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4,486 results for “exploration”
Topic Labels of "Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique"
<p>These are the labels generated with the method proposed in the article <em>"Dynamic Topic Modelling for Exploring the Scientific Literature on Coronavirus: An Unsupervised Labelling Technique".</em> These labels are for the 100 and 200 DTM topic models, trained both with the whole corpus and with only the COVID-19 period data </p> <p> </p> <p>For the generation of these labels you can go to the original published work or to the linked Zenodo resource.</p>
Exploring the Impact of Physiotherapy on Health Outcomes in Elderly Patients with Chronic Diseases: A Cross-Sectional Analysis
<p>In this cross-sectional analysis, we investigate the transformative impact of physiotherapy on health outcomes among elderly patients grappling with chronic diseases. Physiotherapy emerges as a pivotal intervention, offering multifaceted benefits that extend beyond mere symptom management. Through tailored exercises, mobility enhancements, and targeted pain management strategies, physiotherapy not only mitigates physical limitations but also fosters greater independence and quality of life. By examining a diverse cohort of elderly individuals diagnosed with chronic conditions such as osteoarthritis and cardiovascular diseases, this study underscores the profound role of physiotherapy in promoting functional mobility, reducing healthcare burdens, and enhancing overall well-being among this vulnerable population."</p>
Github commit data for the article "Beyond Zipf's law: Exploring the discrete generalized beta distribution in open-source repositories"
<p><span>This dataframe corresponds to the data used in the Nowak's et al. 2024 article "Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories" (see reference below).</span></p> <p><span>It consists of the distirbutions of number of commits per user across a number of GitHub repositories. <br><br>There are three columns:</span></p> <ul> <li><span>repository: the repository name</span></li> <li><span># of commits: the number of commits of a given individual</span></li> <li><span>rank: the user rank in the repository (by decreasing number of commits)<br><br></span></li> </ul> <p><strong><span>Reference:</span></strong></p> <p><span>Nowak, P., Santolini, M., Singh, C., Siudem, G., & Tupikina, L. (2024). Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories. <em>Physica A: Statistical Mechanics and Its Applications</em>, <em>649</em>, 129927. <a href="https://doi.org/10.1016/j.physa.2024.129927">https://doi.org/10.1016/j.physa.2024.129927</a></span></p>
EFSA Project on the use of NAMs to explore the immunotoxicity of PFAS (Annexes B, C, D1, E, G, I, K, M, O)
<p>In vitro raw data, RIN values and RNA concentrations, DNA quality assessment, RNAseq outputs and analysis of EFSA Project on the use of NAMs to explore the immunotoxicity of PFAS (OC/EFSA/SCER/2021/13). </p>
PWAS Hub: exploring gene-based associations of complex diseases with sex dependency - backing data
<p>The contents of the PWAS database is presented on <a title="The PWAS hub" href="https://pwas.huji.ac.il/?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il</a>. The frontend and backend were build on top of a dynamical databse system. Please consult the direct API for PWAS if you wish to query the database directly: <a title="The PWAS API" href="https://pwas.huji.ac.il/API?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il/API</a></p> <p>This is a PostgreSQL dump file that was created using <code>pg_dump</code>, the backup/restore procedure for PostgreSQL. To restore this into PostgreSQL do</p> <p>[a] create a database</p> <p><code>createdb DATABASE</code></p> <p>[b] on the terminal run</p> <p><code>pg_restore -vcC -h HOST -p PORT -d DATABASE < pwas_dump.20220628.psql</code></p> <p>The HOST and PORT are determined by your installation and DATABASE is given by you in step [a] abobe.</p> <p> </p> <p>To access the PWAS tables, look for table names that begin with <code>pwasAPI_</code></p> <p>A possible query to the database may look like this:</p> <p><code>SELECT * FROM "pwasAPI_genediseasestatpwas" WHERE uniprot_id = 'P09914' AND disease = 'C44';</code></p> <p>This query lists the data that associate uniprot id <strong>P09914</strong> (gene symbol IFIT1) and disease ICD-10 <strong>C44</strong> (Other malignant neoplasms of skin)</p>
Graphic Illustration of Molly McDonough's Talk: Exploring bat coronaviruses using the FMNH cryo collection
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Molly McDonough at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland
<p>Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland.</p> <p><a href="https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html">https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html</a></p> <p>Data set contains 3D model in dxf file, additional images, selected handheld spectra.</p> <p>Publication summary:</p> <p>We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain.</p> <p> </p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
Solar Wind properties measured with instruments on the Advanced Composition Explorer (ACE)
