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2,516 results for “Seri”
High-resolution time series of leaf length and leaf thickness of strawberry and tomato measured in a growth chamber
<p>This dataset captures leaf thickness and leaf length of two strawberry plants and one tomato seeding. Leaf thickness measurements are only available of the strawberry plants. Additional data included light intensity data, soil water content (of one plant), relative humidity and temperature data. All sensors were read every second over a period of two weeks.</p>
Rotating globe – time series of temperature
<p>Rotating globe animation created by using data from the CMIP6 next generation archive from ETH Zurich (DOI: 10.5281/zenodo.3734128). It shows the temperature anomaly of the SSP5-8.5 scenario, relative to the mean of the years 1980-2014, using all available CMIP6 models over a 251 years time period (1850-2100).</p>
CESSDA Series on Variable Harmonization: An Overview of Concepts and Strategies
<p>Dr. Kristi Winters (GESIS) and Janek Bruker (formerly of GESIS) discuss social science variable harmonization and harmonized data documentation practices. This is video 1 in a series of 4 videos and the video provides introductory information on social science variable harmonization. Topics in this four-part educational video series include: an introduction to social science variable harmonization, ex ante and ex post harmonization strategies, examples from international social science surveys (including the European Values Survey), the use of international standard classifications, and how to use metadata to construct FAIR variable harmonization documentation. This video was created for the Consortium of European Social Science Archives (CESSDA). For more information visit <a href="http://www.cessda.edu">www.cessda.edu</a>.</p> <p>The course material comprises the presentation "Variable Harmonization in the Social Sciences" (<a href="http://10.5281/zenodo.3924955">http://doi.org/10.5281/zenodo.3924955</a>) and the full series of 4 videos:</p> <p>1. An Overview of Concepts and Strategies <a href="http://doi.org/10.5281/zenodo.3924208">http://doi.org/10.5281/zenodo.3924208 </a> (This video is also available for <a href="https://youtu.be/3GrDFWwHYu4">viewing on Youtube</a>)</p> <p>2. Process, Challenges and Quality Criteria <a href="http://doi.org/10.5281/zenodo.3924218">http://doi.org/10.5281/zenodo.3924218 </a>(This video is also available for <a href="https://youtu.be/DlqDqTn2L0c">viewing on Youtube</a>)</p> <p>3. International Standard Classification <a href="http://doi.org/10.5281/zenodo.3924227">http://doi.org/10.5281/zenodo.3924227 </a>(This video is also available for <a href="https://youtu.be/8hPt3gVn4jQ">viewing on Youtube</a>)</p> <p>4. Documentation and Best Management Practices <a href="http://doi.org/10.5281/zenodo.3924241">http://doi.org/10.5281/zenodo.3924241 </a>(This video is also available for <a href="https://youtu.be/S5rmFgOl7OA">viewing on Youtube</a>)</p>
A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015
<p>Glacier mass balance (MB) data are crucial to understand and quantify the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967-2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct MB observations and remote sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method’s validity was assessed through an extensive cross-validation against a dataset of 32 glaciers , with an estimated average error (RMSE) of 0.55 m.w.e. a<sup>-1</sup>, an explained variance (r2) of 75% and an average bias of -0.021 m.w.e. a<sup>-1</sup>. We estimate an average regional area-weighted glacier-wide MB of -0.71±0.21 (1 sigma) m.w.e. a<sup>-1</sup> for the 1967-2015 period, with negative mass balances in the 1970s (-0.44 m.w.e. a<sup>-1</sup>), moderately negative in the 1980s (-0.16 m.w.e. a<sup>-1</sup>), and an increasing negative trend from the 1990s onwards, up to -1.34 m.w.e. a<sup>-1</sup> in the 2010s. A comparison with ASTER-derived geodetic MB for the 2000-2015 period showed important differences with the photogrammetric geodetic MB used to train our model. When recalibrating our reconstructions with the new ASTER-derived geodetic MB, the estimated average regional area-weighted glacier-wide MB (1967-2015) is reduced to -0.64±0.21 (1 sigma) m.w.e. a<sup>-1</sup>. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967-2015 period are the Chablais (-0.93 m.w.e. a<sup>-1</sup>), Champsaur and Haute-Maurienne (-0.86 m.w.e. a<sup>-1</sup> both) and Ubaye ranges (-0.83 m.w.e. a<sup>-1</sup>), and the ones presenting the lowest mass losses are the Mont-Blanc (-0.69 m.w.e. a<sup>-1</sup>), Oisans and Haute-Tarentaise ranges (-0.75 m.w.e. a<sup>-1</sup> both). This dataset provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments.</p> <p>The MB dataset is presented in two different formats: (a) A single netCDF file containing the MB reconstructions, the glacier RGI and GLIMS IDs and the glacier names. This file contains all the necessary information to correctly interact with the data, including some metadata with the authorship and data units. (b) A dataset comprised of multiple CSV files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: GLIMS-ID_RGI-ID_SMB.csv. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide MB time series. Glaciers with remote sensing-derived estimates (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a<sup>-1</sup> as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;).</p>
