filters "in the wild", we retrieved pre-trained models for 11 different tasks e.g. such as classification, segmentation} and image generation. We also recorded various meta-data such as depth and frequency of included operations for each model, and manually categorized the variety of used training sets into 16 visually distinctive groups like natural scenes, medical ct, seismic, or astronomy. In total, the models were trained on 71 different data sets. The dominant subset is formed by image classification models trained on ImageNet1k (355 models).</p> <p><strong>More details:</strong> <a href="https://github.com/paulgavrikov/cnn-filter-db">https://github.com/paulgavrikov/cnn-filter-db</a></p>
CNN-Filter-DB-Robust
<p>Dataset for the Paper "Adversarial Robustness through the Lens of Convolutional Filters".</p> <p><strong>More details:</strong> <a href="https://github.com/paulgavrikov/cnn-filter-db">https://github.com/paulgavrikov/cvpr22w_RobustnessThroughTheLens</a></p>
X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
Ocean surface currents, SSH and SST from LLC4320, before and after Lagrangian filtering
<p>This dataset comprises daily snapshots of horizontal velocity, sea surface height and sea surface temperature from LLC4320, a high resolution setup of the MITgcm, in the Agulhas region. We provide the unfiltered data, and the data after Lagrangian filtering as described in Jones, CS, Xiao, Q, Abernathey, RP and Smith, KS <em>Separating balanced and unbalanced flow at the surface of the Agulhas region using Lagrangian filtering (preprint: </em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a> ). Lagrangian filtering is not applied to the sea surface temperature.</p> <p>This dataset is not the dataset that was used to make the figures in Jones et al. (see <a href="https://doi.org/10.5281/zenodo.6574163">https://doi.org/10.5281/zenodo.6574163</a>), but a separate dataset that is meant to be used in future study. We have decided to make this dataset publicly available because it may be useful for machine learning, or for studies that investigate the dynamical equations that govern the sea surface height and horizontal velocity field.</p> <p>unfilt_u_v_ssh_sst.nc contains unfiltered horizontal velocity, sea surface height and sea surface temperature</p> <p>filt_u_v_ssh.nc contains horizontal velocity and sea surface height after Lagrangian filtering</p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1.</p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. </p> <p>Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as <span class="math-tex">\(\alpha = 0.3876\)</span> in Figure 5c changes to <span class="math-tex">\(\alpha = 0.4507\)</span> after correction. The resulting value of the ideal inflation factor hence changes from <span class="math-tex">\(I = {1 \over 0.3876} \approx 2.58\)</span> to <span class="math-tex">\(I = {1 \over 0.4507} \approx 2.22\)</span>. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to <span class="math-tex">\(I = 2.58\)</span> than to <span class="math-tex">\(I = 1\)</span> or <span class="math-tex">\(I = 5\)</span>, <em>i.e.</em> the three values that were evaluated.</p> <p>Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended. </p>
Dataset from : Browsing is a strong filter for savanna tree seedlings in their first growing season
<p>1: Newly germinated seedlings are vulnerable to biomass removal but usually have at least six months to grow before they are exposed to dry-season fires, a major disturbance in savannas. In contrast, plants are exposed to browsers from the time they germinate, making browsing potentially a very powerful bottleneck for establishing seedlings. 2: Here we assess the resilience of seedlings of 10 savanna tree species to top-kill during the first 6 months of growth. Newly-germinated seeds from four dominant African genera from across the rainfall gradient were planted in a common garden experiment at the Wits Rural Facility and clipped at 1 cm when they were ~2, 3, 4, and 5 months old. Survival, growth, and key plant traits were monitored for the following 2.5 years. 3: Seedlings from environments with high herbivory pressure survived top-kill at a younger age than those from low-herbivore environments, and more palatable genera had higher herbivore-tolerance. Most individuals that survived were able to recover lost biomass within 12 months, but the clipping treatment affected root mass fraction and branching patterns. 4: Synthesis: The impact of early browsing as a demographic bottleneck can be predicted by integrating information on the probability of being browsed and the probability of surviving a browse event. Establishment limitation through early-browsing is an under-recognised constraint on savanna tree species distributions. Data may be used without requesting permission after the the publication of the paper</p>
NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO2 exposure reduction potential
<p>In-vehicle and on-road (ambient) NO<sub>2</sub> measurements in different car cabin from Birmingham, UK. This dataset was used for the publication NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO<sub>2</sub> exposure reduction potential, Science of The total environment,160395 <a href="https://doi.org/10.1016/j.scitotenv.2022.160395">https://doi.org/10.1016/j.scitotenv.2022.160395</a></p>
