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Supplementary Material: Knobs and dials of retrieving JWST transmission spectra. I. The importance of p-T profile complexity
<p>This is supplementary material to <a title="Schleich et al. (2024)" href="https://www.aanda.org/articles/aa/abs/2024/10/aa51845-24/aa51845-24.html" target="_blank" rel="noopener">Schleich et al. (2024)</a>. The content of the provided data repository (also described in the file "content.txt") is as follows:</p> <p> </p> <h2>ADDITIONAL ANALYSIS</h2> <p>This folder contains a collection of ancillary data products for the evaluation of the retrievals performed in this work.</p> <ul> <li>'bayes-factor' contains the data tables for evaluating the Bayes' factor for each separate collection of models(*)</li> <li>'corner-plots' contains a collection of all corner plots associated with the individual input cases</li> <li>'fit-residuals' contains all fit residuals for the individual atmospheric retrievals performed in this work (used to make Fig. C.1)</li> <li>'resampled-pt-profiles' contains resampled p-T profiles to generate Figs. 6 and F.1</li> <li>'retrieval-accuracy' contains additional plots related to the accuracy of each retrieval (used to make Fig. 5, as well as Figs. E.1 - E.5)</li> </ul> <p><br>(*) SIDE NOTE:<br>Table headers in the "bayes-factor" data tables reference evidence reported from MultiNest (variable "Z"), and calculated Bayes factor (variable R). The case with log(R) = 0 is necessarily the reference case, and outliers are marked in a binary table with 1 (|log(R)| > 5) or 0 (|log(R)| < 5). In all cases, "log" refers to the natural logarithm.</p> <ul> <li>If someone actually reads this, I'm sorry. I also spent way too much time trying to track down if the values reported in MultiNest are natural or base-10 logarithm. I have now been convinced that it is worth it, always, to either specify "ln" for the base-e logarithm, or give the base of your logarithm if your write it down (i.e. log_10(X)) -Simon.</li> </ul> <h1> </h1> <h2>RETRIEVAL RESULTS</h2> <p>This folder contains the data products associated with the retrieval runs for each synthetic spectrum. The sub-directories are aranged by the following keys:</p> <ul> <li>'drs' and 'pandexo' refere to the two noise cases considered</li> <li>'inv-t' and 'norm-t' refere to the two underlying p-T profiles used to make the synthetic spectra</li> <li>'hpc', 'mpc', and 'lpc' refere two the three cloud-top pressure cases considerd</li> </ul> <p>Each individual folder contains (1) the TauREx parameter files for running retrievals using the selection of p-T profiles, (2) a folder called 'results', which containts the associated data products, and (3) a folder called 'chains', which stores the ancillary data products associated with the MultiNest sampling runs of each retrieval.</p> <p> We note that for the "drs_inv-t_mpc" case, the chains for the isothermal, 2-point, and 4-point runs have been lost</p> <p> </p> <h2>SYNTHETIC SPECTRA</h2> <p>This folder contains data products associated with the sample of synthetic transmission spectra.</p> <ul> <li>'pt-profile_*.csv' are csv-files containing the p-T points used to make Figure 1 , and to generate the synthetic transmissions spectra</li> <li>'forward-models' contains TauREx parameter files and forward models for the sample of synthetic transmission spectra. Each of the sub-directories also contains a faux-spectrum representing the wavelength-map of NIRSpec PRISM <ul> <li>'no-clouds' contains contains the above for generating Figure 3.</li> <li>'inv-t' contains forward models using the "inverse" p-T profile</li> <li>'norm-t' contains forwrad models using the "monotonic" p-T profile</li> </ul> </li> </ul>
Raw NanoLuc and NanoBiT Data from "Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay"
<p>This dataset includes raw NanoLuc and NanoBiT data supporting the manuscript titled 'Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay'.</p>
Regional scale shear wave velocity profiles for ground response analyses and uncertainties evaluations – the Piedmont Region (NW Italy) Database
<p>The databases provide detailed information for the Piedmont region in Northwest Italy, offering a view of its geological and geophysical characteristics:</p> <ul> <li><strong>Geological-Geomorphological Database</strong>: Includes 13 distinct Geological-Geomorphological Domains (GGD) in shapefile format. It supports spatial analysis and visualization, based on data from the Geological Map of the Piedmont Region at a 1:250,000 scale.</li> <li><strong>Geotechnical Database</strong>: Contains geotechnical data on bedrock depth and texture attributes derived from available logs in CSV format. Georeferenced using UTM coordinates (WGS84 UTM32N), it includes depth values and texture codes (C for clay, G for gravel, S for sand, R for rock, X for not available).</li> <li><strong>Geophysical Database</strong>: Provides data on shear wave velocity (Vs) profiles in CSV format. Georeferenced with UTM coordinates (WGS84 UTM32N), it includes layer interface depth and shear wave velocity above each layer.</li> </ul>
IODP Expedition 398 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 355 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Analyzed NanoBiT Data from "Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay"
<p>This dataset includes analyzed NanoBiT data supporting the manuscript titled 'Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay'.</p>
5359 industrial VEA load profiles
Open the record for dataset details and reuse information.
