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306 results for “data archive”
Supporting information for IUCr survey on raw data archival and reuse in chemical crystallography
<p>Supporting information i.e. questions, responses and raw data, relating to a study into raw data management and availability in small molecule crystallography conducted under the auspices of the International Union of Crystallography Committee on Data.</p> <p>It is now common to deposit structure factors when publishing, which means that the small molecule crystallography community caters very well for routine structures. However this is generally only the case if everything in a raw image is fully and/or properly accounted for and the model is correct or appropriate. So for example, in some cases raw data may no longer be required, while in others it may be necessary to validate or ‘do better’ in the future. Moreover there are increasing pressures from bodies e.g. funders to make the data relating to research outputs Findable, Accessible, Interoperable and Reusable (FAIR). In acknowledgement of this situation and in order to begin addressing it, IUCr Journals now facilitate access to and citation of large raw diffraction datasets in its articles. Therefore it is important for our community understand and define how we manage our raw data in this respect.</p> <p>As Members of the IUCr Committee on Data we see the need to conduct this survey about exploring raw data archival<br> practice and gathering opinions as to if/how raw data could/should be used if it were to be made more widely<br> available.</p>
Research data archive of the sperical shock experiment with Chelyabinsk meteorite
<p>This archive contains files with research data supplement to a publication "Experimental constrains on the mechanism and the amount of spectral shock darkening in ordinary chondrite materials during asteroid collisions" by Kohout et al. The experiment and material description and sample / zone numbering is consistent with the publication.</p> <p>Content:</p> <p>Chelyabinsk X-ray MicrtoTomography (XMT) dataset of half-sphere and steel jacket cropped.</p> <p>EMPA + Raman results.xlsx - data file with results of Electron Microprobe Analysis (EMPA) and Raman Spectroscopy</p> <p>EMPA BSE images.zip - archive with EMPA Back Scaterred Electrons (BSE) images and locations of EMPA points</p> <p>Full-sized version of the figures in the manuscript</p> <p>Reflectance UV-VIS-NIR-MIR all data.xlsx - data file with reflectance measurements</p> <p>XRD.zip - data file with X-ray diffraction measurement</p> <p>Zone IV SEM BSE images.zip - archive with high-resolution Scanning Electron Microscope (SEM) BSE images of the zone IV</p> <p>Zone IV SEM EDS element profiles and maps.zip - archive with SEM Energy Dispersive Spectroscopy (EDS) element profiles and maps (in detector counts) of the needle olivine crystals in the zone IV</p>
Archived data from Batziakas et al. 2020
<p>Flow cytometry, inverse microscope and image analysis plankton data, as well as the Normalized Biomass Size Spectra (NBSS) constructed from said data of the planktonic communities of Elefsina Bay and Aghios Kosmas (Saronikos Gulf, Greece) during winter (December 2012) and summer (September 2013) in two layers.</p> <p>Data are presented in Batziakas S., Frangoulis C., Tsiola A., Nikolioudakis N., Tsagaraki T. M. and Somarakis S. (2020). Hypoxia changes the shape of the biomass size spectrum of planktonic communities: A case study in the eastern Mediterranean (Elefsina Bay). <em>J. Plankton Res.</em>, <strong>42(6)</strong>, 752-766. (DOI: <a href="http://dx.doi.org/10.1093/plankt/fbaa055">10.1093/plankt/fbaa055</a>)</p>
Data archives for all sessions, consisting of probes and instructional sessions outputs for each of the three research participants
<p>Three participants, identified with fictitious names, were exposed to Instructional and Probe sessions to induce and test for Bidirectional Naming (BiN). This Dataset is composed by the records of both types of individual sessions, as recorded originaly by the computer and the experimenter. In the begginig of the files there are some general information, and below are the data for trials. Each line represents a trial and informs the response latency, the stimuli presented and the response given, as well as the expected response.<br> The data is in portuguese, but the authors are at disposal for any doubts. The name of the file indicates the participant and the study phase.</p>
Data archive for the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method"
<p>Data archive accompanying the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method". In 2020 this article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em>. Data are uploaded in the form of ascii text files, Igor Pro experiment files (.pxp), and Jupyter notebook files. In addition, a Jupyter notebook file is included containing an implementation of the error model used in the paper.</p>
Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture
<p>Vertical profiles of temperature and dewpoint are useful in predicting deep convection that leads to severe weather that threatens property and lives. Currently, forecasters rely on observations from radiosonde launches and numerical weather prediction (NWP) models. Radiosonde observations are, however, temporally and spatially sparse, and NWP models contain inherent errors that influence short-term predictions of high-impact events. This work explores using machine learning (ML) to postprocess NWP model forecasts, combining them with satellite data to improve vertical profiles of temperature and dewpoint. We focus on different ML architectures, loss functions, and input features to optimize predictions. Because we are predicting vertical profiles at 256 levels in the atmosphere, this work provides a unique perspective at using ML for 1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML predictions offer the largest improvement for dewpoint, particularly in the mid- and upper-atmosphere. emperature improvements are modest, but CAPE values are improved by up to 40%. Feature importance analyses indicate that the ML models are primarily improving incoming RAP biases. While additional model and satellite data offer some improvement to the predictions, architecture choice is more important than feature selection in fine-tuning the results. Our proposed deep residual UNet performs the best by leveraging spatial context from the input RAP profiles; however, the results are remarkably robust across model architecture. Further, uncertainty estimates for every level are well-calibrated and can provide useful information to forecasters.</p>
Lost in translation: A historical-comparative reconstruction of Proto-Khoe-Kwadi based on archival data - Supplementary Material
<p>Supplementary Materials 1-4 for the following article: </p><p>Fehn, Anne-Maria & Jorge Rocha. 2023. Lost in translation: A historical-comparative reconstruction of Proto-Khoe-Kwadi based on archival data. Diachronica. https://doi.org/10.1075/dia.23022.feh.</p>
Music Data, Archiving for Community Use and Future Directions Through the Decade of Indigenous Languages
<p>Music Data, Archiving for Community Use and Future Directions Through the Decade of Indigenous Languages</p> <p>Linda Barwick</p> <p>Presented 5 October 2022 at the international conference "Where Do We Need to Go From Here?" Language Documentation and Archiving in the International Decade of Indigenous Languages</p>
Data from: Evaluating genotyping-in-thousands by sequencing as a genetic monitoring tool for a climate sentinel mammal using non-invasive and archival samples
<p>Genetic tools for wildlife monitoring can provide valuable information on spatiotemporal population trends and connectivity, particularly in systems experiencing rapid environmental change. Though many DNA sequencing approaches still require high quality and quantity of DNA obtained from traditional sources (e.g. blood and tissue), rapid genotyping tools such as Genotyping-in-Thousands by sequencing (GT-seq) have improved our ability to make use of degraded and less concentrated DNA commonly obtained from non-invasive and archival samples. Here, we developed a multi-purpose GT-seq panel (307 single nucleotide polymorphisms) for a climate sentinel mammal (the American pika, <em>Ochotona princeps</em>) for use as a genetic tool for monitoring populations in the Canadian Rocky Mountains. We optimized the panel using contemporary tissue samples (n = 77) and subsequently applied it to archival tissue (n = 17) and contemporary fecal pellet samples (n = 129) to evaluate its effectiveness at identifying individuals and sex, estimating relatedness, and inferring population structure. The panel demonstrated high efficacy with contemporary and archival tissue samples (94.7% and 90.5% genotyping success, respectively) and negligible genotyping error (0.001% and 0.0%, respectively). Despite relatively high genotyping success for fecal pellet samples (79.7%), high genotyping error (28.4%) limited its power as a monitoring tool to assess genetic variation using non-invasive samples and highlighted the need for further optimization around sample and data collection.</p>
Data archive for the peer-reviewed journal article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions"
<p>This data archive accompanies the article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions", which was accepted in November 2024 in the peer-reviewed journal Environmental Science and Technology Letters. The data archive contains the processed OP_DTT, PM loading, oxidant level, and elemental ratio measurements presented in this journal article. </p>
Archival bundle of the data used for "Predictive Auto-scaling with OpenStack Monasca" (UCC 2021)