Data for "Ecosystem size filters life-history strategies to shape community assembly in lakes"
<p>Dataset 1. List of 71 fish species collected from north temperate lakes in Wisconsin USA. Data include critical life-history data used for strategy classifications according to Winemiller and Rose (1992), principal component scores, and strategy classification according to the cluster analysis.</p> <p>Dataset 2. Species occurrence data in all study lakes along with results from the 'soft classification" according to Euclidean distance.</p> <p>Dataset 3. Limnological and fish community characteristics of study lakes including species richness, lake area, estimated lake volume, and convex hull statistics for the overall fish community and each life-history strategy type.</p>
A dataset for comparing filtering methods used to wave and non-wave flow at the surface of the Agulhas region
<p>This dataset comprises sea surface height (SSH) and velocity data at the ocean surface in two small regions near the Agulhas retroflection. The unfiltered SSH and a horizontal velocity field are provided, along with the same fields after various kinds of filtering, as described in the accompanying manuscript, <em>Using Lagrangian filtering to remove waves from the ocean surface velocity field</em><em> (</em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a>)<em>. </em>The code repository for this work is <a href="https://github.com/cspencerjones/separating-balanced">https://github.com/cspencerjones/separating-balanced</a> . </p> <p>Two time-resolutions are provided: two weeks of hourly data and 70 days of daily data.</p> <p>Seventy_daysA.nc contains daily data for region A and Seventy_daysB.nc contains daily data for region B, including unfiltered, lagrangian filtered and omega-filtered velocity and sea-surface height. </p> <p>two_weeksA.nc contains hourly data for region A and two_weeksB.nc contains hourly data for region B, including unfiltered and lagrangian filtered velocity and sea-surface height. </p> <p>Note that region A has been moved in version 2 of this dataset. </p> <p>See the manuscript and code repository for more information. </p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
SoundDesc: Cleaned and Group-Filtered Splits
<p>This upload contains dataset splits of <em>SoundDesc</em> [1] and other supporting material for our paper:</p> <p><strong>Data leakage in cross-modal retrieval training: A case study </strong><a href="https://arxiv.org/abs/2302.12258">[arXiv]</a> <a href="https://doi.org/10.1109/ICASSP49357.2023.10094617">[ieeexplore]</a></p> <p>In our paper, we demonstrated that a data leakage problem in the previously published splits of SoundDesc leads to overly optimistic retrieval results.<br> Using an off-the-shelf audio fingerprinting software, we identified that the data leakage stems from duplicates in the dataset.<br> We define two new splits for the dataset: a cleaned split to remove the leakage and a group-filtered to avoid other kinds of weak contamination of the test data.</p> <p>SoundDesc is a dataset which was automatically sourced from the BBC Sound Effects web page [2]. The results from our paper can be reproduced using <em>clean_split01</em> and <em>group_filtered_split01</em>.</p> <p><em>If you use the splits, please cite our work:</em></p> <p>Benno Weck, Xavier Serra, "Data Leakage in Cross-Modal Retrieval Training: A Case Study," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10094617.</p> <pre><code>@INPROCEEDINGS{10094617, author={Weck, Benno and Serra, Xavier}, booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Data Leakage in Cross-Modal Retrieval Training: A Case Study}, year={2023}, volume={}, number={}, pages={1-5}, doi={10.1109/ICASSP49357.2023.10094617}} </code></pre> <p>References:</p> <p>[1] A. S. Koepke, A. -M. Oncescu, J. Henriques, Z. Akata and S. Albanie, "Audio Retrieval with Natural Language Queries: A Benchmark Study," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2022.3149712.</p> <p>[2] https://sound-effects.bbcrewind.co.uk/</p>
Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data for April-July 2019 and 2020. We refer to the original research article below for further information. The footprint data were filtered with respect to predictive uncertainty and MODIS non-vegetated probability.</p><p>The unfiltered data organized in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data is available here:</p><p>April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a></p><p>April-July 2020: <a href="https://doi.org/10.5281/zenodo.7737869">https://doi.org/10.5281/zenodo.7737869</a></p><p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p><p><strong>Citation:</strong></p><p>Use of these data require citation of this dataset:</p><p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020 (1.0) [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7737946">https://doi.org/10.5281/zenodo.7737946</a></p><p>Original research article:</p><p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <i>Remote Sensing of Environment</i>, <i>268</i>, 112760.</p><p>This filtered dataset (2019 and 2020) was used to develop the global canopy height model fusing Sentinel-2 and GEDI that is presented in:</p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p>
