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Qualité sanitaire et énergétique des rénovations
<p><em><strong>Objectifs de l’étude</strong></em></p> <p>La réalisation de bâtiments très performants et le soutien à la massification de la rénovation énergétique constituent des priorités pour lutter contre le changement climatique et la précarité énergétique. Ceci suppose l’engagement de l’ensemble des acteurs de la filière, ainsi que d’apporter sécurité et confiance sur les performances énergétiques réellement atteintes et sur la capacité des professionnels à réaliser des bâtiments sains, confortables et durables. Les retours d’expérience abordant de manière simultanée les questions de la qualité sanitaire, du confort d’ambiance et des consommations énergétiques sont rares. Pourtant l’atteinte de niveaux ambitieux de performance énergétique peut entrer en contradiction avec la qualité de l’environnement intérieur en altérant le confort de l’occupant ou la qualité d’air intérieur.</p> <p>Le projet Qualité Sanitaire et Energétique des rénovations (QSE) du programme PROFEEL a pour but d’apporter un éclairage sur l’impact des travaux de rénovation énergétique sur les performances globales des rénovations. Basé sur le suivi expérimental de 29 bâtiments à usage d’habitation faisant l’objet d’une rénovation ou rénovés depuis 1 à 3 ans, l’étude a permis d’évaluer leur performance globale à l’aide de protocoles utilisant des méthodes de référence.</p> <p> </p> <p><em><strong>Description du jeu de données</strong></em></p> <p>Le jeu de données mis à disposition porte sur 37 logements et fournit des informations concernant les caractéristiques des bâtiments et de leurs systèmes, les travaux de rénovation réalisés, les évènements entrainant une apparition de moisissures, la détection d’une activité fongique active (Moularat <em>et al</em>, 2008) et les mesures de concentration des polluants. Ces mesures ont été effectuées avant et après les travaux de rénovation énergétiques pour une partie du jeu de données (Panel 1) ou ont fait l’objet de mesures uniquement après les travaux de rénovation énergétique (Panel 2). Sur les 37 logements, 28 font partie du panel 1 et 9 du panel 2.</p> <p>Les polluants mesurés sont :</p> <ul> <li>Des composés organiques volatils : 1-methoxy-2-propanol, 1,2,4-triméthylbenzène, 2-butoxyéthanol, 2-éthylhexanol (ou 2-éthyl-1-hexanol), alpha-pinène, benzène, éthylbenzène, hexane, limonène, m-xylènes, o-xylène, p-xylènes, beta-pinène, styrène, toluène ;</li> <li>Des aldéhydes : acétaldéhyde, formaldéhyde, hexaldéhyde ;</li> <li>NO<sub>2</sub> ;</li> <li>PM<sub>2,5</sub> ;</li> <li>Radon.</li> </ul> <p> </p> <p><em><strong>Description des fichiers mis à disposition</strong></em></p> <p>Quatre fichiers sont téléchargeables. Ils permettent de contextualiser le jeu de données, de favoriser sa compréhension et faciliter sa prise en main :</p> <ul> <li>DonneesQSE_V1.csv : ce fichier contient le jeu de données. La modalité ‘NA’ pour ‘Not available’ désigne une donnée vide ;</li> <li>Dico_DonneesQSE_V0 : ce fichier contient le thesaurus du jeu de données ;</li> <li>Metadata_FR_21032022_QSE_V1.xlsx : ce fichier contient l’ensemble des méta données permettant de décrire le jeu de données ;</li> <li>QSE_MAD_donnees_fr_V1.doc : ce fichier reprend le descriptif de l’étude en français.</li> </ul> <p> </p> <p><em><strong>Remerciements</strong></em></p> <p>L’équipe projet adresse ses remerciements à toutes les personnes ayant pris part à l’étude : </p> <ul> <li>A la société PEOPLEVOX en charge de l’élaboration de la plateforme de collecte de données en ligne ; </li> <li>Aux partenaires en charge du recrutement des bâtiments et/ou de la réalisation des enquêtes : ATMO Grand-Est, AUE Corse, CSTB Champs sur Marne, ISPIRA, MEDIECO, Nobatek/INEF 4, QUALITAIR Corse, TIPEE et GREEN SOLUCE ; </li> <li>Aux laboratoires d’analyse des prélèvements : Laboratoire POLLEM du CSTB Grenoble en charge des analyses des prélèvements d’aldéhydes et de COV, Laboratoire Central de la Préfecture de Police (LCPP) en charge de l’analyse des prélèvements de particules, au Service Parisien de Santé Environnementale (SPSE) de la ville de Paris en charge de l’analyse des prélèvements de NO<sub>2</sub> et le laboratoire DOSIRAD pour l’analyse du radon ;</li> <li>Aux participants de l’étude.</li> </ul> <p>Cette étude est financée par le programme Profeel à travers l’appel à programmes 2018 d dispositif des certificats d’économie d’énergie (CEE), <a href="https://programmeprofeel.fr/">PROFEEL : des solutions innovantes pour la rénovation énergétique des bâtiments (programmeprofeel.fr)</a>.</p> <p> </p> <p><em><strong>Référence</strong></em></p> <p>S. Moularat, E. Robine, O. Ramalho M. A. Oturan, 2008. Detection of fungal development in closed spaces through the determination of specific chemical targets. Chemosphere 72(2), p. 224-232.</p>
Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.