<p>Combined ACE/SWEPAM, ACE/Mag, and ACE/SWICS data set<br> ACE/MAG and ACE/SWEPAM data are taken from the ACE Science center (https://izw1.caltech.edu/ACE/ASC/) and binned to the 12-minute time resolution of SWICS.<br> The SWICS data is based on the PHA data and analyzed as described in Berger (2008).<br> This data set is used in the following two publications:<br> Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023, submitted), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>Contact: Verena Heidrich-Meisner, CAU Kiel heidrich@physik.uni-kiel.de</p> <p>We thank the science teams of ACE/SWEPAM, ACE/MAG as well as<br> ACE/SWICS for developing, maintaining and calibrating the instruments and for providing the respective level 2 and level 1 data products.<br> This work was supported by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) as SOHO/CELIAS 50 OC 2104.</p> <p>Data products description:<br> year: year of observation (int)<br> time: day of year in current year as float<br> yeartime: time in years as float (UTC)<br> vsw: solar wind proton speed in km/s, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> dsw: solar wind proton density in cm^{-3}, measured by ACE/SWEPAM (level 2 from ACE Science Center)and rebinned to 12 minute time resolution<br> tsw: solar wind proton temperature in K, measured by ACE/SWEPAM (level 2 from ACE Science Center) and rebinned to 12 minute time resolution<br> B: magnetic feld strength in nT, measured by ACE/MAG (level 2 from ACE Science Center)<br> colage: proton-proton collisional age computed as 6.4* 1e8 * dsw /(vsw* tsw**(3/2)) in K^{3/2} s^2 cm^3 km^{-1}<br> dO7_6: ratio of the O7+ to O6+ charge state densities, measured by ACE/SWICS, derived directly from PHA (pulse height analysis) data<br> eO7_6: estimate of the relative error of dO7_6 based on the counting statistics<br> ldO7_6: decadic logarithm of dO7_6<br> elO7_6: estimate of the relative error of the decadic logarithm dO7_6 based on the counting statistics<br> mcsFe: mean charge state of Fe, based on SWICS PHA of Fe8+, Fe9+, Fe10+, Fe11+, and Fe12+ in units of the elementary charge e. At least 10 counts distributed over Fe8+, Fe9+, Fe10+, Fe11+ and Fe12+ are required<br> emcsFe: estimate of the relative error of dO7_the mean Fe charge state in e (assumes 10% relative error for each Fe charge state)<br> cor_hole: coronal hole wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> sec_rev: sector reversal plasma wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> stream_belt: streamer belt wind category in the categorization of Xu&Borovsky (2015) The ejecta category is disregarded, see for example Heidrich-Meisner (2020). Entries are 0 or 1, 1 of the data point is assigned to this type.<br> ICME: interplanatery coronal mass ejections time periods (with a six hour safety margin before and after each ICME) from the Jian (2006,2011) and Richardson & Cane (2014, 2018) ICME lists. Entries are 0 or 1, 1 of the data point is assigned to this type.<br> totalCountsFe: number of counts in ACE/SWICS distributed over Fe8+-Fe12+<br> The data set is restricted to data points where valid data points are available for all listed data products. Only for the mean charge state of Fe invalid data points are indicated with nan (not a number)</p> <p>References:<br> Berger, L. 2008, PhD thesis, Kiel, Christian-Albrechts-Universität, Diss., 2008<br> Gloeckler, G., Cain, J., Ipavich, F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 497–539<br> McComas, D., Bame, S., Barker, P., et al. 1998b, in The Advanced Composition Explorer Mission (Springer), 563–612<br> Smith, C. W., L’Heureux, J., Ness, N. F., et al. 1998, in The Advanced Composition Explorer Mission (Springer), 613–632</p> <p>Xu, F. & Borovsky, J. E. 2015, Journal of Geophysical Research: Space Physics, 120, 70<br> Heidrich-Meisner, V., Berger, L., & Wimmer-Schweingruber, R. F. 2020, Astronomy & Astrophysics, 636, A103<br> Jian, L., Russell, C., & Luhmann, J. 2011, Solar Physics, 274, 321<br> Jian, L., Russell, C., Luhmann, J., & Skoug, R. 2006, Solar Physics, 239, 393<br> Richardson, I. G. 2004, Space Science Reviews, 111, 267<br> Richardson, I. G. 2018, Living reviews in solar physics, 15, 1</p> <p>Teichmann, S. Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. (2023), "Influence of solar wind parameters on unsupervised solar wind classification with k-means" source code available: 10.5281/zenodo.7695074<br> Hecht, M, Heidrich-Meisner, V, Berger, L, Wimmer-Schweingruber, R.F. 2023 (in preparation) "Scope and limitations of ad-hoc neural network reconstructions of solar wind parameters", source code available: 10.5281/zenodo.7681047.</p> <p>year/1 time/day of year yeartime/UTC vsw/km/s dsw/cm^{-3} tsw/K B/nT colage/(K^{3/2} s^2 cm^3 km^{-1}) dO7_6/1 eO7_6/1 ldO7_6/1 elO7_6/1 mcsFe/e emcsFe/e cor_hole/bool sec_rev/bool stream_belt/bool ICME/bool totalCountsFe/1</p>
Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"
<p>Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"</p> <p>This package contains an anonymized packets of 802.11 probe requests captured in in December 2021 at Universitat Jaume I . The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
Climate Solutions Explorer - downscaled country-level IAM scenarios