Pre-eruption InSAR time-series at Kīlauea (Hawai`i, USA): Sentinel Ascending and Descending 2018
<p>InSAR time-series data for Kīlauea (Hawai`i, USA) derived from InSAR, between Jan 2017 and May 2018 . Data were obtained by processing Sentinel-1 ascending and descending SAR data (tracks 124 and 87, respectively). Data were processed using the JPL-developed InSAR Scientific Computing Environment (<code>ISCE</code>) open-source software package, and further time-series analysis was performed using the <code>MintPy</code> software toolbox (<a href="https://github.com/insarlab/MintPy">Miami INsar Time-series software in PYthon</a>), developed at the University of Miami. </p> <p>Three files are available in Hierarchical Data Format:</p> <ol> <li><code>geo_timeseries_tropHgt_demErr_SenAT124.h5</code>: Ascending Track timeseries file. Dates available: <code>['timeseries-20170107', 'timeseries-20170131', 'timeseries-20170212', 'timeseries-20170224', 'timeseries-20170308', 'timeseries-20170320', 'timeseries-20170401', 'timeseries-20170425', 'timeseries-20170501', 'timeseries-20170507', 'timeseries-20170519', 'timeseries-20170525', 'timeseries-20170531', 'timeseries-20170606', 'timeseries-20170612', 'timeseries-20170618', 'timeseries-20170624', 'timeseries-20170630', 'timeseries-20170706', 'timeseries-20170712', 'timeseries-20170718', 'timeseries-20170724', 'timeseries-20170730', 'timeseries-20170805', 'timeseries-20170811', 'timeseries-20170817', 'timeseries-20170823', 'timeseries-20170829', 'timeseries-20170904', 'timeseries-20170910', 'timeseries-20170916', 'timeseries-20170922', 'timeseries-20170928', 'timeseries-20171004', 'timeseries-20171010', 'timeseries-20171016', 'timeseries-20171022', 'timeseries-20171028', 'timeseries-20171103', 'timeseries-20171109', 'timeseries-20171115', 'timeseries-20171121', 'timeseries-20171127', 'timeseries-20171203', 'timeseries-20171209', 'timeseries-20171215', 'timeseries-20171221', 'timeseries-20171227', 'timeseries-20180102', 'timeseries-20180108', 'timeseries-20180114', 'timeseries-20180120', 'timeseries-20180126', 'timeseries-20180201', 'timeseries-20180207', 'timeseries-20180213', 'timeseries-20180219', 'timeseries-20180225', 'timeseries-20180303', 'timeseries-20180309', 'timeseries-20180315', 'timeseries-20180327', 'timeseries-20180408', 'timeseries-20180420', 'timeseries-20180502']</code></li> <li><code>geo_timeseries_tropHgt_demErr_SenDT87.h5</code>: Descending Track timeseries file. Dates available:<code>['timeseries-20170104', 'timeseries-20170116', 'timeseries-20170128', 'timeseries-20170209', 'timeseries-20170221', 'timeseries-20170305', 'timeseries-20170317', 'timeseries-20170329', 'timeseries-20170410', 'timeseries-20170422', 'timeseries-20170428', 'timeseries-20170504', 'timeseries-20170510', 'timeseries-20170522', 'timeseries-20170603', 'timeseries-20170615', 'timeseries-20170627', 'timeseries-20170709', 'timeseries-20170721', 'timeseries-20170814', 'timeseries-20170826', 'timeseries-20170907', 'timeseries-20170919', 'timeseries-20171001', 'timeseries-20171013', 'timeseries-20171025', 'timeseries-20171106', 'timeseries-20171118', 'timeseries-20171130', 'timeseries-20171212', 'timeseries-20171224', 'timeseries-20180105', 'timeseries-20180117', 'timeseries-20180129', 'timeseries-20180210', 'timeseries-20180222', 'timeseries-20180306', 'timeseries-20180318', 'timeseries-20180330', 'timeseries-20180411', 'timeseries-20180423', 'timeseries-20180505', 'timeseries-20180511', 'timeseries-20180517', 'timeseries-20180523', 'timeseries-20180529']</code></li> <li><code>up_20180315_20180423.h5</code>: Vertical displacement file between 20180315 and 20180423, created using <code>timeseries2velocity.py</code> (from <code>MintPy</code>).</li> </ol> <p>These data are supplemental to: Farquharson, J. I. and Amelung, F. [2020], "<em>Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano.</em>" <a href="https://doi.org/10.1038/s41586-020-2172-5">https://doi.org/10.1038/s41586-020-2172-5</a></p>
Supplementary material 1 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Supplementary file for Dryad
Figure 5 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 5 Illustration of I. probstii. A Habit B inflorescence C sepal D petal E style branch, anther F fruit (Sources: A–ED. Probst CPC24.7.03.1 (UC); F photograph by M. Schafer and J. Sacks, photographs available from author).