<p>This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper, "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.<br> as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a.</p> <p>The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. <em>Nat Commun</em> <strong>11</strong>, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4.</p> <p>The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains.</p> <p>The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").</p> <p> </p>
Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707
<p>Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat <em>Carollia perspicillata</em>: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707</p> <p>File descriptions</p> <p>ArmTA.txt - Temperature and surface areas for arms of <em>C. perspicillata</em> after flight experiment<br> BodyTA.txt - Temperature and surface areas for body of <em>C. perspicillata</em> after flight experiment<br> HeadTA.txt - Temperature and surface areas for head of <em>C. perspicillata</em> after flight experiment<br> WingTA.txt - Temperature and surface areas for wings (patagium) of <em>C. perspicillata</em> after flight experiment<br> WingMorph.txt - Morphological variables measured in the body and wings of <em>C. perspicillata</em><br> HeatLoss.R - Function to estimate heat loss (Qt)<br> PowFlight.R - Function to estimate minimum power required to fly<br> Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed<br> SupportingInformationFile.docx - R notebook with set of analyses performed, word format<br> SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format<br> SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown)</p> <p>For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook (<br> SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.</p>
Karamaneas et al., 2022_PSET_DATASET
<p>This dataset contains the data used and calculations performed for conducting the analysis, including the answers provided by regional stakeholders for the assessment of SDGs<em> </em>for the book chapter Karamaneas et al., 2022 published in Palgrave Handbook of Zero Carbon Energy Systems and Energy Transitions. Palgrave Studies in Energy Transitions.</p>
Song et al., 2022_iScience_DATASET
<p>This dataset contains the underlying data (energy sector data for India) for the book chapter Song et al., 2022 published in Science in May 2022.</p>
Koasidis et al.,2022_MCDM_SD_DATASET
<p>This dataset contains the underlying data (Stakeholder votes) for the book chapter Koasidis et al., 2022 published in Multiple Criteria Decision Making in January 2022.</p>
Koasidis et al., 2022_ESD_DATASET
<p>This dataset contains the underlying data (Stakeholder votes) for the journal article by Koasidis et al., 2022 published in Energy for Sustainable Development in May 2022.</p>
Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change
<p>Environmental and biodiversity data associated with the article: <br><strong>Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. <em>Oikos. </em></strong>https://doi.org/10.1111/oik.10697</p> <p>Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables.</p> <p>Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file.</p> <ul> <li><strong>Danish Common Bird Monitoring programme</strong> – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. <em>Dansk Ornitologisk Forening</em>.</li> <li><strong>E-OBS European gridded climate data</strong> – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. <em>Journal of Geophysical Research Atmospheres</em>, <em>113</em>(20). https://doi.org/10.1029/2008JD010201</li> <li><strong>AVONET bird traits data</strong> – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. <em>Ecology Letters</em>, <em>25</em>(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul> <p> </p>
Raman spectra dataset of hydrous glasses of Le Losq et al., 2012, Am. Min 97:779-790