<p><strong>This is a pre-release dataset and is subject to change.</strong></p> <p>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <strong><a href="https://www.climate-solutions-explorer.eu">www.climate-solutions-explorer.eu</a></strong></p> <p>The Mitigation (and Summary) Dashboards present mitigation information, i.e. emissions, energy and carbon sequestration, for over 200 countries and 10 regions. To present data for all countries, Integrated Assessment Model runs from the MESSAGEix-GLOBIOM model have been downscaled by using a methodology described in Sferra et al. 2021 <a href="#_ftn1">[1]</a>. The algorithm produces a range of pathways consistent with the underlying IAM-results, based on criteria such as historical data, planned capacities, country-available resource in the form of supply cost-curves, quality of governance as well as regional benchmarks based on IAM results. The data is provided from 2020 to 2070, for a limited set of variables used on the website.</p> <p>The scenarios included are:</p> <ul> <li><strong>Current Policies:</strong> Current Policies scenarios here are based on the implementation of national mitigation targets implemented by country without any further strengthening of action. Expected to lead to 2.7 °C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_CurPol_T45 scenario.</li> <li><strong>NDCs Delayed Action to 2030:</strong> Assumes trajectory based on the implemented NDCs until 2030, and then reduces emissions typically in line with a globally 2°C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_NDC2030_T45 scenario.</li> <li><strong>Glasgow Pledges:</strong> "Glasgow Pledges" scenarios here are based on the pledges made by countries at the 2022 COP26 Glasgow Summit, and represent increased ambition, likely taking the world closer to below 2°C in 2100, but still some distance away from the aspirations of 1.5°C of the Paris Agreement. The data is from the MESSAGEix-GLOBIOM_1.1 GP_Glasgow scenario.</li> <li><strong>Glasgow Pledges+:</strong> "Glasgow Pledges+" scenarios drops the NDC pledges and expands mid-century strategy pledges to net-zero for all countries and regions. The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowP scenario.</li> <li><strong>Glasgow Pledges++:</strong> "Glasgow Pledges++" scenarios here aims at filling the gap between national mid-century strategies and the 1.5/2 °C global scenarios. This scenario builds upon the Glasgow+ scenario and anticipates the action (net-zero target year defined for each region) in 5 or 10 years (depending on the model’s time steps). The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowPP scenario.</li> </ul> <p><a href="#_ftnref1">[1]</a> Sferra, F. et al. 2021. Downscaling IAMs results to the country level – a new algorithm. IIASA Report. IIASA, Laxenburg, Austria. <a href="https://pure.iiasa.ac.at/17501">https://pure.iiasa.ac.at/17501</a>.</p> <p> </p> <p> </p> <p><strong>Release notes (v0.2)</strong></p> <p>This version brings improvements in:</p> <ul> <li>harmonization data source, now done for 2018 using PRIMAP</li> <li>calculation of Kyoto Gases for R10, and Kyoto Gases (incl. indirect AFOLU) for countries</li> <li>addition of R10 and EU27 region data</li> <li>Corrections to variable aggregation</li> </ul> <p> </p>
Dataset exploring the use of quasi-harmonic approximation to understand the thermal properties of Bi2Se3
<p>This data set contains input and output files for DFT calculations on Bi<sub>2</sub>Se<sub>3</sub> for a number of different fixed unit cells with calculations performed using VASP and Phonopy. At each fixed volume, optimisations and phonon calculations have been performed. These have been used to understand the thermal properties of the material using the quasi-harmonic approximation. </p>
CurveCurator: A recalibrated F-statistic to assess, classify, and explore significance of dose-response curves - Example Datasets
<p>CurveCurator is an open-source analysis platform for any dose-dependent data. It fits a classical 4-parameter equation to estimate effect potency, effect size, and the statistical significance of the observed response. 2D-thresholding efficiently reduces false positives in high-throughput experiments and separates relevant from irrelevant or insignificant hits in an automated and unbiased manner. An interactive dashboard allows users to quickly explore data locally.</p> <p><br> Here, we store example dose-dependent data, parameter files, and the corresponding CurveCurator pipeline outputs (v.0.2.0). Example data sets include Kinobeads Drug-binding data (1), CTRP Viability data sets (2), and deryptM Proteomics data sets (3). The F-value matrices for developing the CurveCurator tools are deposited as well.</p> <p>Original data sources:</p> <p>(1)<a href="https://doi.org:10.1126/science.aan4368"> https://doi.org:10.1126/science.aan4368</a></p> <p>(2) <a href="https://doi.org:10.1158/2159-8290.CD-15-0235">https://doi.org:10.1158/2159-8290.CD-15-0235</a></p> <p>(3) <a href="https://doi.org:10.1126/science.ade3925">https://doi.org:10.1126/science.ade3925</a></p> <p> </p>
Data to explore circular manureshed management in beef supply chains of the United States and western Canada
Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.
