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594 results for “REACH”
Lower American River restoration snorkel surveys at the project and control reaches
Sacramento Water Forum will implement spawning and rearing habitat enhancement projects on the Lower American River at the Lower Sailor Bar, Nimbus Basin, and Upper River Bend reaches. Enhancements include installation of gravel to restore over 18 acres of spawning habitat and in-channel/floodplain grading to create over 14 acres of rearing habitat. Construction of Lower Sailor Bar and Nimbus Basin were completed in summer 2022 and Upper River Bend was completed in summer 2023.The goal of the projects is to increase existing spawning and rearing habitat for salmonids under typical flows. This work supports effectiveness monitoring for this project, including spawning and rearing (snorkel) surveys before and after restoration. This work also informs performance metrics and adaptive management strategies.
Pre- and Post- construction snorkel surveys at the project and reach stretches, Yuba River, CA, 2014 through 2024
Cramer Fish Sciences (CFS), cbec, inc. ecoengineering, and South Yuba River Citizen’s League, funded and directed by the United States Fish and Wildlife Service’s Anadromous Fish Restoration Program (USFWS AFRP), teamed to plan, design, monitor, and perform regulatory compliance for the Hallwood Side Channel and Floodplain Restoration Project (Project) on the Yuba River, California. The Project is designed to restore and enhance ecosystem processes, with a primary focus on improving productive juvenile salmonid rearing habitat to increase natural production of fall and spring-run Chinook Salmon Oncorhynchus tshawytscha and steelhead O. mykiss in the Yuba River. The Project enhanced up to 157 acres of seasonally inundated riparian floodplain habitats, 1.7 miles of perennial side and alcove channels, and more than 6.1 miles of seasonal side channels. The design approach focused on removing unnatural constraints (such as a mid-river training wall and very coarse surface materials left from mining activities) in order to allow natural river and floodplain processes to function. Construction planning efforts included multi-year phasing to remove about 3.2 million cubic yards of material from the site while optimizing habitat establishment in early years and minimizing disturbance to existing high quality riparian and aquatic habitat. The Project included a robust monitoring program that measured the effect of restoration on a range of ecological parameters thought to influence salmonid habitat use and productivity and riparian ecosystem function using a Before-After-Control-Impact study framework. Specifically, we monitored salmonid and non-native predator density, juvenile salmonid growth and residence time, predation, invertebrate prey (drift) density and biomass, and changes in acreage of a range of habitat types, including terrestrial and aquatic vegetation. We also examined factors influencing natural riparian tree recruitment following restoration.
Lower American River restoration spawning surveys at project and control reaches (2022 - 2024)
Sacramento Water Forum has implemented spawning and rearing habitat enhancement projects on the Lower American River at the Lower Sailor Bar, Nimbus Basin, Upper River Bend and Lower River Bend reaches. Enhancements include installation of gravel to restore over 28 acres of spawning habitat and in-channel/floodplain grading to create over 44 acres of rearing habitat. Construction of Lower Sailor Bar and Nimbus Basin were completed in summer 2022, Upper River Bend was completed in summer 2023, and Lower River Bend was completed in summer 2024. The goal of the projects is to increase existing spawning and rearing habitat for salmonids under typical flows. This work supports effectiveness monitoring for this project, including spawning and rearing (snorkel) surveys before and after restoration. This work also informs performance metrics and adaptive management strategies.
Ecosystem metabolism and associated environmental data for a forested, meadow and reforested reach of White Clay Creek, Chester Co., Pennsylvania; 1971-1975 and 1997-2010
Ecosystem metabolism data for a 3rd-order Piedmont stream were collected during two periods: P1- April 1971 – Dec 1975, and P2- May 1997 – January 2010. Measures were made in a meadow and a forested reach during each period and in a reforested (formerly meadow) reach during the latter years of P2. During P1, measures were made by transferring streambed substrata to chambers in water jackets located on the streambank and measuring dissolved oxygen changes over diel periods. During P2, open system measures of dissolved O2 change were made for several days in warm and cold seasons, with reaeration determined from a propane injection experiment. Metabolism estimates were determined from diel curves of dissolved O2 change. Photosynthetically active radiation (PAR) and chlorophyll were measured concurrent with many measurements in P1 and all measures during P2, and temperature with all measures. Water chemistry parameters (NH4-N, NO3-N, PO4-P, SiO2, Cl, SO4, total alkalinity, pH) associated with each run are included in the data set, as are days since storm of various thresholds. Field procedures, analytical methods and data analyses are detailed in Bott, T.L. & J. D. Newbold, 2023. A multi-year analysis of factors affecting ecosystem metabolism in forested and meadow reaches of a Piedmont Stream. Hydrobiologia
Invertebrate Community Asemblage from the Arctic LTER Upper Kuparuk River Reference (2001-2012) and Fertilized Reach (2002-2016), Toolik Field Station, Alaska
Surber sampler (25 X 25 cm frame fitted with a 243 um mesh net) was used to sample invertebrates at on the Kuparuk River in Reference (2001-2012) and Fertilized Reach (2002-2016) reach.