The Spitzer Data Fusion Astronomical Photometric Filter Database
<p>The Spitzer Data Fusion Astronomical Photometric Filter Database - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a></p> <p>A collection of photometric filters from a variety of astronomical observatories by</p> <p>Lucia Marchetti (University of Cape Town) & Mattia Vaccari (University of Cape Town)</p> <p>The complete collection of filters is also available at: <a href="https://www.mattiavaccari.net/df/filters">https://www.mattiavaccari.net/df/filters</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Lucia Marchetti and Mattia Vaccari acknowledge financial support from the Inter-University Institute for Data Intensive Astronomy (IDIA), a partnership of the University of Cape Town, the University of Pretoria, the University of the Western Cape and the South African Radio Astronomy Observatory, and from the South African Department of Science and Innovation's National Research Foundation under the ISARP RADIOSKY2020 Joint Research Scheme (DSI-NRF Grant Number 113121) and the CSUR HIPPO Project (DSI-NRF Grant Number 121291).</p>
Native North American Silene (L.) Occurrences Filtered from GBIF
<p>This is a dataset including all Native <em>Silene</em> species accepted in taxonomic nomenclature and considered to inhabit the North American range. Data was downloaded using rgbif::occ_download and accessed from R via rgbif (https://github.com/ropensci/rgbif) on 2023-03-29. The original unfiltered GBIF occurrences can be download at https://doi.org/10.15468/dl.g89y2y, and https://api.gbif.org/v1/occurrence/download/request/0128277-230224095556074.zip. The data is filtered to have coordinates in North America, no geospatial issues, no spatial duplicates filtered to the infraspecific epithet, no coordinate uncertainty greater than 100000 meters, and no occurrences lying within 1km of a college or university. This dataset is incomplete as it does not include ALL observations that occur in North American countries as observations lacking a continent field of "north_america" in GBIF are not included.</p>
Perturbed Parameters for ICEPACK-DART Study Titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation"
<p>The file contains the values of the two perturbed CICE parameters that were used in the study titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation." The tw perturbed parameters are the standard deviation of the dry snow grain radius (Rsnow), and the thermal conductivity of snow (Ksnow). There are 80 values since the ensemble used in the study had 80 members.</p>
Phenotypic differences between interfertile Chlamydomonas species- focus-filtered timelapse data and measurements
<p>This repository contains focus-filtered timelapse microscopy data of two interfertile <i>Chlamydomonas</i> algal species. The protocol to generate this data is described in the associated publication, <a href="https://doi.org/10.57844/arcadia-35f0-3e16">"Phenotypic differences between interfertile <i>Chlamydomonas</i> species"</a>, and summarized here. Cells were collected from agar plates and suspended in water, then left to sit overnight to encourage gamete formation. During this time, non-motile cells settled, allowing for the enrichment of motile cells in the supernatant. These enriched cells were then loaded onto agar microchambers (100 micron diameter and 40 micron depth) for imaging. We collected videos on a Nikon Ti2-E microscope equipped with a Photometrics Kinetix digital scMos camera. We performed differential interference contrast (DIC) imaging using a Plan Apo 10× 0.45 Air objective. We collected videos with a 5.1 ms exposure with acquisition every 50 ms for three minutes. We placed a red light filter [IR longpass, 610 nm (ThorLabs)] in the light path to maintain swimming behavior of cells. The procedure was standardized and repeated four times to ensure consistency. Focus-filtered timelapse data of <i>C. reinhardtii </i>or C<i>. smithii </i>cells in agar microchamber wells are shared here. The code for focus-filtering and collection of measurements can be found in the <a href="https://github.com/Arcadia-Science/chlamy-comparison">associated Github repository</a>.</p><h4>Reference</h4><p><a href="https://doi.org/10.57844/arcadia-35f0-3e16">Essock-Burns T, Garcia III G, MacQuarrie CD, Mets DG, York R. (2023). Phenotypic differences between interfertile <i>Chlamydomonas </i>species</a></p><h4>Notes</h4><p>In addition to the raw data, the dataset includes sample images that are intermediates in the image processing pipeline, as well as 2D morphology measurements of the cells in a csv file.</p><p>"Cr" indicates <i>Chlamydomonas reinhardtii</i></p><p>"Cs" indicates <i>Chlamydomonas smithii</i></p><p>Frame rate: 20 frames per second (fps)</p><p>Pixel size: 0.6398 microns/pixel</p>
INP Teflon filter measurements and OPC data from the WesCon campaign