Figure 1 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 1 Phylogenetic tree showing resolution of I. dabashanensis and I. probstii (bold) in series Chinenses (grey box). Posterior probability/bootstrap values shown above branches.
Figure 3 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 3 Illustration of I. dabashanensis. A Habit B inflorescence C sepal D petal E style branch, anther F fruit G stolon, roots, tubers H root tuber (Source: A–ED. Probst CPC3.5.01.3 (UC); F photograph by Marty Schafer and Jan Sacks; G, H photograph by D. Probst, photographs available from author).
Figure 2 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 2 Images A–CI. dabashanensisD–FI probstii. A Habit B inflorescence C flower D habit E inflorescence F flower (Photos B–F C. author; A Mark McDonough).
A time-series of strains in response to wind measured on 19 trees in Danum Valley, Malaysia
<p>Strain data measured on the trunks of 19 trees in the Danum Valley, Malaysia. It also contains wind speed data measured using anemometers attached to emergent trees nearby. This data can be used to extract the fundamental swaying frequencies of these trees, and to quantify the relationship between wind speed and bending strain and so estimate wind damage risk. This data would be useful for anyone studying the interaction between wind and trees. The tallest tree in this data set is approximately 51 m tall. The nearby field centre is located at (4°57'53.0"N 117°48'18.3"E) and the measured trees are part of the 50ha Smithsonian plot and the 1ha intensive carbon monitoring plot 1. </p>
Time Series Charactersitics of Libra and existing Forecasting Competitions
<p>We calculate different time series characteristics for our data set (libra) and the time series competitions M1, M3, M4, NN3, NN5, NNGC1, and Tourism.</p>
Additional information for manuscript entiteld "Host-parasitoid associations in marine planktonic time series: can metabarcoding help reveal them?" (PONE-D-20-17825R1)
<p><strong>Description:</strong></p> <p>This repository contains material to reproduce metabarcoding analyses based on the q-zip pipeline (https://github.com/PyoneerO/qzip). Raw fastq files can be downloaded from https://www.ebi.ac.uk/ena/browser/view/PRJEB37135. The used reference file can be downloaded from https://github.com/pr2database/pr2database/releases/tag/4.11.1. Please select the files created for the classifier implemented in mothur.</p> <p>The dockerfile in this repository can be used to set up the environment which inludes the installation of the needed versions of the needed tools.</p> <p>Twelve different analyses had been conducted. For each analysis one zip file had been created which contains the following files:</p> <p>- q-zip_commands.sh: the shell script to launch the pipeline</p> <p>- q-zip_parameters.txt: pipeline parameter file as input of the shell script</p> <p>- q-zip_workflow.log: log file containing stdout and sdterr</p> <p>- q-zip_seq_of_coms.txt: file containing each command executed during the pipeline run (minimal set of command to reproduce the results)</p> <p>- seq_number_stats.txt: file containing the sequence numbers at each filtering step</p> <p>- OTU tables in tsv and biom format (sequences and taxonomic annotation included)</p> <p>- Meta data map (here only including the raw file names)</p> <p>- swarm sequences in fasta format</p> <p> </p> <p><strong>The following analyses had been conducted:</strong></p> <p>- otu formation at swarm distance 1; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 5; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 10; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; very strict settings settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p><strong>Settings into more detail:</strong></p> <p>relaxt settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 3 bp - threshold of average quality within of 5</li> <li>vsearch paired-end merging: length of minimum overlap of 25 bp - number of mismatches allowed of 5 bp</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 75% - percentage mismatches allowed of 20%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.6</li> </ul> <p>default settings (used for the manuscript):</p> <ul> <li>trimmomatic filtering: sliding window length of 3 bp - threshold of average quality within of 8</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 5</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 90% - percentage mismatches allowed of 10%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.25 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.8</li> </ul> <p>strict settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 1 bp - threshold of average quality within of 15</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 0</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 90% - percentage mismatches allowed of 10%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.9</li> </ul> <ul> </ul> <p>very strict settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 1 bp - threshold of average quality within of 15</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 0</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 100% - percentage mismatches allowed of 0%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.9</li> </ul>
Figure 8 from: Bulbul MZH, Chowdhury TS, Misbah MMH, Ferdous J, Dey S, Hasan I, Fujii Y, Ozeki Y, Kawsar SMA (2021) Synthesis of new series of pyrimidine nucleoside derivatives bearing the acyl moieties as potential antimicrobial agents. Pharmacia 68(1): 23-34. https://doi.org/10.3897/pharmacia.68.e56543
Figure 8 Structures of compounds 7 (octanoyl derivative), 9 (myristoyl derivative), and 14 (dichloroacetyl derivative).
