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306 results for “data archive”

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edi60/100

Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to a Darwin Core Archive)

This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/345/4, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894

openCC (other)Dec 2022View details →
zenodo52/100

Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"

<p>This datasets supports the paper &quot;Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network&quot; submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file &quot;goes-samples-2019-128x128.nc&quot; contains the training dataset called &quot;GOES-COT&quot; in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files &quot;gen_weights*.nc&quot; contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> &nbsp;</p>

opencc-by-4.0May 2020View details →
zenodo52/100

FuTRES (Functional Trait Resource for Environmental Studies) data store archival copy - 5/21/2022

<p>&nbsp;</p> <p>The Functional Trait Resource for Environmental Studies (FuTRES) project is a collaborative project among four universities (University of Oregon, University of Arizona, University of Florida, and Howard University). The key deliverables of FuTRES are a workflow for assembling functional trait data measured at the specimen level, a database to serve that data, and scientific publications demonstrating the utility of the assembled data.&nbsp; This dataset represents the FuTRES datastore as of 5/21/2022, providing an archive that is timestamped and providing all data that is not currently embargoed by providers.&nbsp; &nbsp;The column headers for FuTRES data are:&nbsp;&nbsp;basisOfRecord,catalogNumber,class,collectionCode,country,decimalLatitude,decimalLongitude,diagnosticID,eventID,family,genus,individualID,institutionCode,lifeStage,locality,mapped_project,materialSampleID,maximumChronometricAge,maximumChronometricAgeReferenceSystem,maximumElevationInMeters,measurementMethod,measurementSide,measurementType,measurementUnit,measurementValue,minimumChronometricAge,minimumChronometricAgeReferenceSystem,minimumElevationInMeters,observationID,occurrenceID,occurrenceRemarks,order,reproductiveCondition,samplingProtocol,scientificName,sex,specificEpithet,stateProvince,verbatimElevation,verbatimEventDate,verbatimLatitude,verbatimLocality,verbatimLongitude,verbatimMeasurementUnit,yearCollected,projectID,inferred_traits.&nbsp; The traits available and number of records for each trait:&nbsp;</p> <ul> <li><a href="https://futres-data-interface.netlify.app/">length&nbsp;(1,790,883)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tail length&nbsp;(520,281)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length&nbsp;(456,165)</a></li> <li><a href="https://futres-data-interface.netlify.app/">pes length&nbsp;(413,668)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ear length to notch&nbsp;(397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">external ear length&nbsp;(397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body mass&nbsp;(373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">weight&nbsp;(373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">width&nbsp;(7,429)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus width&nbsp;(1,705)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 proximal articular breadth&nbsp;(783)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface width&nbsp;(782)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 breadth&nbsp;(716)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 depth&nbsp;(706)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus length&nbsp;(649)</a></li> <li><a href="https://futres-data-interface.netlify.app/">long bone length&nbsp;(637)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface width&nbsp;(605)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus trochlea breadth&nbsp;(596)</a></li> <li><a href="https://futres-data-interface.netlify.app/">epiphysis width&nbsp;(581)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus breadth&nbsp;(560)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus medial depth&nbsp;(549)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tooth row length&nbsp;(498)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower tooth row length&nbsp;(425)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal width&nbsp;(413)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface length&nbsp;(402)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 4 occlusal surface width&nbsp;(361)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface length&nbsp;(343)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur width&nbsp;(301)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length&nbsp;(297)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus width&nbsp;(293)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface width&nbsp;(261)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface length&nbsp;(260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis width&nbsp;(260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface length&nbsp;(242)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia length&nbsp;(228)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal breadth&nbsp;(208)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal depth&nbsp;(201)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface width&nbsp;(197)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface length&nbsp;(187)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface width&nbsp;(185)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface length&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface width&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface width&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary canine tooth to premolar tooth 3 length&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface length&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface width&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface length&nbsp;(180)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface length&nbsp;(177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface width&nbsp;(177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface length&nbsp;(176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface width&nbsp;(176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal width&nbsp;(162)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 3 occlusal surface length&nbsp;(159)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface length&nbsp;(155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface width&nbsp;(155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis breadth&nbsp;(145)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface length&nbsp;(120)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface width&nbsp;(119)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis depth&nbsp;(111)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface width&nbsp;(106)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface length&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper premolar tooth 1 occlusal surface length&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface length&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface width&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal breadth&nbsp;(85)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface length&nbsp;(81)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface length&nbsp;(79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface width&nbsp;(79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface length&nbsp;(78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface width&nbsp;(78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlea breadth&nbsp;(76)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus medial trochlear height&nbsp;(74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear height at sagittal crest&nbsp;(74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear sulcus height&nbsp;(74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal depth&nbsp;(73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1-2 length&nbsp;(73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper tooth row length&nbsp;(73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">anterior tibial tuberosity length&nbsp;(70)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus distal depth&nbsp;(69)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length&nbsp;(67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna width&nbsp;(67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia medial length&nbsp;(63)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis breadth&nbsp;(57)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis depth&nbsp;(54)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 3 occlusal surface length&nbsp;(51)</a></li> <li><a href="https://futres-data-interface.netlify.app/">trochlea tali length&nbsp;(49)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis breadth&nbsp;(45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">forelimb zeugopod bone length&nbsp;(45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal breadth&nbsp;(44)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna length&nbsp;(42)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal breadth&nbsp;(40)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to caput&nbsp;(38)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus length&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur caput depth&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to ventral tubercle&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus proximal breadth&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis depth&nbsp;(36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal depth&nbsp;(36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur trochlea breadth&nbsp;(32)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal depth&nbsp;(31)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface width&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus lateral length&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface length&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface width&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to lateral condyle&nbsp;(28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from greater trochanter to medial condyle&nbsp;(28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length with tail&nbsp;(25)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 1 occlusal surface length&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 2 occlusal surface length&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna depth across the process anaconaeus&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna proximal articular breadth&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1 occlusal surface length&nbsp;(22)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon depth&nbsp;(21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon length&nbsp;(21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 2 occlusal surface length&nbsp;(21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">breadth of calcaneal body&nbsp;(18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus width&nbsp;(18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia lateral length&nbsp;(14)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to medial condyle&nbsp;(5)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius distal width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius length&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal articular width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body height&nbsp;(1)</a></li> <li><a href="https://futres-data-interface.netlify.app/">height&nbsp;(1)</a></li> </ul>