<p>This dataset contains Raman spectra of hydrous glasses used in the publication of Le Losq et al. (2012) to implement a chemical-independent method to quantify water content of glasses with Raman spectroscopy.</p> <p>Spectra are unprocessed. They were acquired with a T64000 Jobin-Yvon triple grating Raman spectrometer equipped with a confocal system, a 1024 CCD detector cooled by liquid nitrogen and an Olympus microscope. The optimal spatial resolution allowed by the confocal system is 1–2 μm<sup>2</sup> with a 100× Olympus objective. The spectral resolution of the spectrometer is 0.7 cm<sup>–1</sup>. A Coherent laser 70-C5 Ar+, having a wavelength of 514.532 nm, is used for the excitation line.</p> <p>The file dataliste.csv contains a list of the spectra together with the sample name and water contents in wt%. See Tables 1 and 2 in Le Losq et al. (2012) for corresponding sample chemical composition and errors on water concentrations, as well as supplementary information for a table containing the regions of interest for background fitting.</p> <p>Reference</p> <p>Le Losq, C., Neuville, D.R., Moretti, R., Roux, J., 2012. Determination of water content in silicate glasses using Raman spectrometry: Implications for the study of explosive volcanism. American Mineralogist 97, 779–790. <a href="https://doi.org/10.2138/am.2012.3831">https://doi.org/10.2138/am.2012.3831</a></p>
Data and script for Van Berkel et al: Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?
<p>Supporting materials for:</p> <p><strong>Can starlings use a reliable cue of future food deprivation to adaptively modify foraging and fat reserves?</strong></p> <p>Menno van Berkel<sup>a</sup>, Melissa Bateson<sup>a</sup>, Daniel Nettle<sup>a</sup> and Jonathon Dunn<sup>a</sup>*</p> <p><sup>a</sup>Centre for Behaviour and Evolution & Institute of Neuroscience, Newcastle University, Newcastle, UK</p> <p>*Author for correspondence (email: jonathon.dunn@newcastle.ac.uk; telephone: (+44)7730015855; postal address: Institute of Neuroscience, Henry Wellcome Building, The Medical School, Framlington Place, Newcastle University, Newcastle upon Tyne, UK, NE2 4HH).</p> <p>R script and 3 .csv files.</p>
Data from: Padfield et al. (2016) Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters.
<p>This repository provides the data from the TPC and logistic growth curves from the paper:</p> <p>Padfield, D., Yvon‐Durocher, G., Buckling, A., Jennings, S., & Yvon‐Durocher, G. (2016). Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters, 19(2), 133-142.</p> <p>metadata.pdf gives a more detailed explanation of the data.</p>
Matejcic et al. 2018 data release
<p>Dataset and scripts from study Matejčić et al. 2018. PLoS Biology, on isotropic growth of the embryonic zebrafish retinal tissue.</p>
R script and data files for Oakley et al (2017) Journal of Proteome Research. DOI: 10.1021/acs.jproteome.6b00797
<p>This R script and data replicates the analysis of Oakley et al (2017) Thermal shock induces host proteostasis disruption and endoplasmic reticulum stress in the model symbiotic Cnidarian <em>Aiptasia</em>. <em>Journal of Proteome Research</em>. 16:2121-2134. DOI: 10.1021/acs.jproteome.6b00797. </p>
cldf-datasets/szetosinitic: Chinese Structure Dataset from Szeto et al.'s (2018) paper in CLDF-Format
<p>This is a structural dataset originally published along with a paper by Szeto et al. (2018) on Chinese dialect classification:</p> <blockquote> <p>Szeto, P. Y.; Ansaldo, U. & Matthews, S.Typological variation across Mandarin dialects: An areal perspective with a quantitative approach Linguistic Typology, 2018, 22, 233-275.</p> </blockquote>
Processed features in support of Liebeskind et al (2018)
<p>Processed feature matrices used in Liebeskind et al. (2018). Supporting code: https://github.com/marcottelab/plum</p> <p>Datasets 1 - 4 correspond to those used in Figure 4:</p> <p>Dataset 1: No AP-MS, yeast CF-MS, training species: Human</p> <p>Dataset 2: AP-MS, yeast CF-MS, training species: Human</p> <p>Dataset 3: AP-MS, yeast CF-MS, training species: Human, Yeast</p> <p>Dataset 4: AP-MS, no yeast CF-MS, training species: Human, Yeast</p> <p>".train_labeled.missing_annotated.csv" files are those used for training the model and include only orthogroups for which interactions are known in the training species. These known interactions come either from gold-standard test sets, such as CORUM or EMBL's training portal, or from the fact that at least of the orthogroup pairs is missing in the focal taxon.</p> <p>".missing_annotated.csv" files were used for prediction, and include the entire feature matrices, plus known missing pairs. Note that there is no dataset 3 file. This is because data sets 2 and 3 differ only in the training species used, so dataset 3 predictions used dataset2_07302018.missing_annotated.csv as a feature matrix.</p> <p>dataset4_prediction_07302018.csv contains the predictions for all pairs on data set 4, the best performing data set that was used for all downstream analyses.</p>