<p>This archive contains the data used for the paper</p> <p><strong>Predictive Auto-scaling with OpenStack Monasca</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella<br> 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC)<br> <a href="https://doi.org/10.1145/3468737.3494104">10.1145/3468737.3494104</a></p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/UCC2021-predictive-auto-scaling-openstack">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>.log</p> </td> <td> <p>distwalk run log</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-pred.json</p> </td> <td> <p>Predictive metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-real.json</p> </td> <td> <p>Actual metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-times.csv</p> </td> <td> <p>Client-side response time for each request sent during a run</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models and data scalers used for the validation</p> </td> </tr> <tr> <td> <p>predictor.log</p> </td> <td> <p>monasca-predictor log</p> </td> </tr> <tr> <td> <p>predictor-times.log</p> </td> <td> <p>monasca-predictor` log (timing info only)</p> </td> </tr> <tr> <td> <p>predictor-times-{lin,mlp,rnn}.{csv,log}</p> </td> <td> <p>monasca-predictor log (timing info only, group by predictor)</p> </td> </tr> <tr> <td> <p>super_steep_behavior.csv</p> </td> <td> <p>Dataset used to train MLP and RNN models</p> </td> </tr> <tr> <td> <p>test_behavior_02_distwalk-6t_last100.dat</p> </td> <td> <p>distwalk load trace</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-min-distwalk.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>* <em>contact author</em></p>
Data Archive for "Nonequilibrium Statistical Thermodynamics of Multicomponent Interfaces"
<p>Selected data, including certain simulation output, analysis scripts, and processed data files used for figures.</p>
Data archive for paper "Machine Learning Emulation of Urban Land Surface Processes"
<p>This archive contains models, data* (Overview), as well as the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002744">Machine Learning Emulation of Urban Land Surface Processes</a>".</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/">Singularity</a> (tested with version 3.6.3-1.el8)</li> </ul> <p>Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p><strong>Overview</strong></p> <p>A general overview of the repository structure is given below. Due to licensing restrictions analysis and forcing data (*) cannot be included and need to be requested separately (see Initialization). Data derivatives (**) from either analysis or forcing, as well as intermediary data (***), are not included as they can be generated by rerunning experiments (see Usage).</p> <pre><code>. ├── data │ ├── analysis* │ ├── forcing* │ ├── teb │ ├── utils │ ├── wps │ └── wrf ├── hpc ├── models │ ├── teb │ ├── unn │ ├── wps │ └── wrf-unn ├── notebooks ├── outputs │ ├── analysis** │ ├── benchmark*** │ ├── forcing** │ ├── kerastuner*** │ ├── notebooks │ ├── tabular │ ├── teb** │ ├── unn** │ ├── wps*** │ └── wrf ├── paper │ └── figures ├── singularity └── tools </code></pre> <p><strong>Initialization</strong></p> <p>Forcing and analysis data need to be requested separately. The following directories should map to their respective data archives:</p> <ul> <li><code>./data/analysis</code> -> <a href="http://doi.org/10.5281/zenodo.4678387">Grimmond et al. (2013)</a></li> <li><code>./data/forcing</code> -> <a href="http://doi.org/10.5281/zenodo.4679279">Grimmond et al. (2021)</a></li> </ul> <p><strong>Usage</strong></p> <p>To rerun all experiments and reproduce results, run <code>tools/run_all.sh</code> from your command prompt. After completion, all results are saved in the <code>outputs</code> directory. Note that WRF simulations require high CPU time and may take hours or days to complete.</p> <p>Alternatively, if <a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System (PBS)</a> is available on your system, the following helpers may be used instead:</p> <pre><code>qsub hpc/submit_init.pbs qsub hpc/submit_tuner.pbs qsub hpc/submit_unn.pbs qsub hpc/submit_find_median_unn.pbs qsub hpc/submit_wrf.pbs qsub hpc/submit_postprocess.pbs qsub hpc/submit_benchmark.pbs </code></pre> <p>Note that you may need to modify PBS helper scripts to suit your specific environment.</p> <p><strong>Development notes</strong></p> <p>See DEVELOP.md.</p> <p><strong>License</strong></p> <p>The source code developed for this work is licensed under MIT (<code>LICENSE_CODE.txt</code>). For licensing information of third-party software see licenses under the <code>models</code> directory. Data files in this archive, including the initial and boundary condition data from the European Centre for Medium-Range Weather Forecasts (<code>data/wps/ungrib</code>), are licensed under CC BY-NC 4.0 (<code>LICENSE_DATA.txt</code>).</p>
Data archive for "Rock abundance on the lunar mare on surfaces of different age: Implications for regolith evolution and thickness"
<p>This is associated data for the study "<strong>Rock abundance on the lunar mare on surfaces of different age: Implications for regolith evolution and thickness </strong>" in GRL.</p> <p>The zipfile FT2014-Dense50kmN-n800mto5km.tif.zip is a raster of neighborhood crater frequencies from Fassett and Thomson 2014 (JGR) that are used for the age information in this paper. </p> <p>The csv file is the extracted rock abundance for every frequency pixel in this dataset. </p> <p>A github software release associated with the paper is also available on <a href="https://zenodo.org/badge/latestdoi/417542233">as a Zenodo software repository</a>, as well as on <a href="https://github.com/cfassett/MareRockAbundances">Github</a>.</p>
Archived data for: Balancing selection, genetic drift, and human mediated-introgression interplay to shape MHC (functional) diversity in Mediterranean brown trout