S100 | PFASREACH | List of PFAS identified in REACH 2019
<p>This is the collection associated with list S100 PFASREACH List of PFAS identified in REACH 2019 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of 437 PFAS identified in Registration, Evaluation, Authorisation and Restriction of Chemicals <a href="https://eur-lex.europa.eu/eli/reg/2008/1272">(REACH) Reg. (EC) No 1272/2008</a> in September 2019. Of these, 17 are produced at >1000 tonnes/year and 84 at >10 tonnes per year for use in Europe. Collaborative effort between Hans Peter Arp (NGI) and Emma Schymanski (LCSB) within <a href="https://zenodo.org/communities/zeropm-h2020?page=1&size=20">ZeroPM</a> (EU H2020 grant 101036756).</p> <p>Updates: 14 Dec 2022: added 5 new CIDs following PubChem deposition. 16 April 2025: removed CID <a href="https://pubchem.ncbi.nlm.nih.gov/compound/10996402">10996402</a> and <a href="https://pubchem.ncbi.nlm.nih.gov/compound/21967041">21967041</a> as they are not PFAS, due to external report via PubChem. </p>
S36 | UBAPMT | Prioritised PMT/vPvM substances in the REACH registration database
<p><strong>Prioritised PMT/vPvM substances in the REACH registration database</strong></p> <p>This is the 2022 update (first update) of the UBA list of prioritised persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) substances in the REACH registration database. All substances are registered under REACH (EC No 1907/2006) and meet the <a href="https://www.umweltbundesamt.de/publikationen/protecting-the-sources-of-our-drinking-water-the">PMT/vPvM criteria as proposed by UBA in 2019</a>. Compared to the first version from 2019, this 2022 update of the UBA list adds new substances and improved the PMT/vPvM assessment. This UBA list is published as UBA TEXTE xxx /2022. It is indicated if a substance would also meet the less stringent PMT/vPvM criteria as published by the European Commission (EC) in September 2021, which are currently under discussion for inclusion in the Classification, Labelling and Packaging (<a href="https://echa.europa.eu/guidance-documents/guidance-on-clp">CLP</a>) regulation (EC No 1272/2008).</p> <p><em>Reference: </em>Hans Peter H Arp, Sarah E Hale, Ivo Schliebner and Michael Neumann (2022). Prioritised PMT/vPvM substances in the REACH registration database, Texte | XXX/2022, edited by Michael Neumann and Ivo Schliebner, IV 2.3 Chemicals, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804 xxx pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0 and Report No. (to be announced)</p> <p><em>Previous version:</em></p> <p>The first version of this UBA list from 2019 was published as a <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">technical note (UBA TEXTE 126/2019)</a>.</p> <p><em>Reference: </em>Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN:1862-4804, 131 pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG.</p> <p>This collection is associated with list S36 UBAPMT on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>).</p>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Responsive Environmental Assessment Commercially Hosted (REACH)
<p>This is the historical REACH data release. These are the “v3” files produced at The Aerospace Corporation, spanning March 2017 until December 2019. It is accompanied by a README (PDF), which includes a brief mission description, describes the features of the collection of dosimeters, discusses data availability throughout the dataset, describes data quality flags, and concludes with a data dictionary.</p>
Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality Dataset
<p>This dataset accompanies the paper “Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality” published in the Computer and Graphics journal from Elsevier. It contains 3 datasets related to the experiments described in the paper. All datasets are in “.csv” format and can be easily loaded by statistical analysis tools (e.g. a dataset can be loaded in r using the command read.csv(“filename.csv”)). It also contains the C# Unity implementation of the distortion function presented in the paper.</p> <p>Paper reference:</p> <p>Galvan Debarba H, Boulic R, Salomon R, Blanke O, Herbelin B. Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality. Computers & Graphics. 2018; ISSN 0097-8493. Elsevier.</p> <p>DOI: doi.org/10.1016/j.cag.2018.09.001</p>