opencc-by-4.0May 2022View details →
edi52/100

Moss point transect data for the Kuparuk River near Toolik Field Station, Alaska 1993-current. (Reformatted to a Darwin Core Archive)

This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/280/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-arc/10317/8. The abstract below was extracted from the Level 0 data package and is included for context: This file contains the consolidated data for percent cover of dominant bryophytes and other easily identifiable macro-algae in the experimental reaches of the Kuparuk River beginning in 1993 and updated annually. In some years percent cover was recorded more than one time per season. In all years percent cover was recorded in riffle habitats and in some (early) years percent cover was recorded for pool habitats. Moss point transects have been done on the Kuparuk since 1993. The Arctic is one of the most rapidly warming regions on Earth. Responses to this warming involve acceleration of processes common to other ecosystems around the world (e.g., shifts in plant community composition) and changes to processes unique to the Arctic (e.g., carbon loss from permafrost thaw). The objectives of the Arctic Long-Term Ecological Research (LTER) Project for 2017-2023 are to use the concepts of biogeochemical and community “openness” and “connectivity” to understand the responses of arctic terrestrial and freshwater ecosystems to climate change and disturbance. These objectives will be met through continued long-term monitoring of changes in undisturbed terrestrial, stream, and lake ecosystems in the vicinity of Toolik Lake, Alaska, observations of the recovery of these ecosystems from natural and imposed disturbances, maintenance of existing long-term experiments, and initiation of new experimental manipulations. Based on these data, carbon and nutrient budgets and indices

openCC (other)Jul 2021View details →
zenodo48/100

Paleo-CO2 Data Archive

<p>These data&nbsp;were compiled from published paleo-CO<sub>2</sub>&nbsp;data that have been assembled&nbsp;by an international group of proxy experts supported through an&nbsp;NSF-funded Research Coordination Network. It&nbsp;brings together&nbsp;paleo-CO<sub>2</sub> reconstruction data from terrestrial and marine samples, and the compilation includes data derived from multiple proxies including Phytoplankton, Boron, Stomatal Frequencies, Leaf Gas Exchange, Liverworts, Land Plant Carbon Isotopes, Paleosols, and Nahcolite.&nbsp;&nbsp;These are used in the interactive archive plot made available on the Paleo-CO2 project web page at (https://www.paleo-co2.org) and are also archived in the NCDC database (<a href="https://www.ncdc.noaa.gov/paleo/study/35079">https://www.ncdc.noaa.gov/paleo/study/35079</a>).&nbsp;</p>

opencc-by-4.0Nov 2023View details →
zenodo48/100

Spacekit Data Archive

<p>Collection of datasets, models and training results&nbsp;for <a href="https://github.com/spacetelescope/spacekit"><strong>spacekit</strong></a> machine learning algorithms. To learn more, please visit <a href="https://spacekit.readthedocs.io/en/latest/">https://spacekit.readthedocs.io/en/latest/</a></p> <p>Versioning note: modifications to existing uploads are indicated by major version iterations (e.g. 1.0, 2.0, 3.0); new file additions&nbsp;are denoted by minor version increments&nbsp;(e.g. 1.1, 1.2, 1.3) since these are inherently backwards compatible.</p>

