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6,467 results for “process”
Herbaceous Vegetation Survey:Effects of Long Term Fertilization and Oak Canopy Cover on Plant Communities and Ecosystem Processes
In 1996 E142 was established in field D on top of the E004 macroplots. E004 was conducted in fields A, B, C and D by Dave Tilman. The purpose of E004 was to see what effect NH4NO3 addition has on large areas over a longer period of time with exposure to naturally-occurring levels of herbivory. The nutrient addition treatments in E004, E142 plots have been applied annually since 1982. These experiments, along with others at Cedar Creek, examine the community and ecosystem consequences of chronic nutrient loading.
Species trait tissue chemistry: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements
The Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity, and functional diversity planted in 100 m2 and 400 m2 plots at 1 m spacing, appropriate for testing long-term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of twelve species was planted in 16 m2 plots at 0.5 m spacing. This data package examines the connections between aboveground and belowground processes in FAB2. This data package includes information on tree diversity and community composition, forest structure, forest understories, soil microbes, net nitrogen mineralization, and canopy nitrogen. A wide variety of data types are included, such as data from hyperspectral and LiDAR remote sensing, percent cover analysis, soil microbial analyses, and soil assays including C:N, pH, and net nitrogen mineralization. This data package is included in the submission of the manuscript entitled “Predicting aboveground and belowground processes in diverse forest ecosystems using remote sensing and in-situ measurements.”
Terrestrial-Stream Biodiversity Litter Processing Datasets from Watershed 20 within the Coweeta Hydrologic Laboratory
Although litter decomposition is a fundamental ecological process, most of our understandings comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss -- the focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. This study was conducted at the Coweeta Hydrologic Laboratory in Watershed 20 on Ball Creek that drains into Coweeta Creek, a tributary of the Little Tennessee River. Data were analyzed using a statistical approach that first looks for additive identiy effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and/ or composition. This approach addresses questions key to understanding the potential effects of species loss on ecosystem processes. If additive effects dominate, the consequences for decomposition dynamics will be predictable based on our knowledge of individual species, but not statistically predictable if non-additive effects dominate.
SMP01 Spatial variation of soil microbial processes under Cornus drummondii shrubs of varying size at Konza Prairie, 2017
Soil was collected from multiple locations under individual Cornus drummondii shrub islands of varying size to measure within-shrub heterogeneity in soil microbial processes. Soil chemistry (Total C, Total N, extractable inorganic N, extractable P, and organic matter cotent), microbial biomass C and N, and potential extracellular enzymatic activity of β-glucosidase, phosphatase, NAG-ase, and LAP-ase were measured. Potential carbon mineralization and the isotopic composition of respired soil carbon was measured over a 77-day laboratory incubation.
STREAMS Project: Emergent landscape patterns in stream ecosystem processes resulting from groundwater/surface water interactions
This Data Set is hosted by the Luquillo LTER Program (LUQ) and owned by a LUQ's investigator. Our primary objective is to understand the linkage between surface-subsurface water interactions and ecosystem processes in neotropical lowland streams over an extended time frame (>25 yrs). Proposed research will occur at La Selva Biological Reserve in Costa Rica, which is owned and operated by the Organization for Tropical Studies In tectonically active regions of Central America, it is common for solute-rich groundwater to emerge at gradient breaks within the complex volcanic topography of mountains and foothills which intergrade with the coastal plain. These groundwaters can significantly influence solute chemistry and related ecological and ecosystem-level processes in receiving surface waters. Many solute-rich groundwaters are associated with underlying volcanic activity which has altered the chemistry of receiving streams throughout Central America. Geothermally-modified groundwaters, surfacing at the gradient break between the Central Mountain range and the coastal plain at La Selva Biological Station, have high levels of P (up to 400 mg SRP L-1) and other solutes (Ca, Cl, Mg, SO4) but are not elevated in temperature. Spatial patterns in stream solute chemistry are determined by geomorphic features of the volcanic landscape that include: upland lavas drained by P-poor streams; a gradient break (~50 m.a.s.l.), at or near where P-rich springs emerge; and lowland alluvial areas drained by streams that are both P-rich and P-poor depending on whether they receive the input of solute-rich springs. Our project is the first to determine long-term effects of nutrient enrichment in a detrital-based stream within the wet tropics. We will continue to build upon our long-term(1988-present) data set on stream solute chemistry, which is the only one that we are aware of for lowland primary rainforest of Central America. The proposed project will build on 18 years of past resear