Participatory activities good practices in the field of cultural heritage (REACH project)
<p>The REACH repository of good practices comprises over a hundred and twenty records of European and extra European participatory activities in the field of cultural heritage, with an emphasis on small-scale, localised examples, but including also larger collaborative projects and global or distributed online initiatives. Located in over twenty different countries, the activities showcased here cover a wide variety of topics and themes, from urban, rural and institutional heritage to indigenous and minority heritage; from preservation, and management to use and re-use of cultural heritage. This easy-to-use collection of good practices offers professionals, practitioners, researchers and citizens useful information about activities which could be transferred, adapted or replicated in new contexts.</p>
Shrimp populations variability in numbers and sizes in response to disturbance and seasons on 20 pools along the reach of Quebrada Prieta, Luquillo Experimental Forest
Shrimp populations were monitored at approximately 3 week intervals to determine the variability in numbers and sizes of each species in response to disturbance and seasons. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
S63 | UBADWGW | REACH Registered Substances Detected in Drinking (DW) or Groundwater (GW)
<p><strong>REACH Registered Substances Detected in Drinking (DW) or Groundwater (GW)</strong></p> <p>This is a literature review and a subsequent persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) assessment published by the German Environment Agency (UBA) in 2019. All substances are reported in literature to have been detected in drinking water and groundwater. This list contains only those substance that are registered under REACH (EC No 1907/2006). It is assessed whether a substance meets the PMT/vPvM criteria as proposed by the German Environment Agency (UBA) in 2019 <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">(UBA TEXTE 126/2019)</a>.</p> <p><em>Reference</em>: Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804, 131 pages.</p> <p><em>Acknowledgement:</em> Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG</p> <p>This is the collection associated with list S63 UBADWGW on the <a href="https://www.norman-network.com/nds/SLE/">NORMAN Suspect List Exchange</a>.</p>
Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology
<p><strong>General Description.</strong> This dataset consists of:</p> <ol> <li>The threshold crossing times of extracellularly and simultaneously recorded spikes, sorted into units (up to five, including a "hash" unit), along with sorted waveform snippets, and,</li> <li>The x,y position of the fingertip of the reaching hand and the x,y position of reaching targets (both sampled at 250 Hz).</li> </ol> <p>The behavioral task was to make self-paced reaches to targets arranged in a grid (e.g. 8x8) without gaps or pre-movement delay intervals. One monkey reached with the right arm (recordings made in the left hemisphere); The other reached with the left arm (right hemisphere). In some sessions recordings were made from both M1 and S1 arrays (192 channels); in most sessions M1 recordings were made alone (96 channels).</p> <p>Data from two primate subjects are included: 37 sessions from monkey 1 ("Indy", spanning about 10 months) and 10 sessions from monkey 2 ("Loco", spanning about 1 month), for a total of ~ 20,000 reaches and 6,500 reaches from monkeys 1 and 2, respectively.</p> <p><strong>Possible uses. </strong>These data are ideal for training BCI decoders, in particular because they are not segmented into trials. We expect that the dataset will be valuable for researchers who wish to design improved models of sensorimotor cortical spiking or provide an equal footing for comparing different BCI decoders. Other uses could include analyses of the statistics of arm kinematics, spike noise-correlations or signal-correlations, or for exploring the stability or variability of extracellular recording over sessions.