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1,298 results for “Archive”
Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"
<p><strong>Overview</strong></p> <p>This archive contains models, data, and the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002550">Machine Learning Emulation of 3D Cloud Radiative Effects</a>".</p> <p>For the Python tool to generate synthetic data, please refer to the <a href="https://github.com/dmey/synthia">Synthia repository</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/singularity/">Singularity</a> (tested with version 3.6.3-1.el8)*.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler**.</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>**Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>).</p> <p><strong>Initialization</strong></p> <p>Deflate the data archive with:</p> <pre><code>./init.sh </code></pre> <p>Compile ecRad with Singularity:</p> <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <p><strong>Usage</strong></p> <p>To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder:</p> <pre><code>qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub submit_benchmark.sh </code></pre> <p>then, to plot stats and identify notebooks run:</p> <pre><code>qsub submit_stats.sh </code></pre> <p><strong>License</strong></p> <p>Paper code released under the <a href="./LICENSE.txt">MIT license</a>. Data released under <a href="./data/LICENSE.txt">CC BY 4.0</a>. <a href="https://confluence.ecmwf.int/display/ECRAD">ecRad</a> released under the <a href="./ecrad/LICENSE">Apache 2.0 license</a>.</p>
Proteomic source data archive
<p>Source data from proteomic analysis performed in manuscript "<strong>Mitochondrially targeted tamoxifen alleviates markers of obesity and type 2 diabetes mellitus" </strong>by<strong> </strong>Vacurova et al.</p>
Reproducible, high-dimensional imaging in archival human tissue by Multiplexed Ion Beam Imaging by Time-of-Flight (MIBI-TOF)
<p>1. SingleChannelMIBI.zip: Single-channel MIBI-TOF images</p> <p>All folders are labeled as Slide[Number]Stain[Number]_Point[Number]_[TMACoreIndex], where the slide number and stain number correspond to the slide and day of staining, the point number corresponds to the order in which the images were collected for each slide, and the TMA core index corresponds to the ID of the tissue microarray core. Each folder contains single-channel TIFFs for each marker. See paper for details.</p> <p>2. SegmentationOutput.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021, https://www.deepcell.org/predict). Output of Mesmer that delineates the single cells in each of the images is included here. Naming convention is the same as above.</p> <p>3. DataTables.zip: Data tables that are needed to run mpi_ppp_ihc_regression.ipynb</p> <p>Contains MIBI-TOF data (ionpath_processed_data.csv), MIBI-TOF calibration data (calibration_data.csv), IHC data (ihc_data.csv), and a map of each sample to its tissue type (tissue_data.csv). Also includes cell table output from Mesmer with the cell clusters appended to the table (cell_table_size_normalized_clusters.csv).</p>
Theta: portfolio of CEGAR-based analyses with dynamic algorithm selection (Competition Contribution): Tool Archive
<p>This archive contains the tool archive of Theta for SV-COMP 2022, which was also added as a release to the tool (<a href="https://github.com/ftsrg/theta/releases/tag/svcomp22-v1">here</a>).</p>
Replication Archive for "Parental Disability and Teenagers' Time Allocation"
<p>This archive includes the STATA code to replicate all results in the referenced paper.</p>
Archive data supporting the results in the paper: Long-term soil warming alters fine root dynamics and morphology, and their ectomycorrhizal fungal community in a temperate forest soil"