<p>The extraordinary polymorphism of Major Histocompatibility Complex (MHC) genes is considered a paradigm of pathogen-mediated balancing selection, although empirical evidence is still scarce. Furthermore, the relative contribution of balancing selection to shape MHC population structure and diversity, compared to that of neutral forces, as well as its interaction with other evolutionary processes such as hybridization, remains largely unclear. To investigate these issues, we analysed adaptive (MHC-DAB gene) and neutral (11 microsatellite loci) variation in 156 brown trout (<i>Salmo trutta </i>complex) from six wild populations in central Italy exposed to introgression from domestic hatchery lineages (assessed with the LDH gene). MHC diversity and structuring correlated with those at microsatellites, indicating the substantial role of neutral forces. However, individuals carrying locally rare MHC alleles/supertypes (regardless of the zygosity status and degree of sequence dissimilarity of MHC) were in better body condition (a proxy of individual fitness/parasite load), hence supporting balancing selection under rare allele advantage, but not heterozygote advantage or divergent allele advantage. The association between specific MHC supertypes and body condition confirmed in part this finding. Across populations, MHC allelic richness increased with increasing admixture between native and domestic lineages, indicating introgression as a source of MHC variation. Furthermore, introgression across populations appeared more pronounced for MHC than microsatellites, possibly because initially-rare MHC variants are expected to introgress more readily under rare allele advantage. Providing evidence for the complex interplay among neutral evolutionary forces, balancing selection and human-mediated introgression in shaping the pattern of MHC (functional) variation, our findings contribute to a deeper understanding of the evolution of MHC genes in wild populations exposed to anthropogenic disturbance.</p>
Data archive for the publication: Multi-variable compensated quantum yield measurements of upconverting nanoparticles with high dynamic range: a systematic approach
<p>The two compressed repositories contain raw and analysed data for the following publication on Optics Express: <strong><a href="https://doi.org/10.1364/OE.452874">Multi-variable compensated quantum yield measurements of upconverting nanoparticles with high dynamic range: a systematic approach</a></strong></p> <p>The code for processing and analysing these data is available on GitHub and it has its own DOI. </p> <p>To access the code and for instructions on how to run the it follow the link: <a href="https://github.com/Biophotonics-Tyndall/QY-System-paper">Biophotonics-Tyndall/QY-System-paper (github.com)</a>. </p>
Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"
<p>Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019). </p> <p> </p>
Data archive for "Seamless lightning nowcasting with recurrent-convolutional deep learning"
<p>This dataset contains the machine learning training data files, pretrained model weights and precomputed results for the paper "Seamless lightning nowcasting with recurrent-convolutional deep learning" published in:<br> Leinonen, J., Hamann, U., & Germann, U. (2022). Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning, <em>Artificial Intelligence for the Earth Systems</em>, <em>1</em>(4), e220043, doi:<a href="https://doi.org/10.1175/AIES-D-22-0043.1">10.1175/AIES-D-22-0043.1</a>.<br> A preprint of the paper can be found at <a href="https://arxiv.org/abs/2203.10114">https://arxiv.org/abs/2203.10114</a>.</p> <p>The ML code can be found at <a href="https://github.com/MeteoSwiss/c4dl-lightningdl">https://github.com/MeteoSwiss/c4dl-lightningdl</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Training data (c4dl-patches-*.zip) -> data/2020/</li> <li>Results (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-results-lightningdl.zip?versionId=54046830-4c7e-48c6-af42-d6d5606af86b">c4dl-results-lightningdl.zip</a>) -> results/</li> <li>Pretrained models (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-models-lightningdl.zip?versionId=364bca7c-e6ad-4ed9-9264-57c759ea0ac6">c4dl-models-lightningdl.zip</a>) -> models/</li> </ul> <p>Additionally, the file <a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-randomexamples-lightningdl.zip?versionId=426ca113-950f-4a50-8ef0-8e5d12afe697">c4dl-randomexamples-lightningdl.zip</a> contains the randomly selected examples complementing Figs. 7–9 of the paper, and the file <a href="https://zenodo.org/api/files/939609f2-6699-4f56-9428-391ebe78e010/c4dl-inputsamples-lightningdl.zip">c4dl-inputsamples-lightningdl.zip</a> contains figures showing samples of all the input variables for the three cases shown in Figs. 7–9.</p>
Archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil
<p>This is the archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil; submitted to the Journal Science of the Total Environment.</p>
Samoan Passage Towyo Data Archive
<p>Samoan Passage Towyo Data Archive. Please note that the copy here on zenodo contains only the GitHub repository, data are stored elsewhere.</p> <p>Head to <a href="https://github.com/gunnarvoet/sp-data-archive-towyo">https://github.com/gunnarvoet/sp-data-archive-towyo</a> for instructions on how to clone the full dataset or download data files manually at <a href="https://osf.io/uaem3/">https://osf.io/uaem3/</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.