</p> <p><strong>Variable names. </strong>Each file contains data in the following format. In the below, <em>n</em> refers to the number of recording channels, <em>u</em> refers to the number of sorted units, and <em>k</em> refers to the number of samples.</p> <ul> <li>chan_names - n x 1 <ul> <li>A cell array of channel identifier strings, e.g. "<em>M1 001</em>".</li> </ul> </li> <li>cursor_pos - k x 2 <ul> <li>The position of the cursor in Cartesian coordinates (x, y), mm.</li> </ul> </li> <li>finger_pos - k x 3 <em>or </em>k x 6 <ul> <li>The position of the working fingertip in Cartesian coordinates (z, -x, -y), as reported by the hand tracker in cm. Thus the cursor position is an affine transformation of fingertip position using the following matrix:<br>\(\begin{pmatrix} 0 & 0 \\ -10 & 0 \\ 0 & -10 \end{pmatrix}\)<br>Note that for some sessions finger_pos includes the orientation of the sensor as well; the full state is thus: (z, -x, -y, azimuth, elevation, roll).</li> </ul> </li> <li>target_pos - k x 2 <ul> <li>The position of the target in Cartesian coordinates (x, y), mm.</li> </ul> </li> <li>t - k x 1 <ul> <li>The timestamp corresponding to each sample of the cursor_pos, finger_pos, and target_pos, seconds.</li> </ul> </li> <li>spikes - n x u <ul> <li>A cell array of spike event vectors. Each element in the cell array is a vector of spike event timestamps, in seconds. The first unit (<em>u</em>1) is the "unsorted" unit, meaning it contains the threshold crossings which remained after the spikes on that channel were sorted into other units (<em>u</em>2, <em>u</em>3, etc.) For some sessions spikes were sorted into up to 2 units (i.e. <em>u</em>=3); for others, 4 units (<em>u</em>=5).</li> </ul> </li> <li>wf - n x u <ul> <li>A cell array of spike event waveform "snippets". Each element in the cell array is a matrix of spike event waveforms. Each waveform corresponds to a timestamp in "spikes". Waveform samples are in microvolts.</li> </ul> </li> </ul> <p><strong>Decoder Results.</strong> These data were used to fit decoder models, as reported in Makin, et al [1]. To aid comparisons to other decoders, we include performance summaries (for each session, decoder, bin-width, etc.) in the file <em>refh_results.csv</em>, containing the following columns:</p> <ul> <li>session - a session identifier, e.g. "indy_20160407_02"</li> <li>monkey - one of, "indy" or "loco"</li> <li>num_neurons - total number of features used in the decoder</li> <li>num_training_samples - number of samples (at the specified bin-width) used to train the decoder (sequential, from file start)</li> <li>num_testing_samples - number of samples used to evaluate the decoder (sequential, until file end)</li> <li>kinematic_axis - one of, "posx", "posy", "velx", "vely", "accx" or "accy"</li> <li>bin_width - one of, "16", "32", "64" or "128"</li> <li>decoder - one of, "regression", "KF_observed", "KF_static", "KF_dynamic", "UKF", "rEFH_static" or "rEFH_dynamic"</li> <li>rsq - coefficient of determination, R2</li> <li>snr - Signal to noise ratio, SNR := -10 log10(1 - R2)</li> </ul> <p><strong>Videos. </strong>For some sessions, we recorded screencasts of the stimulus presentation display using a dedicated hardware video grabber. These screencasts are thus a faithful representation of the stimuli and feedback presented to the monkey and are available for the following sessions:</p> <ul> <li><a href="https://youtu.be/bPkpdpm03z8">indy_20160921_01</a></li> <li><a href="https://youtu.be/B02z6w4c3yk">indy_20160930_02</a></li> <li><a href="https://youtu.be/S640zzIKJs8">indy_20160930_05</a></li> <li><a href="https://youtu.be/tRoe84E0AzA">indy_20161005_06</a></li> <li><a href="https://youtu.be/hNZlBa516jM">indy_20161006_02</a></li> <li><a href="https://youtu.be/L6GKwI2u1Es">indy_20161007_02</a></li> <li><a href="https://youtu.be/eV1joYU5vt0">indy_20161011_03</a></li> <li><a