<p><span>Climate warming is predicted to affect temperate forests severely, but the response of fine roots, key to plant nutrition, water uptake, soil carbon and nutrient cycling is unclear. Understanding how fine roots will respond to increasing temperature is a prerequisite for predicting the functioning of forests in a warmer climate. We studied the response of fine roots and their ectomycorrhizal (EcM) fungal and root-associated bacterial communities to soil warming by 4 °C in a mixed spruce-beech forest in the Austrian Limestone Alps after 8 and 14 years of soil warming, respectively. Fine root biomass (FRB) and fine root production were 17% and 128% higher in the warmed plots, respectively, after 14 years. The increase in FRB (13%) was not significant after 8 years of treatment, whereas specific root length, specific root area, and root tip density were significantly higher in warmed plots at both sampling occasions. Soil warming did not affect EcM exploration types and diversity, but changed their community composition, with an increase in the relative abundance of <em>Cenoccocum</em> at 0 – 10 cm soil depth, a drought-stress tolerant genus, and an increase in short and long-distance exploration types like <em>Sebacina </em>and <em>Boletus </em>at 10 – 20 cm soil depth. Warming increased the root-associated bacterial diversity but did not affect their community composition. Soil warming did not affect nutrient concentrations of fine roots, though we found indications of limited soil phosphorus (P) and potassium (K) availability. </span><span>Our findings suggest that, in the studied ecosystem, global warming could persistently increase soil carbon inputs due to accelerated fine root growth and turnover, and could simultaneously alter fine root morphology and EcM fungal community composition towards improved nutrient foraging. </span></p>
ROOOH modeling data — archive
<p>This archive contains ECHAM-HAMMOZ model output files used for the paper:</p> <p>Hydrotrioxide (ROOOH) formation in the atmosphere</p> <p>by Berndt et al.</p> <p> </p>
Poster from Archives of Ola and Kehinde Oni (test)
<p>From Archives of Ola and Kehinde Oni (test)</p>
Archive for regression and estimation of the total freeboard of the Arctic using AMSR2 data v2.0
<p>This fileset contains data used for generating Tables and Figures in Kim et al. (2022). All file has csv format separated by a comma. In the first line of the individual file, we provide the variable name and unit of each column.</p> <p> </p> <p>The fileset has 6 sub-directories. The name of each directory indicates the figures corresponding to its contents.</p> <p>• Figure 2,7</p> <p>• Figure 4 (and Table 1-3)</p> <p>• Figure 5</p> <p>• Figure 6,8</p> <p>• Figure 6_ICESat2</p> <p>• Figure A1</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PM</p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PMT</p>
Archival bundle of the data used for "Extending OpenStack Monasca for Predictive Elasticity Control"
<p>This archive contains the data used for the paper</p> <p><strong>Extending OpenStack Monasca for Predictive Elasticity Control</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella</p> <p> </p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/predictive-elasticity-monasca">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,aim,mlp,rnn,stc}-<INCREMENTAL-ID>.csv</p> </td> <td> <p>Run traces</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>This work extends our <a href="https://doi.org/10.1145/3468737.3494104">previous one</a> appeared at the <em>IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC'21)</em>. </p> <p>* <em>contact author</em></p>
A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1
<p>Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural<br> Networks ?" submitted to the journal Information and Software Technology</p> <p>Replication package using the data is available at https://github.com/FlowSs/PMT</p>
Aesthetic Trends and Semantic Web Adoption of Media Outlets Identified through Automated Archival Data Extraction
<p>This dataset includes a variety of structured data gathered via various Web data extraction techniques which were employed in order to collect current and archival data from almost a thousand news websites that are popular in Greece, for the purpose of monitoring and recording their progress through time. The collected information, that took the form of a website’s source code and an impression of their homepage in different time instances of the last decade, has been used to identify trends concerning Semantic Web integration, DOM structure complexity, number of graphics, color usage and more. In total more than ten thousands impressions (including screenshots and source code) were analyzed which resulted to conclusions regarding the evolution of aesthetics and the adoption of new technologies.</p>
Raw images, annotations, and vvipr code archive to support 'Evaluating thermal and color sensors for automating detection of penguins and pinnipeds in images collected with an unoccupied aerial system''