opencc-by-4.0Apr 2023View details →
zenodo48/100

Energy System Time Series Suite (ESTSS) - Data Archive

<h2>Energy System Time Series Suite - Data Archive</h2> <p>&nbsp;</p> <p>This archive contains variously sized sets of declustered time series within the context of energy systems. These series demonstrate low discrepancy and high heterogeneity in feature space, resulting in a roughly uniform distribution within this space.</p> <p>For detailed information, please refer to the corresponding GitHub project:<br><a href="https://github.com/s-guenther/estss/">https://github.com/s-guenther/estss/</a></p> <p>For associated research, see<br><a href="https://doi.org/10.1186/s42162-024-00304-8">https://doi.org/10.1186/s42162-024-00304-8</a></p> <p>Data is provided in .csv format. The GitHub project includes a Python function to load this data as a dictionary of pandas data frames.</p> <p>Should you utilize this data, kindly also cite the associated research paper. For any queries, please feel free to reach out to us through GitHub or the contact details provided at the end of this readme file.</p> <p>&nbsp;</p> <h3>Folder Content</h3> <ul> <li>`ts_*.csv`: Contains declustered load profile time series in tabular format. <ul> <li>Size: `(n+1) x (m+1)`, with `n` representing time steps (1000 per series) and `m` the number of series.</li> <li>Includes a header row and index column. Headers indicate series id, and the index column numbers each time step, starting from `0`.</li> <li>The first half of the series `(m/2)` consistently display a constant sign (negative). They are sequentially numbered from 0.</li> <li>The second half `(m/2)` display varying signs. Numbering starts from `1,000,000`.</li> </ul> </li> <li>`features_*.csv`: Tabulates features corresponding to the time series. <ul> <li>Size: `(m+1) x (f+1)`, where `m` is the number of time series and `f` is the number of features</li> <li>Includes a header row and index column. Indexes represent time series id (matching `ts_*.csv` headers), and headers name the features.</li> </ul> </li> <li>`norm_space_*.csv`: Shows feature vectors in normalized feature space where time series are declustered. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x (g+1)`, where `m` is the number of timer series and `g` is the number of selected features space features. (a subset of `f` from `features_*.csv`).</li> <li>Format matches `features_*.csv`.</li> </ul> </li> <li>`info_*.csv`: Maps declustered datasets to the manifolded dataset. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x 2`, with `m` as series count. Columns contain manifolded set time series ids.</li> <li>Includes an index column and a header. The index holds the remapped id of declustered series. Header `0` is non-significant.</li> </ul> </li> </ul> <p>Each `ts_*.csv`, `features_*.csv`, `norm_space_*.csv`, and `info_*.csv` file comes in four versions to accommodate various set sizes:</p> <ul> <li>`*_4096.csv`</li> <li>`*_1024.csv`</li> <li>`*_256.csv`</li> <li>`*_64.csv`</li> </ul> <p>These represent sets with 4096, 1024, 256, and 64 time series, respectively,offering different densities in feature space population. The objective is to balance computational load and resolution for individual research needs.</p> <p>&nbsp;</p> <h3>Contact</h3> <p>ESTSS - Energy System Time Series Suite<br>Copyright (C) 2023<br>Sebastian G&uuml;nther<br>sebastian.guenther@ifes.uni-hannover.de</p> <p>Leibniz Universit&auml;t Hannover<br>Institut f&uuml;r Elektrische Energiesysteme<br>Fachgebiet f&uuml;r Elektrische Energiespeichersysteme</p> <p>Leibniz University Hannover<br>Institute of Electric Power Systems<br>Electric Energy Storage Systems Section</p> <p><a href="https://www.ifes.uni-hannover.de/ees.html">https://www.ifes.uni-hannover.de/ees.html</a></p>

opencc-by-sa-4.0Nov 2023View details →
zenodo48/100

MALDI MS data and metadata from "A biocodicological analysis of the medieval library and archive from Orval Abbey, Belgium"

<p>See <a href="https://doi.org/10.1098/rsos.210210">Ruffini-Ronzani et al</a>.</p>

opencc-by-4.0Oct 2021View details →
zenodo48/100

Librecast: IoT Software Updates over IPv6 Multicast Archive of all experimental data