href="https://youtu.be/4LM_gKt2cYg">indy_20161013_03</a></li> <li><a href="https://youtu.be/GLGrKHgf-zw">indy_20161014_04</a></li> <li><a href="https://youtu.be/6aPrv8HEPGQ">indy_20161017_02</a></li> </ul> <p><strong>Supplements. </strong>The raw broadband neural recordings that the spike trains in this dataset were extracted from are available for the following sessions:</p> <ul> <li>indy_20160622_01: <a href="https://doi.org/10.5281/zenodo.1488440">doi:10.5281/zenodo.1488440</a></li> <li>indy_20160624_03: <a href="https://doi.org/10.5281/zenodo.1486147">doi:10.5281/zenodo.1486147</a></li> <li>indy_20160627_01: <a href="https://doi.org/10.5281/zenodo.1484824">doi:10.5281/zenodo.1484824</a></li> <li>indy_20160630_01: <a href="https://doi.org/10.5281/zenodo.1473703">doi:10.5281/zenodo.1473703</a></li> <li>indy_20160915_01: <a href="https://doi.org/10.5281/zenodo.1467953">doi:10.5281/zenodo.1467953</a></li> <li>indy_20160916_01: <a href="https://doi.org/10.5281/zenodo.1467050">doi:10.5281/zenodo.1467050</a></li> <li>indy_20160921_01: <a href="https://doi.org/10.5281/zenodo.1451793">doi:10.5281/zenodo.1451793</a></li> <li>indy_20160927_04: <a href="https://doi.org/10.5281/zenodo.1433942">doi:10.5281/zenodo.1433942</a></li> <li>indy_20160927_06: <a href="https://doi.org/10.5281/zenodo.1432818">doi:10.5281/zenodo.1432818</a></li> <li>indy_20160930_02: <a href="https://doi.org/10.5281/zenodo.1421880">doi:10.5281/zenodo.1421880</a></li> <li>indy_20160930_05: <a href="https://doi.org/10.5281/zenodo.1421310">doi:10.5281/zenodo.1421310</a></li> <li>indy_20161005_06: <a href="https://doi.org/10.5281/zenodo.1419774">doi:10.5281/zenodo.1419774</a></li> <li>indy_20161006_02: <a href="https://doi.org/10.5281/zenodo.1419172">doi:10.5281/zenodo.1419172</a></li> <li>indy_20161007_02: <a href="https://doi.org/10.5281/zenodo.1413592">doi:10.5281/zenodo.1413592</a></li> <li>indy_20161011_03: <a href="https://doi.org/10.5281/zenodo.1412635">doi:10.5281/zenodo.1412635</a></li> <li>indy_20161013_03: <a href="https://doi.org/10.5281/zenodo.1412094">doi:10.5281/zenodo.1412094</a></li> <li>indy_20161014_04: <a href="https://doi.org/10.5281/zenodo.1411978">doi:10.5281/zenodo.1411978</a></li> <li>indy_20161017_02: <a href="https://doi.org/10.5281/zenodo.1411882">doi:10.5281/zenodo.1411882</a></li> <li>indy_20161024_03: <a href="https://doi.org/10.5281/zenodo.1411474">doi:10.5281/zenodo.1411474</a></li> <li>indy_20161025_04: <a href="https://doi.org/10.5281/zenodo.1410423">doi:10.5281/zenodo.1410423</a></li> <li>indy_20161026_03: <a href="https://doi.org/10.5281/zenodo.1321264">doi:10.5281/zenodo.1321264</a></li> <li>indy_20161027_03: <a href="https://doi.org/10.5281/zenodo.1321256">doi:10.5281/zenodo.1321256</a></li> <li>indy_20161206_02: <a href="https://doi.org/10.5281/zenodo.1303720">doi:10.5281/zenodo.1303720</a></li> <li>indy_20161207_02: <a href="https://doi.org/10.5281/zenodo.1302866">doi:10.5281/zenodo.1302866</a></li> <li>indy_20161212_02: <a href="https://doi.org/10.5281/zenodo.1302832">doi:10.5281/zenodo.1302832</a></li> <li>indy_20161220_02: <a href="https://doi.org/10.5281/zenodo.1301045">doi:10.5281/zenodo.1301045</a></li> <li>indy_20170123_02: <a href="https://doi.org/10.5281/zenodo.1167965">doi:10.5281/zenodo.1167965</a></li> <li>indy_20170124_01: <a href="https://doi.org/10.5281/zenodo.1163026">doi:10.5281/zenodo.1163026</a></li> <li>indy_20170127_03: <a href="https://doi.org/10.5281/zenodo.1161225">doi:10.5281/zenodo.1161225</a></li> <li>indy_20170131_02: <a href="https://doi.org/10.5281/zenodo.854733">doi:10.5281/zenodo.854733</a></li> </ul> <p><strong>Contact Information.</strong> We would be delighted to hear from you if you find this dataset valuable, especially if it leads to publication. Corresponding author: J. E. O'Doherty <joeyo@neuroengineer.com>.</p> <p><strong>Citation.</strong></p> <p>@misc{ODoherty:2017, author = {O'{D}oherty, Joseph E. and Cardoso, Mariana M. B. and Makin, Joseph G. and Sabes, Philip N.}, title = {Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex electrophysiology}, doi = {10.5281/zenodo.788569}, url = {https://doi.org/10.5281/zenodo.788569}, month = may, year = {2017} }</p> <p><strong>Publications making use of this dataset.