S4 | UJIBADE | University of Jaume I Bade et al List
<p>This is the collection associated with list S4 UJIBADE on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S4</p> <p>UJIBADE</p> <p><strong>University of Jaume I</strong></p> <p>Bade <em>et al </em><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/Bade_etal_544Compounds_wInChIs_DTXSIDs.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/Bade_etal_544Compounds_wInChIs_DTXSIDs.xlsx">XLSX</a> (3/10/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/ujibade">UJIBADE List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Bade_etal_2015_544Compounds_InChIKeys.txt">Bade <em>et al</em> InChIKeys</a> (28/11/2015)</p> <p>Bade <em>et al</em> 2015, Sci. Tot. Environ. 538: 934-941. <br> DOI: <a href="http://dx.doi.org/10.1016/j.scitotenv.2015.08.078">10.1016/j.scitotenv.2015.08.078</a> </p>
Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)
<p>This data set contains intracranial EEG data, analysis code and results associated with the manuscript, "<strong>Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans</strong>". [DOI: 10.1126/science.aax1030]</p> <p>Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: “Norman_et_al_2019_data_and_code_zenodo.zip".</p> <p>The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.</p> <p>Matlab's Signal Processing Toolbox is required.</p> <p><strong>General notes:</strong></p> <p>1) The data does not contain identifying details about the patients, nor voice recordings.</p> <p>2) Before running the analyses, make sure you set the correct paths in the "startup_script.m" located in the main folder where the zip file was extracted.</p> <p>3) To run the code, the following open-source toolboxes are required:</p> <ul> <li><strong>EEGLAB</strong> (<a href="https://sccn.ucsd.edu/eeglab/download.php">https://sccn.ucsd.edu/eeglab/download.php</a>), version: "eeglab14_1_2b". <ul> <li>A. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. <em>J. Neurosci. Methods</em>. <strong>134</strong>, 9–21 (2004).</li> </ul> </li> <li><strong>Mass Univariate ERP Toolbox</strong> (<a href="https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox">https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox</a>), version: "dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4". <ul> <li>D. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. <em>Psychophysiology</em>. <strong>48</strong>, 1711–1725 (2011).</li> </ul> </li> </ul> <p>*** Make sure you download the relevant toolboxes and save them in the "path_to_toolboxes" before running the analysis scripts (see "startup_script.m")</p> <p>4) Code developed by other authors (redistributed here as part of the analysis code):</p> <ul> <li>DRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b. <ul> <li>L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. <strong>Dimensionality Reduction: A Comparative Review</strong>. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.</li> </ul> </li> <li>Scott Lowe / superbar (<a href="https://github.com/scottclowe/superbar">https://github.com/scottclowe/superbar</a>), version: 1.5.0.</li> <li>Oliver J. Woodford, Yair M. Altman / export_fig (<a href="https://github.com/altmany/export_fig">https://github.com/altmany/export_fig</a>).</li> </ul>
RNA-seq Dataset from Crowley et. al. 2015