Customizable induction heating profiles: from tailored colloidally stable nanoparticles towards multi-stage heatable supraparticles
<p>This data publication is based on the metadata and datasets underlying the manuscript: "Inductively heatable nano- and supraparticles: from colloidally stable hot nanoparticles to supraparticles with customizable multi-stage heating profiles"</p> <p>Magnetic nanoparticles (NPs) are efficient heat mediators in induction heating. Originally explored for hyperthermia, their applications have broadened to industrial processes where temperature control is crucial. By adjusting the NP composition or morphology, magnetic characteristics such as Curie temperatures can be tailored, allowing control over maximum heating thresholds. These NPs are, however, usually designed for maximum heating rates at specific magnetic fields. In this work, the synthesis is presented for colloidally stable Co and ZnCo ferrite NPs with customizable maximum heating temperatures, and their combination within micron-scaled supraparticles (SPs). Maximum induction heating temperatures of ZnCo ferrite NPs are tuned between 150 and 220 °C, while customization of Co ferrite species yields temperatures between 200 and 350 °C. These distinct magnetic properties are exploited in the selective multi-stage heating of SPs consisting of both species. Here, ZnCo ferrite components heat up to a first temperature plateau at low alternating magnetic fields (AMF), while Co ferrite NPs reach higher temperatures at increased AMF. The precise control of induction heating thresholds through the adaptability of NPs offers a high degree of customizability which makes induction heating particularly attractive for applications requiring sequential or spatial heating, such as catalysis or debonding on demand.</p>
IODP Expedition 353 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 359 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
CTD Profiles from the HDMS Lauge Koch cruise to East Greenland fjords, August 2018
<p>Greenland fjords are currently undergoing large ecosystem changes due to unprecedented melting of the Greenland Ice Sheet (GrIS). The rapidly increasing discharge of meltwater and ice not only influences circulation patterns and stratification of the water column, but it also introduces large fluxes of allochthonous carbon and nutrients into the Greenland coastal environment, as well as transports large quantities of inorganic particles and suspended sediments which could limit light availability to primary producers. However, data is still limited for most Greenland fjord systems and the east coast of Greenland is especially understudied. During this cruise we investigated 3 different fjord systems of East Greenland in August 2018 aboard the HDMS Lauge Koch. We aimed to describe the physical, chemical and biological variability from glaciers to the shelf. This data set consists of 84 CTD profiles that were obtained with a Seabird SBE25 conductivity, temperature, depth (CTD) instrument. In addition to measuring pressure, conductivity, and temperature, the CTD recorded chlorophyll-a fluorescence, photosynthetically available radiation (PAR), dissolved oxygen, turbidity and pH. The instrument was factory calibrated before the cruise. The CTD recorded variables at 16 Hz and the raw data were processed using Seabird standard workflow to produce 0.1 m binned profiles using the downcast data only. This dataset compiles all CTD variables measured from all profiles into a single comma separated CSV file. </p> <p>We would like to thank the captain and crew of HDMS Lauge Koch for excellent collaboration. The cruise was funded by the Danish Center for Marine Research and by the EU Horizon2020 funded project INTAROS (grant no. 727890) and the Danish Cooperation for Environment in the Arctic.</p> <p> </p> <p> </p> <p> </p>
Dataset for: Owner-ascribed personality profiles distinguish domestic cats that capture and bring home wild animal prey
<p>Dataset allowing repetition of the analyses in the above paper, comprising personality scores and predation data, with details of cat characteristics. See readme.txt file.</p>
Morphological profiles extracted from Human MCF7 cells – compound-profiling experiment BBBC021.