Analysis of the planning process for a new park in Minneapolis, Minnesota, at the Upper Harbor Terminal site with planners and community members focusing on redevelopment that addresses green gentrification concerns, 2019 to 2021
This dataset is from a study focuses on the Minneapolis Park and Recreation Board’s planning process for a new park at the Upper Harbor Terminal (UHT) site - a defunct barge-to-rail terminal on the Mississippi River being redeveloped with a mix of housing, commercial uses, and park space (see "UHT_location_map in Other Entities). While the redevelopment affords an opportunity to remediate pollution and provide new greenspace, a high portion of nearby residents are low-income and housing-cost burdened, raising concerns among community members about gentrification. As a counter to green gentrification, the concept of “Just Green Enough” (JGE) became a common theme around the UHT development process. JGE aims to center community-centered greening efforts with broader community development goals in mind (i.e. living-wage jobs and affordable housing). Amidst growing community pressure, the Minneapolis Park and Recreation Board (MPRB) appointed a Community Advisory Committee (CAC). The CAC was tasked with meeting monthly for in-depth deliberations (facilitated by planning staff and external consultants) with the goal of making final park design and programming recommendations, which planning staff would present to the MPRB Board of Commissioners. The Upper Harbor Terminal CAC consisted of 16 members, mostly residents of North Minneapolis and Northeast, and many with a background in nonprofit, environmental, or community organizing work. Meetings began in July 2019 and lasted nearly two years until in May 2021. This dataset includes the qualitative codebooks for the CAC meetings, accompanied by the meeting minutes and other documents related to the UHT planning process. The transcripts of semi-structured interviews with stakeholders in the UHT planning process were also analyzed and coded, but specific quotations are omitted from this dataset to protect the privacy the participants.
Software for processing data from a fast-responding RINKO EC oxygen/temperature sensor (JFE Advantech Co, Ltd)
This dataset describes how data from a fast-responding JFE Advantech RINKO EC ARO-EC-CM sensor connected to a Nortek Vector is processed to obtain accurate aquatic eddy covariance measurements. The code and documentation are stored in a .zip file. It consists of a manual, Fortran source code, a definition file and a complied executable suitable for running on Microsoft Windows. The software development was supported by NSF funding to PI Berg (OCE-1824144, OCE-2223204).
A mesial-to-lateral dissociation for orthographic processing in the visual cortex
Open the record for dataset details and reuse information.
Dataset containing the results of the selection process of DSH and CSS articles 2018-2020
<p>Dataset containing the results of the selection process of Digital Scholarship of Humanities and Computational Social Science articles. It shows which articles use data and have a clear data section. These are used to create a corpus to help build and validate and evaluate the data model of data scopes.</p>
Internet use: participating in social networks [percentage of individuals] processed Eurostat data [CEEMID indicator]
<p>The indicator '<strong>Internet use: participating in social networks (creating user profile, posting messages or other contributions to facebook, twitter, etc.) [percentage of individuals]</strong>' from the Eurostat statistical product <em>Individuals who used the internet, frequency of use and activities.</em></p> <p>- NUTS2013 regional codes are recoded to NUTS2016<br> - missing data is handled with last observation carry forward, next observation carry back, linear interpolation<br> -NUTS2 areas are imputed when only NUTS1 level data is available. <br> <br> The original dataset is available here:<br> <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en">https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en</a></p> <p>More about CEEMID: <a href="http://ceemid.eu">www.ceemid.eu</a><br> Get in touch: <a href="http://danielantal.eu/#contact">danielantal.eu/#contact</a></p>
Processed data and analysis results for 104 RBPs
<p>This repository makes available the processed data and the results of our SURF paper. </p> <p>The paper presents the <strong>S</strong>tatistical <strong>U</strong>tility for <strong>R</strong>BP <strong>F</strong>unctions (SURF) for integrative analysis of RNA-seq and CLIP-seq data. The goal of SURF is to identify alternative splicing (AS), alternative transcription initiation (ATI), and alternative polyadenylation (APA) events regulated by individual RBPs and elucidate protein-RNA interactions governing these events. We applied the SURF pipeline to analyze 104 RBP data sets (from <a href="https://www.encodeproject.org">ENCODE</a>) and performed downstream analysis. Check out the browsable results from this <a href="http://www.statlab.wisc.edu/shiny/surf/">shiny</a> app!</p> <p>The current repository includes:</p> <ul> <li>meme.326.input.zip -- input of 326 MEME runs on SURF-inferred location features</li> <li>meme.326.output.zip -- output of 326 MEME runs on SURF-inferred location features</li> <li>surf_inferred_feature.gtf -- SURF-inferred location features for 52 RBPs</li> <li>gencode.v24.annotation.filtered.gtf -- filtered genome annotation used for ENCODE data analysis</li> <li>Homo_sapiens.GRCh37.71.primary_assembly.protein_coding.gtf -- genome annotation used for simulation study</li> <li>simulation_truth.txt -- truth parameters used for RNA-seq simulation</li> <li>[RBP].results.rds -- SURF output (each an R object) for 104 RNA-binding proteins ([RBP] the protein name). </li> </ul> <p>For reproducing the results, the source code is available at DOI: <a href="https://doi.org/10.5281/zenodo.3779853">10.5281/zenodo.3779853</a>.</p>