Scheme 1 from: Bulbul MZH, Chowdhury TS, Misbah MMH, Ferdous J, Dey S, Hasan I, Fujii Y, Ozeki Y, Kawsar SMA (2021) Synthesis of new series of pyrimidine nucleoside derivatives bearing the acyl moieties as potential antimicrobial agents. Pharmacia 68(1): 23-34. https://doi.org/10.3897/pharmacia.68.e56543
Scheme 1 Reagents and conditions: (a) dry Py, (C6H5)3COCl, –5 °C, 6 h, (70%; Rf = 0.52); (b) dry Py, various acyl halides (3–14), 0 °C to rt, DMAP, 6 h.
Figure 5 from: Bulbul MZH, Chowdhury TS, Misbah MMH, Ferdous J, Dey S, Hasan I, Fujii Y, Ozeki Y, Kawsar SMA (2021) Synthesis of new series of pyrimidine nucleoside derivatives bearing the acyl moieties as potential antimicrobial agents. Pharmacia 68(1): 23-34. https://doi.org/10.3897/pharmacia.68.e56543
Figure 5 Percentage zone of mycelial growth inhibition for compounds 3 and 11 against A. niger (A and B, respectively). DMSO represents the negative control, whereas nystatin is the positive control.
Figure 2 from: Bulbul MZH, Chowdhury TS, Misbah MMH, Ferdous J, Dey S, Hasan I, Fujii Y, Ozeki Y, Kawsar SMA (2021) Synthesis of new series of pyrimidine nucleoside derivatives bearing the acyl moieties as potential antimicrobial agents. Pharmacia 68(1): 23-34. https://doi.org/10.3897/pharmacia.68.e56543
Figure 2 Percentage of inhibition observed for A) B. subtilis by compounds 7, 9, and 8; B) E. coli by compounds 2, 4, and 9; C) S. abony by compounds 7, 9, and 14; and D) S. aureus by compounds 7, 9, and 14. DMSO was the negative control, whereas azithromycin represented the positive control.
Figure 7 from: Bulbul MZH, Chowdhury TS, Misbah MMH, Ferdous J, Dey S, Hasan I, Fujii Y, Ozeki Y, Kawsar SMA (2021) Synthesis of new series of pyrimidine nucleoside derivatives bearing the acyl moieties as potential antimicrobial agents. Pharmacia 68(1): 23-34. https://doi.org/10.3897/pharmacia.68.e56543
Figure 7 Percentage growth inhibition observed for various concentrations of compound 9. Right figure 96-well flat-bottom experiment.
Data from: Uncovering the genetic signature of quantitative trait evolution with replicated time series data
The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.
Data from: Increasing compliance with low tidal volume ventilation in the ICU with two nudge-based interventions: evaluation through intervention time-series analyses
Objectives: Low tidal volume (TVe) ventilation improves outcomes for ventilated patients, and the majority of clinicians state they implement it. Unfortunately, most patients never receive low TVes. 'Nudges' influence decision-making with subtle cognitive mechanisms and are effective in many contexts. There have been few studies examining their impact on clinical decision-making. We investigated the impact of 2 interventions designed using principles from behavioural science on the deployment of low TVe ventilation in the intensive care unit (ICU). Setting: University Hospitals Bristol, a tertiary, mixed medical and surgical ICU with 20 beds, admitting over 1300 patients per year. Participants: Data were collected from 2144 consecutive patients receiving controlled mechanical ventilation for more than 1 hour between October 2010 and September 2014. Patients on controlled mechanical ventilation for more than 20 hours were included in the final analysis. Interventions: (1) Default ventilator settings were adjusted to comply with low TVe targets from the initiation of ventilation unless actively changed by a clinician. (2) A large dashboard was deployed displaying TVes in the format mL/kg ideal body weight (IBW) with alerts when TVes were excessive. Primary outcome measure: TVe in mL/kg IBW. Findings: TVe was significantly lower in the defaults group. In the dashboard intervention, TVe fell more quickly and by a greater amount after a TVe of 8 mL/kg IBW was breached when compared with controls. This effect improved in each subsequent year for 3 years. Conclusions: This study has demonstrated that adjustment of default ventilator settings and a dashboard with alerts for excessive TVe can significantly influence clinical decision-making. This offers a promising strategy to improve compliance with low TVe ventilation, and suggests that using insights from behavioural science has potential to improve the translation of evidence into practice.
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.