</strong></p> <ol> <li>Makin, J. G., O'Doherty, J. E., Cardoso, M. M. B. & Sabes, P. N. (2018). Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm. <em>J Neural Eng.</em> 15(2): 026010. <a href="https://doi.org/10.1088/1741-2552/aa9e95">doi:10.1088/1741-2552/aa9e95</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2018). Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces. <em>2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em>, Honolulu, HI, USA, 2018, pp. 2547-2550. <a href="https://doi.org/10.1109/EMBC.2018.8512830">doi:10.1109/EMBC.2018.8512830</a></li> <li>Balasubramanian, M., Ruiz, T., Cook, B., Bhattacharyya, S., Prabhat, Shrivastava, A. & Bouchard K. (2018). Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale. <em>arXiv Preprint.</em> <a href="https://arxiv.org/abs/1808.06992">arXiv:1808.06992</a></li> <li>Sachdeva, P. S., Bhattacharyya, S., & Bouchard, K. E. (2019). Sparse, Predictive, and Interpretable Functional Connectomics with UoILasso, <em>41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em>, Berlin, Germany, pp. 1965-1968. <a href="https://doi.org/10.1109/EMBC.2019.8856316">doi:10.1109/EMBC.2019.8856316</a></li> <li>Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2019). End-to-End Hand Kinematic Decoding from LFPs Using Temporal Convolutional Network. <em>2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), </em>Nara, Japan, pp. 1-4. <a href="https://doi.org/10.1109/biocas.2019.8919131">doi:10.1109/biocas.2019.8919131</a></li> <li>Bose, S. K., Acharya, J., & Basu, A. (2019). Is my Neural Network Neuromorphic? 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Architectural exploration of hybrid neural decoders for neuromorphic implantable BMI. <em>arXiv Preprint. </em>arXiv:XXXX</li> <li>Yik, J., Berghe, K., Blanken, D., Bouhadjar, Y., Fabre, M., Hueber, P., Ke, W., Khoei, M. A., Kleyko, D., Pacik-Nelson, N., Pierro, A., Stratmann, P., Sun, P. V., Tang, G., Wang, S., Zhou, B., Ahmed, S. H., Vathakkattil Joseph, G., Leto, B., Micheli, A., Mishra, A. K., Lenz, G., Sun, T., Ahmed, Z., Akl, M., Anderson, B., Andreou, A. G., Bartolozzi, C., Basu, A., Bogdan, P., Bohte, S., Buckley, S., Cauwenberghs, G., Chicca, E., Corradi, F., Croon, G., Danielescu, A., Daram, A., Davies, M., Demirag, Y., Eshraghian, J., Fischer, T., Forest, J., Fra, V., Furber, S., Furlong, P. M., Gilpin, W., Gilra, A., Gonzalez, H. A., Indiveri, G., Joshi, S., Karia, V., Khacef, L., Knight, J. C., Kriener, L., Kubendran, R., Kudithipudi, D., Liu, S., Liu, Y., Ma, H., Manohar, R., Margarit-Taulé, J. M., Mayr, C., Michmizos, K., Muir, D. R., Neftci, E., Nowotny, T., Ottati, F., Ozcelikkale, A., Panda, P., Park, J., Payvand, M., Pehle, C., Petrovici, M. A., Posch, C., Renner, A., Sandamirskaya, Y., Schaefer, C. J. S., Schaik, A., Schemmel, J., Schmidgall, S., Schuman, C., Seo, J., Sheik, S., Shrestha, S. B., Sifalakis, M., Sironi, A., Stewart, K., Stewart, M., Stewart, T. C., Timcheck, J., Tömen, N., Urgese, G., Verhelst, M., Vineyard, C. M., Vogginger, B., Yousefzadeh, A., Zohora, F. T., Frenkel, C. & Reddi, V. J. (2025). The neurobench framework for benchmarking neuromorphic computing algorithms and systems. <em>Nature Communications. </em>16(1): 1545. doi:10.1038/s41467-025-56739-4</li> <li>Zheng, J., Li, Y., Chen, L., Wang, F., Gu, B., Sun, Q., Gao, X. & Zhou, F. (2025). Effects of packet loss on neural decoding effectiveness in wireless transmission. <em>Brain Sciences.</em> 15(3): 221. doi:10.3390/brainsci15030221</li> </ol> <p><strong>History.</strong></p> <ul> <li>Version 2 - added CSV of results from Makin et al.</li> <li>Version 1 - initial release.</li> </ul>
30-m Spatial Resolution Bioclimatic Dataset of 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches
<p><strong>Brief Introduction of the Dataset</strong></p> <p>This bioclimatic dataset is the product of research article "Mapping 30-m Resolution Bioclimatic Variables During 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches." published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.</p> <p>The dataset contains 19 30-m resolution average bioclimatic variables during 1991-2020 Climate Normals for Hubei Province (108°21′42″—116°07′50″ E, 29°01′53″—33°6′47″ N), the core region of the Yangtze River middle reaches. The dataset was constructed by statistically downscaling the Climatic Research Unit (CRU) 1-km monthly climate variables (1440 in total), cablirating with ground observation data with 82 weather stations and aggregating based on the defination of 19 bioclimatic variables. The downscaling of four 1-km Climatic Research Unit monthly climate variables including monthly maximum, mean, minimum temperature and precipitation was firstly achieved by random forest model with 30-m resolution terrain and spatial data. Then the interpolation-based geographical differential analysis (GDA) was applied to improve the accuracy of downscaled products based on ground observation data. Finally, the bioclimatic variables were aggregated based on their definitions and averaged for the 30 years. The Yangtze River middle reaches is abundant of forestry, agriculture, biodiversity resources that requires finer bioclimatic data for better understands of these aspects. This dataset will provide higher spatial accuracy, more information and applicability in finer regional studies in the Yangtze River middle reaches.</p> <p> </p> <p><strong>Description of the 19 Bioclimatic Variables</strong></p> <p>The dataset contains 19 geotiff files in total. File names and the corresponding full name of bioclimatic variables are described as follows:</p> <p>Bio01 Mean annual air temperature (℃)<br>Bio02 Mean diurnal air temperature range (℃)<br>Bio03 Isothermality (%)<br>Bio04 Temperature seasonality (℃)<br>Bio05 Mean daily maximum air temperature of the warmest month (℃)<br>Bio06 Mean daily minimum air temperature of the coldest month (℃)<br>Bio07 Annual range of air temperature (℃)<br>Bio08 Mean daily mean air temperatures of the wettest quarter (℃)<br>Bio09 Mean daily mean air temperatures of the driest quarter (℃)<br>Bio10 Mean daily mean air temperatures of the warmest quarter (℃)<br>Bio11 Mean daily mean air temperatures of the coldest quarter (℃)<br>Bio12 Annual precipitation amount (mm)<br>Bio13 Precipitation amount of the wettest month (mm)<br>Bio14 Precipitation amount of the driest month (mm)<br>Bio15 Precipitation seasonality (%)<br>Bio16 Precipitation amount of the wettest quarter (mm)<br>Bio17 Precipitation amount of the driest quarter (mm)<br>Bio18 Precipitation amount of the warmest quarter (mm)<br>Bio19 Precipitation amount of the coldest quarter (mm)</p> <p> </p> <p><strong>Others</strong></p> <p>More information related to bioclimatic variables can be found on https://chelsa-climate.org/bioclim/</p>
Input data set for the statistical analsysis of rockfall reach probabilities
<p>These files contain reach probability values extracted from 3D rockfall simulations for field-mapped block deposits as well as a series of attributes characterising the deposits. They served for the statistical analysis of the reach probability values as a function of site, forest and rockfall characteristics. The results of the analysis are published in Dorren et al. 2022: Delimiting rockfall runout zones using reach probability values simulated with a Monte-Carlo based 3D trajectory model. Natural Hazards and Earth System Scienses.</p>
Diversity of options to eliminate fossil fuels and reach carbon-neutrality across the entire European energy system
<p><strong>Sector-coupled Euro-Calliope model outputs</strong></p> <p>The subdirectories found here cover cost-optimal and cost relaxation (SPORES) carbon-neutrality runs for a sector-coupled, sub-national resolution European energy system model.</p> <p>The underlying model to produce these results, <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-coupled Euro-Calliope</a>, is an extension of the power-sector only <a href="https://github.com/calliope-project/euro-calliope">Euro-Calliope model</a>. It incorporates all energy consuming sectors and includes a more detailed representation of transmission capacities between 98 model regions in Europe.