<p>This contains morphological profiles extracted from Human MCF-7 cells from the compound-profiling experiment BBBC021 hosted at the Broad Bioimage Benchmark Collection.</p> <p>The profiles were extracted using the software SCIP.</p>
Software repository for data-driven reconstruction of doping profiles in semiconductors
<p>Datasets and code used described in paper: "Data-driven solutions of ill-posed inverse problems arising from doping reconstruction in semiconductors" [arXiv:2208.00742]</p>
3D S-wave velocity profiles with random fields
<p>This dataset contains 100,000 3D matrices representing S-wave velocity profiles with random fields. The spatial resolution corresponds to a grid of 9.6 x 9.6 x 9.6km with a spatial step of 300m (matrices are of size 33 x 33 x 33). Velocities go from 1,071 to 4,500m/s.</p> <p>Each profile contains a 1.8km-thick bottom layer at constant velocity 4500m/s. Above, the profile contains between 1 and 6 layers of random thickness. In each layer, the mean velocity is chosen uniformly in [1,785; 3,214m/s] and the coefficient of variation follows a Gaussian distribution N(0.2; 0.1). Then, a correlation length is chosen randomly among 1.5, 3, 4.5, 6 km in all three directions for each layer.</p> <p>The random fields' kernel has a von Karman correlation with a Hurst exponent of 0.1 and log-normal marginal distributions.</p>
Multidimensional pain profiling in people living with obesity and attending weight management services: a protocol for a longitudinal cohort study
<p><strong>Please note: The final dataset will not be available until data collection has been completed in the Autumn of 2024.<br> This dataset currently contains the following:</strong></p> <p>1. Details of the study, including authorship, ethical approval, funding, registration details, an abstract for the protocol of the study and details of data being collected (both in Microsoft Word and open access .txt formats)</p> <p>2. Outline of the data in the process of collection (Microsoft Excel)</p> <p>3. Ethical approval letters from relevant Research Ethics Committees (PDF)<br> <br> </p> <p> </p> <p><strong>Project Title: </strong>Multidimensional pain profiling in people living with obesity and attending weight management services: a longitudinal cohort study</p> <p> </p> <p><strong>Authors:</strong> Keith M. Smart<sup>1,2</sup>, Natasha Hinwood<sup>1</sup>, Colin G. Dunlevy<sup>3</sup>, Catherine Doody<sup>1</sup>, Catherine Blake<sup>1</sup>, Brona Fullen<sup>1</sup>, Jean O’Connell<sup>3</sup>, Carel W. Le Roux<sup>4</sup>, Clare Gilsenan<sup>5</sup>, Francis M. Finucane<sup>6,7</sup>, Gráinne O’Donoghue<sup>1</sup>.</p> <p> </p> <p><strong>Corresponding author</strong>: Natasha Hinwood</p> <p><strong>Address:</strong> UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><strong>Email:</strong> <a href="mailto:natasha.hinwood@ucdconnect.ie">natasha.hinwood@ucdconnect.ie</a></p> <p><strong>Phone:</strong> +353 1 716 6511</p> <p> </p> <p>Full name, department, institution, city, and country of all co-authors.</p> <p><sup>1</sup>UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><sup>2</sup>Physiotherapy Department, St. Vincent’s University Hospital, Dublin, Ireland</p> <p><sup>3</sup>Weight Management Service, St Columcille’s Hospital, Dublin, Ireland</p> <p><sup>4</sup>Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland</p> <p><sup>5</sup>Physiotherapy Department, Beaumont Hospital, Dublin, Ireland</p> <p><sup>6</sup> School of Medicine, College of Nursing and Health Sciences, University of Galway</p> <p><sup>7</sup>Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals</p> <p> </p> <p><strong>ORCID</strong></p> <p>1. Keith M. Smart: 0000-0002-1598-5215</p> <p>2. Natasha Hinwood: 0000-0001-9382-716X</p> <p>3. Colin G. Dunlevy:</p> <p>4. Catherine Doody:</p> <p>5. Catherine Blake: 0000-0002-0600-629X</p> <p>6. Brona Fullen: 0000-0003-4408--2063</p> <p>7. Carel W. Le Roux: 0000-0001-5521-5445</p> <p>8. Jean O’Connell: 