<p>Ice Nucleating Particle (INP) measurements taken during the Met Office's Wessex Convection Experiment (WesCon) in Jun-Aug 2023. The samples were collected on Sartorius 47 mm 1.2 um PTFE filters onboard the FAAM aircraft and at the Netheravon field site (51.24 deg N, 1.78 deg W). Aircraft filters are in the "FAAM Subtracted Samples" folder and start with a "C". Ground measurements are in the "Digitel Subtracted Samples" folder and start with "S". Information about each measurement can be found in the respective info csv file. Handling blanks were taken throughout the campaign and the data has been background subtracted.</p> <p> </p> <p>The filters were analysed through the drop-on method, which is used in Price et al., 2014 and Raif et al., 2024 (Preprint).</p>
NEON Biorepository Benthic Microbe Collection (Sterivex Filters) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains benthic biofilm samples collected on 67 mm long, 1.7 cm diameter, 0.22 um Sterivex capsule filters (NEON sample class: amb_fieldParent_in.archiveID). Benthic biofilm samples are collected 3 times per year at the same time and location as periphyton (microalgae) samples and microbe samples sent for sequencing analysis, three times per year in wadeable streams during aquatic biology bout windows, roughly in spring, summer, and fall. Benthic biofilms are not collected in lakes and rivers. Samples are collected from rock and wood scrubs using field-sterile methods, and filtered through a 0.22 um Sterivex SVGP capsule filters. In wadeable streams, periphyton samples are collected in the two most dominant benthic habitat types (e.g. riffles, runs, pools, step pools). Sterivex filters are capped and flash-frozen in the field and then shipped to the Biorepository to be archived at -80 degrees Celsius. See related links below for protocols and NEON related data products.
NEON Biorepository Particulate Mass Filter Collection (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains quartz microfiber particulate mass filters (NEON sample class: dpm_fieldData_in.sampleID and dpm_filterBlank_in.sampleID). Particulate mass sampling is executed at six of NEON's terrestrial sites, located in Domains 10, 13, and 15. The subset of sites included for sampling are those in the Basin and Range, Eastern and Western slopes of the Rocky Mountains, and the Eastern plains of Colorado. This selection of sites enables focus on transportation of particulate matter from the Great Basin and the Colorado Plateau by prevailing westerly winds over the Colorado Rocky Mountains, to receptor sites in the Rockies and Great Plains. Samples are collected by an automated assembly that pulls air through a quartz microfiber filter with a porosity of 10 micrometers, to collect PM10. Filters are weighed at high precision pre- and post-deployment at the Colorado Department of Public Health and Environment Air Resources Laboratory to determine dust deposition mass. Filters are then shipped to the Biorepository to be archived at 4 degrees Celsius in air-tight plastic sleeves. Subsamples of the filters are available to the science community upon request to enable the assessment of chemical and nutrient inputs in the region. Additionally, 5 filter blanks from each box of filters are archived and available upon request. See related links below for protocols and NEON related data products.
NEON Biorepository Surface Water Microbe Collection (Sterivex Filters) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains surface water microbe samples collected on 67 mm long, 1.7 cm diameter, 0.22 um Sterivex capsule filters (NEON sample class: amc_fieldCellCounts_in.archiveID). Surface water microbe samples are collected at the same time and location as surface water cell count samples and surface water chemistry samples once per month in wadeable streams (12 times per year) and every-other month in lakes and rivers (6 times per year). Details on sampling locations and timing are provided in the NEON document titled Surface Water Chemistry Sampling in Aquatic Habitats (https://data.neonscience.org/documents). In wadeable streams, surface water microbe samples are collected near the downstream S2 sensor location. In lakes, microbial samples are collected near the the 'buoy', 'littoral 1', and 'littoral 2' sensors, and sampling depth(s) is dependent on lake stratification. In rivers, microbial samples are collected near the buoy sensor. Water samples are filtered on 0.22 um Sterivex capsule filters, capped and flash-frozen in the field. Sterivex filters are archived at the NEON Biorepository at -80 degrees Celsius. See related links below for protocol.
Dataset and Simulation Files for article "Bright and Vivid Diffractive-Plasmonic Reflective Filters for Color Generation"
<p>This work was supported by Ministério da Ciência Tecnologia, Inovações e Comunicações, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil, Finance, Code 001, National Counsel of Technological and Scientific Development, and São Paulo Research Foundation (Fapesp) through grants 2018/15580-6, 2018/15577-5, 2016/18308-0, 2012/ 17610-3, and 2012/17765-7. Part of the results presented in this work were obtained through Project 4716-11, funded by Samsung Eletrônica da Amazônia Ltda., under the Brazilian Informatics Law 8.248/91. The authors thank the Center for Semiconductor Components and Nanotechnologies for the nanofabrication infrastructure.</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.