A global dataset of SST anomaly evolving processes retrieved from remote sensing products (GDSSTAEP V1.0)
<p> The GDSSTAEP includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively. </p> <p> </p>
Comparing Perturbation Modes for Evaluating Instabilities in Neuroimaging: Processed NKI-RS Subset (08/2019)
<p>The processed subset of the NKI-RS dataset for evaluation of various perturbation modes when studying instabilities. Linked to the <a href="https://arxiv.org/abs/1908.10922">pre-print found here</a>, can be visualized using the <a href="https://github.com/gkiar/stability-mca/blob/master/code/dipy_exploratory/mca_dipy_exploratory_analysis.ipynb">plotting code here</a>, and generated with various <a href="https://github.com/gkiar/stability/tree/master/code/experiments/paper0_comparing_perturbation_modes">scripts and launch configurations found here</a>.</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
Grammatical category and the neural processing of phrases - EEG data
<p>EEG data presented in the paper</p> <p>Grammar, lexical category and the neural processing of phrases</p> <p>Amelia Burroughs, Nina Kazanina, Conor Houghton</p> <p>abstract:</p> <p><br> The interlocking roles of lexical, syntactic and semantic processing in language comprehension has been the subject of longstanding debate. Recently, the cortical response to a frequency-tagged linguistic stimulus has been shown to track the rate of phrase and sentence, as well as syllable, presentation. This could be interpreted as evidence for the hierarchical processing of speech, or as a response to the repetition of grammatical category. To examine the extent to which hierarchical structure plays a role in language processing we record EEG from human participants as they listen to isochronous streams of monosyllabic words. Comparing responses to sequences in which grammatical category is strictly alternating and chosen such that two-word phrases can be grammatically constructed - cold food loud room - or is absent - rough give ill tell - showed cortical entrainment at the two-word phrase rate was only present in the grammatical condition. Thus, grammatical category repetition alone does not yield entertainment at higher level than a word. On the other hand, cortical entrainment was reduced for the mixed-phrase condition that contained two-word phrases but no grammatical category repetition - that word send less - which is not what would be expected if the measured entrainment reflected purely abstract hierarchical syntactic units. Our results support a model in which word-level grammatical category information is required to build larger units.</p> <p>_______________________________</p> <p>all the corresponding code is at</p> <p>github.com/conorhoughton/NeuralProcessingOfPhrases</p> <p> </p>
Eddy Kinetic Energy and SST gradients global datasets and trends. Additionally, this dataset includes ocean basins and ocean processes masks.
<p>This dataset includes the post-processed data used for the paper titled "Mesoscale kinetic energy response to changing oceans". The original data was obtained from AVISO+ SSH altimetry and NOAA optimal interpolated sea surface temperature (OISST):</p> <p>AVISO+ SSH: https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/gridded-sea-level-heights-and-derived-variables.html</p> <p>NOAA-OISST: https://www.ncdc.noaa.gov/oisst</p> <p>From satellite observations of sea surface height (SSH) and sea surface temperature (SST) over the satellite record (1993 - 2019), EKE and SST gradients are derived. </p> <p>Then the fields are then temporally smoothed using a running average of 12 months. Trends and the significance of each field are finally computed with linear regression and a modified Mann–Kendall test (https://github.com/josuemtzmo/xarrayMannKendall).</p> <p>Geographical regions consist of the following ocean basins: the Southern Ocean, the Indian Ocean, the Pacific Ocean, and the Atlantic ocean. These ocean basins were expert-defined to capture ocean processes at all scales (ocean_basins_and_dynamical_masks.nc).</p> <p>Dynamical regions (Fig. 5d): the Antarctic Circumpolar Current (ACC), the boundary currents and their extensions, the tropics, the subtropical ocean gyres, and the remaining regions (ocean_basins_and_dynamical_masks.nc).</p> <p>Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/EKE_SST_trends</p>
Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.
<p>Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes.</p> <p>Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives</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.