<p>This dataset was uploaded to this repository by Joshua P. Zitovsky and Michael I. Love with permission from the original authors, due to the fact that the original dataset is not currently hosted in a stable repository. If this dataset is used in original research leading to published work, please cite the Crowley et. al. 2015 paper.</p> <p>This repository contains an RNA-seq dataset based on the allelic expression study by Crowley et. al. (2015). The study took mice from three divergent inbred strains (CAST/EiJ, PWK/PhJ and WSB/EiJ) and performed a diallel cross. The data set contains allele-specific expression (ASE) counts for 72 mice and 23,297 genes in the resulting cross, with 12 mice of each possible parent combination, and an equal number of males and females within each parent combination. Sequencing was performed with the Illumina HiSeq 2000 platform to generate 100-bp paired-end reads and following the TruSeq RNA Sample Preparation v2 protocol.</p>
ERT Datasets for the paper of Nickschick et al. (2019) in Solid Earth
<p>Underlying data for the paper</p> <p>Nickschick, T., Flechsig, C., Mrlina, J., Oppermann, F., Löbig, F. & Günther, T. (2019): Large-scale electrical resistivity tomography in the Cheb Basin (Eger Rift) at an ICDP monitoring drill site to image fluid-related structures. Solid Earth. https://doi.org/10.5194/se-2019-38.</p> <p>The paper contains two types of data:</p> <p>DC resistivity (geoelectrics) ERT data in four profiles<br> -------------------------------------------------------<br> P1, P2 and P3 represent classical multi-electrode ERT data with a unit electrode spacing of a=5m using the Wenner array. They are a representative selection of in total 8 profiles measured in the frame of the (German) MSc work of F. Loebig (there called P2, P5 and P7) where also the lithological section (Fig. 2) was developed:<br> 1. P1 between Lesinka and Hnevin, 620m long<br> 2. P2 around the Hartousov mofette, 700m long<br> 3. P3 between Hartousov and Kacerov, 700m long</p> <p>The inversion results are shown in Fig. 6a,b,c.</p> <p>The large-scale dataset represents data from a dipole-dipole experiment that is in detail described in the paper (Figs. 3-5) with the inversion result given in Fig. 7a along with borehole data.<br> For location see Fig. 1.</p> <p>For all four profiles we provide<br> - measured data with electrode positions on top and the electrode array (abmn) along with the resistance below, topography at the bottom<br> - the configuration file for the inversion software BERT (see https://gitlab.com/resistivity-net/bert), we used version 2.2.9 from January 2019<br> - kml/gpx files denoting the positions of the electrode chains (P1-P3) or an Excel file containing the positions in UTM33N</p> <p>Note that for P2 and the large-scale profile the topography needs to be taken into account whereas it is not necessary <br> Users should be able to reproduce the results by calling<br> bert cfgfile all show</p> <p>Gravity data<br> ------------<br> The data represent a two-column file:<br> 1. position along the ERT profile (projected) in metres<br> 2. Bouguer anomaly in mGal</p> <p>See also special README file in the gravity folder.</p>
Raw data for manuscript A. Dey et. al., ACS Nano 2023, 17, 16, 16080–16088.
<p><strong>Scan_00165.zip</strong>: This compressed file contains the raw X-ray data collected at beamline P06 at PETRA III, DESY, relevant for the manuscript. From this dataset that includes Bragg diffraction and X-ray fluorescence data from the indium Kα, gallium Kα, and arsenic Kα lines, all figures containing X-ray data in the manuscript were created. For viewing, use, e.g., <a href="https://ncnr.nist.gov/ncnrdata/view/nexus-hdf-viewer.html">https://ncnr.nist.gov/ncnrdata/view/nexus-hdf-viewer.html</a>.</p> <p><strong>W795_29a.tif</strong>: This data file is the raw SEM image from which the high magnification cut-out in the manuscript figure was taken. The SEM image was collected at an acceleration voltage of 15 kV using a trough-lens detector in secondary electron imaging mode. The field of view is 2.12 µm. For viewing, use any standard image viewer.</p> <p><strong>W795_InGaAsQDs.0_00023.spm</strong>: This data file is the raw AFM image from which after processing the AFM figures and AFM height information given in the manuscript were obtained. The image size is 200 nm × 200 nm, and was obtained at a scanning speed of 1 Hz with a resolution of 512 × 512 pixels. For viewing, install, e.g., <a href="http://gwyddion.net/download.php">http://gwyddion.net/download.php</a>.</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.