</p> <p>The model runs here are based on specific Sector-Coupled Euro-Calliope minor releases:</p> <ul> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/74f6a9b2e157b6147e155b556f521c03ef23246a">cost-opt</a></li> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/519a4fb26920114e451b8247b38ed86b93b6af89">slack-*</a></li> </ul> <p>The models were optimised using the <a href="https://github.com/calliope-project/calliope">Calliope open energy system modelling framework</a>, again based on different minor releases:</p> <ul> <li><a href="https://github.com/calliope-project/calliope/commit/1faed85eeddbe41c29d52982a6bfb147ef9001a3">cost-opt</a></li> <li><a href="https://github.com/calliope-project/calliope/commit/19460da2e23e752995a9a02ae6dca49379565d43">slack-*</a></li> </ul> <p><code>slack-*</code> results are for cost relaxation runs, where <code>*</code> refers to the percentage relaxation from the optimal cost of the 2018 energy system. All results use the <a href="https://github.com/sentinel-energy/friendly_data">friendly data</a> format. Data files are structured according to standardised sector-coupled Euro-Calliope output processing provided by the <a href="https://github.com/brynpickering/friendly-calliope">friendly-calliope</a> package + additional processing to produce data relevant to nine high-level metrics (see script <a href="https://github.com/calliope-project/sector-coupled-euro-calliope/blob/main/src/analyse/result_to_friendly.py">here</a>).</p> <p>Both cost optimal and SPORES results related to a projected demand scenario are given in the directories ending in "demand-update".</p> <p>To explore the data, please refer to the <a href="https://sentinel-energy.github.io/friendly_data/">friendly data documentation</a>.</p>
S32 | REACH2017 | >68,600 REACH Chemicals
<p>This is the collection associated with list S32 REACH2017 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S32 | REACH2017 | <strong>>68,600 REACH Chemicals</strong></p> <p>A list of >68,600 REACH chemicals including InChIKeys and spectral information, provided by N. Alygizakis and J. Slobodnik, EI. </p> <p>Update 14 Nov 2019: added CSV. Feb 6th, 2020: edited SMILES in CSV to remove \\ (fixed_SMILES column); edited more SMILES. Nov 6, 2020: fixed entry for (2H5)benzoic acid based on feedback from PubChem. 17 July 2022: more SMILES fixes (CSV only).</p>
Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20170131_02
<p>This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> .<br> <br> <strong>General description.</strong> These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20170131_02".</p> <p><strong>Filtering. </strong>The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate.</p> <p><strong>File format.</strong> The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification.</p> <p><strong>Datasets. </strong>A few of the relevant dataset paths are listed here for convenience. In the below, <em>n</em> refers to the number of recording channels and <em>k</em> refers to the number of samples.</p> <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> <p>Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.</p> <p><strong>History</strong></p> <ul> <li>Version 2 - corrects a error with the electrode mapping.</li> <li>Version 1 - initial release.</li> </ul>
Self-attribution of distorted reaching movements in immersive virtual reality
<p>This dataset and Unity 3D code scripts are associated to the following paper : H. Debarba, R. Boulic, R. Solomon, O. Blanke, B. Herbelin (Computers & Graphics, Vol 76, November 2018, pp 142-152, <a href="https://www.sciencedirect.com/science/article/pii/S0097849318301353?utm_campaign=STMJ_75273_AUTH_SERV_PPUB&utm_medium=email&utm_dgroup=&utm_acid=810891&SIS_ID=0&dgcid=STMJ_75273_AUTH_SERV_PPUB&CMX_ID=&utm_in=DM377782&utm_source=AC_30">in Open Access</a>) : “Self-attribution of distorted reaching movements in immersive virtual reality”. <a href="https://doi.org/10.1016/j.cag.2018.09.001">https://doi.org/10.1016/j.cag.2018.09.001</a></p> <p> </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.