<p>Images, annotations, and code archived here were used in the paper "Evaluating a machine learning approach to detect penguins and pinnipeds in thermal and color images collected with an unoccupied aerial system" submitted for publication in Drones. The files contain raw thermal and color images of aggregations of gentoo (<em>Pygoscelis papua</em>) and chinstrap (<em>P. antarcticus</em>) penguins and Antarctic fur seals (<em>Arctocephalus gazella</em>). All images were collected with the Flir DuoPro R camera (Teledyne FLIR LLC, Wilsonville, OR, U.S.A.), carried into flight under an APH-28 hexacopter (Aerial Imaging Solutions, LLC, Old Lyme, CT, U<strong>.</strong>S<strong>.</strong>A<strong>.)</strong> at Cape Shirreff, Livingston Island, Antarctica (60.79 °W, 62.46 °S), during the austral summer of 2019-20. All aerial surveys occurred under the Marine Mammal Protection Act Permit No. 20599 granted by the Office of Protected Resources/National Marine Fisheries Service, the Antarctic Conservation Act Permit No. 2017-012, NMFS-SWFSC Institutional Animal Care and Use Committee Permit No. SWPI 2014-03R, and all domestic and international UAS flight regulations. The annotations of the images were conducted using VIAME desktop software (v 0.16.1 or later;<a href="https://github.com/VIAME/VIAME">https://github.com/VIAME</a>) or the online using the DIVE interface (https://viame.kitware.com/). Model results were assessed with the vvipr code (v.0.3.2), archived here and available online (https://github.com/us-amlr/vvipr/releases/tag/v0.3.2).</p> <p> </p>
Data Archive of 'Hadley Cell Edge Modulates the Role of Ekman Heat Flux in a Future Climate'
<p>Output data used to create figures in ' Hadley Cell Edge Modulates the Role of Ekman Heat Flux in a Future Climate ' is archived. Original data sources from which the output data are generated are Reanalysis Products (ERA5, JRA55, NCEP/NCAR reanalysis) and 8 CMIP6 model simulations.</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 12 September 2015
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 12 September 2015</p>
Skeleton Test Suite and PRONOM Archive v73
<p>Skeleton Test Suite and PRONOM Archive v73</p>
List of publishers allowing deposit of preprint and postprint or final version in an open archive.
<p>The content of this dataset is the following:</p> <p><em>api29.green.xml</em><br> List of the publishers allowing deposit of the preprint (version submitted to the publisher) and of the postprint (version reviewed by the publisher) or the final version (version published with the publisher's layout) in a open archive.</p> <p><em>api29.blue.xml</em><br> List of the publishers allowing deposit of the postprint (version reviewed by the publisher) or the final version (version published with the publisher's layout) in a open archive.</p> <p><em>api29.yellow.xml</em><br> List of the publishers allowing deposit of the preprint (version submitted to the publisher) in an open archive.</p> <p><em>api29.greenCH.xml</em><br> List of Swiss journals allowing deposit of preprint and postprint or final version in an open archive.</p> <p><em>api29.greenGB.xml</em><br> List of British journals allowing deposit of preprint and postprint or final version in an open archive.</p> <p><em>api29.greenUS.xml</em><br> List of American journals allowing deposit of preprint and postprint or final version in an open archive.</p> <p>These files describe publishers by providing their name, conditions of deposit in an open archive of preprints (can | cannot), postprints (can | cannot) and final version (can | cannot). Potential restrictions are also mentionned.</p>
West Heslerton Anglo-Saxon Settlement -Primary Excavation Archive for interactive viewing in Google Earth Pro
<p>The Landscape Research Centre pioneered digital recording in field archaeology using hand-held computers in the field from the mid 1980s onwards. The excavation of an Early Anglo-Saxon settlement covering nearlly 25Ha, funded by English Heritage from the rescue archaeology commissions budget was one of the largest excavations in Europe conducted between 1986 and 1996 with an analytical program that contuniued into th 2020s. The digital plans provide an interactive interface to the primary excavation archives when this file id loaded inot Google Earth Pro.</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.