0000-0001-7241-8025</p> <p>9. Clare Gilsenan:</p> <p>10. Francis M. Finucane: 0000-0002-5374-7090</p> <p>11. Gráinne O’Donoghue: 0000-0002-9126-2094</p> <p> </p> <p> </p> <p><strong>Project abstract (Protocol): </strong></p> <p><em>Introduction</em>:</p> <p>Pain is prevalent in people living with overweight and obesity. Obesity is associated with increased self-reported pain intensity and pain-related disability, reductions in physical functioning and poorer psychological well-being. People living with obesity tend to respond less well to pain treatments or management compared to people living without obesity. Mechanisms linking obesity and pain are complex and may variously include contributions from and interactions between physiological, behavioural, psychological, socio-cultural, biomechanical, and genetic factors. Our aim is to study the multidimensional pain profiles of people living with obesity, over time, in an attempt to better understand the relationship between obesity and pain.<br> </p> <p><em>Methods and analysis: </em></p> <p>This longitudinal observational cohort study will recruit (n=216) people living with obesity and who are newly attending three weight management services in Ireland. Participants will complete questionnaires that assess their multidimensional biopsychosocial pain experience at baseline and at 3, 6, 12 and 18-months post-recruitment. Quantitative analyses will characterise the multidimensional pain experiences and trajectories of the cohort as a whole and in defined sub-groups.<br> </p> <p><em>Ethics and dissemination: </em></p> <p>The study protocol has been approved by the Ethics and Medical Research Committee of St Vincent’s Healthcare Group, Dublin, Ireland (Reference No.: RS21-059) and the University College Dublin Human Research Ethics Committee (Reference No.: LS-E-22-41-Hinwood-Smart). Findings will be disseminated through peer-reviewed journals, conference presentations, public and patient advocacy groups, and social media.</p>
Murine brain organoids developmental profiling
<p>The dataset includes the RNA sequencing data generated for the study entitled "Murine cerebral organoids develop network of functional neurons and hippocampal brain region identity" by Ciarpella et al. Starting from mouse neuronal stem cells, we generate organoids to recapitulate the hippocampal structure as new platform for modeling diseases, for drug screening and for organ transplantation in regenerative medicine. Performing RNA sequencing in organoids at different stage of proliferation and maturation, we obtained a measure of the cellular diversity within the organoids, the degree of differentiation and the affinity to hippocampus or cortex tissue (reference samples). </p>
NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH
<p>This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009).</p> <p>The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm<sup>-1</sup> and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances.</p> <p>In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior.</p> <p>The file format is netcdf and the file naming conventions are</p> <p>NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p> </p> <p>The time stamp of all data is in UTC.</p> <p>Selected basic variables are (many more provided):</p> <p> </p> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Dimension</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>base_time</p> </td> <td> <p>Single value</p> </td> <td> <p>Seconds (since 00 UTC 1 Jan 1970)</p> </td> </tr> <tr> <td> <p>time_offset</p> </td> <td> <p>Time</p> </td> <td> <p>Second (since base_time)</p> </td> </tr> <tr> <td> <p>hour</p> </td> <td> <p>Time</p> </td> <td> <p>Hours since 00UTC this day</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>Height</p> </td> <td> <p>km AGL</p> </td> </tr> <tr> <td> <p><strong>temperature </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, temperature</p> </td> </tr> <tr> <td> <p><strong>waterVapor </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, water vapor mixing ratio</p> </td> </tr> <tr> <td> <p>theta</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, potential temperature</p> </td> </tr> <tr> <td> <p>pressure</p> </td> <td> <p>Time, Height</p> </td> <td> <p>hPa, pressure</p> </td> </tr> <tr> <td> <p>rh</p> </td> <td> <p>Time, Height</p> </td> <td> <p>%, relative humidity</p> </td> </tr> <tr> <td> <p>dewpt</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, dew point temperature</p> </td> </tr> <tr> <td> <p>thetae</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, equivalent potential temperature</p> </td> </tr> <tr> <td> <p>sigma_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, 1-sigma uncertainty temperature</p> </td> </tr> <tr> <td> <p>sigma_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, 1-sigma uncertainty water vapor</p> </td> </tr> <tr> <td> <p>cdfs_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for temperature</p> </td> </tr> <tr> <td> <p>cdfs_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for water vapor</p> </td> </tr> </tbody> </table> <p>Bold variables are the main retrieved profiles, from which the other variables are derived.</p> <p>Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model.</p> <p>Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. </p> <p><strong>References: </strong></p> <p>Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17.</p> <p>Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.</p> <p>Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.</p> <p>Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.</p>
Modelled hydrodynamic profiles and salmon louse larval densities at Norwegian salmon farms
<p>Data compiled for use by the PreventLice web app, a decision support tool intended to help Norwegian salmon farmers avoid salmon louse infestations: <a href="https://havforskningsinstituttet.shinyapps.io/preventlice">https://havforskningsinstituttet.shinyapps.io/preventlice</a></p> <p>Each file contains the relevant data for a registered salmonid farm in Norway, identified by its locality number according to the Norwegian <a href="https://sikker.fiskeridir.no/akvakulturregisteret/web/sites">Aquaculture Registry</a>. A total of 1023 localities are included in version 1.0.0.</p> <p>The data are in long rectangular format, with each row corresponding to a single depth interval on a single date. Each row provides variables for locality number ("loc"), date ("date"), depth (m, "depth"), daily mean temperature (°C, "meanTemp"), daily mean salinity (ppt, "meanSal"), daily mean current speed (ms<sup>-1</sup>, "meanCurrSpd"), daily 95th percentile current speed (ms<sup>-1</sup>, "95PercCurrSpd"), daily salmon louse infestation pressure (copepodids m<sup>-3</sup>, "meanCopDensity"), and daily mean significant wave height (m, "SignWaveHeight").</p> <p>Temperature, salinity and current speeds are taken from the NorFjords-160 model (<a href="https://doi.org/10.1016/j.ecss.2020.107028">Dalsøren et al. 2020</a>), a finer-scale update of the NorKyst-800 model (<a href="https://doi.org/10.1007/s10236-020-01378-0">Asplin et al. 2020</a>). Wave height data are taken from the MyWaveWAM800m Norwegian coastal wave forecasting system (<a href="https://thredds.met.no/thredds/fou-hi/mywavewam800.html">Norwegian Meteorological Institute</a>). Salmon louse copepodid densities are estimated by coupling louse biology and behaviour parameters with hydrodynamic predictions from NorKyst-800 (<a href="https://doi.org/10.1371/journal.pone.0201338">Myksvoll et al. 2018</a>).</p>
Transcriptomic profiles of resected pancreatic adenocarcinoma, whole-slide match
<p>RNA was extracted from the whole-slide tumor regions of 100 pancreatic adenocarcinomas, consecutively resected at the Beaujon hospital (Clichy, FRANCE). Tumors were sequenced in two batches, using 3' RNA-sequencing for FFPE compatibility.</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.