Ground temperature profiles from DVDP borehole 11 at Explorers Cove, McMurdo Dry Valleys, Antarctica (2020-2025, ongoing)
The Dry Valley Drilling Project (DVDP) drilled multiple boreholes throughout Antarctica’s McMurdo Dry Valleys in the early 1970s, several of which remain open and accessible. DVDP borehole 11, with a total depth of 327.86 m, is located adjacent to the Explorers Cove Meteorological Station (EXEM), operated by the McMurdo Dry Valleys Long Term Ecological Research program (MCM LTER). In January 2020, the MCM LTER instrumented this borehole with a string of thermistors to monitor ground temperatures through the permafrost. Sensors were installed at depths of 1, 2, 3, 4, 5, 10, 20, and 30 m, providing ongoing measurements of subsurface thermal conditions at Explorers Cove.
Data to accompany "Exploring the Complexity of Ocean Acidification: An Ecosystem Comparison of Coastal pH Variability"
The goal of this project was to create a science lesson at the middle school level with data illustrating the variablilty of pH and temperature in nature. The lesson allows students to interpret pH data and gain knowledge of abiotic and biotic processes that contribute to pH differences between tropical, temperate and polar marine ecosystems. Students use what they have learned to interpret data from a 'mystery' site and develop a hypothesis as to which ecosystem the unknown data was collected from. The full cirriculum is described in Kapsenberg, L, AL Kelley, LA Francis, and SB Raskin (2015) Exploring the complexity of ocean acidification: an ecosystem comparison of coastal pH variability. Science Scope 39(3): 51-60. doi: 10.2505/4/ss15_039_03_51 This dataset contains an Excel workbook with five worksheets: A "Readme" tab with citations for additional reading and data attribution.Three time-series of pH and temperature from coastal locations: a temperate kelp forest in the Santa Barbara Channel (Spring season, 2 months, 20 min interval). a coral reef near the island of Moorea, Tahiti (Summer season, 3 weeks, 30 min interval), and the polar ocean of Cape Evans, McMurdo Bay, Antarctica (Spring/Summer, 6 months, bi-hourly interval). A fourth worksheet contains data for a "mystery site" for student examination. Data for the study were contributed by the Santa Barbara Coastal LTER, Moorea Coral Reef LTER and the G. Hofmann lab (University of California, Santa Barbara). Additional pH data are available from both LTER sites. Antarctic data in this dataset are also available from NSF's Biological and Chemical Oceanographic Data Management Office (see Cape Evans Mooring, 2012).
figure data for "Radiation environment and doses on Mars at Oxia Planum and Mawrth Vallis: support for exploration at sites with biosignature preservation potential", by F. Da Pieve, G. Gronoff, J. Guo et al (2020)
<p>The data are the tabular format of the plots in figures 2-7 of the paper submitted.</p> <p> </p>
Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins
<p>This data repository contains all previously unpublished raw data files for the manuscript “Exploring Data Evaluation Strategies for Enhanced Identification of Host Cell Proteins in Drug Products of Therapeutic Antibodies and Fc-Fusion Proteins” by Wolfgang Esser-Skala, Marius Segl, Therese Wohlschlager, Veronika Reisinger, Johann Holzmann, and Christian G. Huber. See <em>readme.md</em> for further information.</p>
Understanding ENSO dynamics through the exploration of past climates
<p>The palaeoclimate record shows that significant changes in ENSO characteristics took place during the Holocene. Exploring these changes, using both data and models, provides a means of understanding ENSO dynamics. Previous modelling studies have suggested a mechanism whereby changes in the Earth’s orbital geometry explain the strengthening of ENSO over the Holocene. Decreasing summer insolation over the Asian landmass resulted in a weakening of the Asian monsoon system. This led to a weakening of the easterly trade winds in the western Pacific, creating conditions more favourable for El Niño development. To explore this hypothesised forcing mechanism, we use a climate system model to conduct a suite of simulations of the climate of the past 8,000 years. In the early Holocene, we find that the Asian summer monsoon system is intensified, resulting in an amplification of the easterly trade winds in the western Pacific. The stronger trade winds represent a barrier to the eastward propagation of westerly wind bursts, therefore inhibiting the onset of El Niño events. The fundamental behaviour of ENSO remains unchanged, with the major change over the Holocene being the influence of the background state of the Pacific on the susceptibility of the ocean to the initiation of El Niño events.</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.