<p>The librecast project states that ``Multicast is, by definition, the most efficient way for multiple nodes to communicate&#39;&#39;. &nbsp;This experiment is designed to provide evidence of this efficiency by comparing multicast and unicast methods of sending the same data to a large number of nodes, as would for example happen when a software update is released.<br> <br> The data set covers the experimental runs on the Virtual Wall 1 at IMEC as part of the Fed4Fire+ &quot;SME and NGI Cascaded Experiments&quot; https://www.fed4fire.eu/demo-stories/cc/librecasttesting/</p> <p>This directory contains raw experiment results as produced by the &quot;run-experiment&quot; script. File names containing &quot;.test.&quot; are experiment runs using code changes which we decided not to keep, and are excluded from processing and summarising. File names containing &quot;.partial.&quot; are experiment runs which were interrupted for some reason (usually when some nodes in a testbed stopped responding, and we could not get a complete set). These are also excluded from processing and summarising.</p> <p>Summaries:</p> <p>Results collated by testbed:</p> <table> <tbody> <tr> <th>Testbed</th> <th>Booted</th> <th>Clients</th> <th>Routers</th> <th>Runs</th> </tr> <tr> <td>S1L20B</td> <td>2022-01-19 10:15:08 UTC</td> <td>20</td> <td>0</td> <td>1</td> </tr> <tr> <td>S1L20C</td> <td>2022-01-19 10:15:08 UTC</td> <td>20</td> <td>0</td> <td>30</td> </tr> <tr> <td>S1L40</td> <td>2022-02-16 12:17:41 UTC</td> <td>40</td> <td>0</td> <td>6</td> </tr> <tr> <td>S1L48A</td> <td>2022-02-18 21:27:33 UTC</td> <td>48</td> <td>0</td> <td>6</td> </tr> <tr> <td>S1L49F</td> <td>2022-02-25 19:03:03 UTC</td> <td>49</td> <td>0</td> <td>11</td> </tr> <tr> <td>S1L50</td> <td>2022-02-17 21:02:31 UTC</td> <td>50</td> <td>0</td> <td>2</td> </tr> <tr> <td>S1L51G</td> <td>2022-02-28 19:23:06 UTC</td> <td>51</td> <td>0</td> <td>14</td> </tr> <tr> <td>S1R1L19C</td> <td>2022-01-28 08:41:43 UTC</td> <td>19</td> <td>1</td> <td>1</td> </tr> <tr> <td>S1R1L19D</td> <td>2022-01-28 08:41:43 UTC</td> <td>19</td> <td>1</td> <td>8</td> </tr> <tr> <td>S1R1L20A</td> <td>2022-02-11 17:14:31 UTC</td> <td>20</td> <td>1</td> <td>13</td> </tr> <tr> <td>S1R3L10H</td> <td>2022-03-09 20:46:54 UTC</td> <td>40</td> <td>7</td> <td>4</td> </tr> <tr> <td>S1R3L5B</td> <td>2022-01-24 19:09:44 UTC</td> <td>20</td> <td>7</td> <td>1</td> </tr> <tr> <td>S1R3L5C</td> <td>2022-01-24 19:09:44 UTC</td> <td>20</td> <td>7</td> <td>7</td> </tr> </tbody> </table> <p>&nbsp;</p> <p>Results collated by number of clients:</p> <table> <tbody> <tr> <th>Collection</th> <th>Clients</th> <th>Runs</th> </tr> <tr> <td>Multiple LANs</td> <td>19</td> <td>9</td> </tr> <tr> <td>Two LANs</td> <td>19</td> <td>9</td> </tr> <tr> <td>All testbeds</td> <td>19</td> <td>9</td> </tr> <tr> <td>Single LAN</td> <td>20</td> <td>31</td> </tr> <tr> <td>Multiple LANs</td> <td>20</td> <td>21</td> </tr> <tr> <td>Two LANs</td> <td>20</td> <td>13</td> </tr> <tr> <td>Multiple LANs</td> <td>20</td> <td>8</td> </tr> <tr> <td>All testbeds</td> <td>20</td> <td>52</td> </tr> <tr> <td>Single LAN</td> <td>40</td> <td>6</td> </tr> <tr> <td>Multiple LANs</td> <td>40</td> <td>4</td> </tr> <tr> <td>Multiple LANs</td> <td>40</td> <td>4</td> </tr> <tr> <td>All testbeds</td> <td>40</td> <td>10</td> </tr> <tr> <td>Single LAN</td> <td>48</td> <td>6</td> </tr> <tr> <td>All testbeds</td> <td>48</td> <td>6</td> </tr> <tr> <td>Single LAN</td> <td>49</td> <td>11</td> </tr> <tr> <td>All testbeds</td> <td>49</td> <td>11</td> </tr> <tr> <td>Single LAN</td> <td>50</td> <td>2</td> </tr> <tr> <td>All testbeds</td> <td>50</td> <td>2</td> </tr> <tr> <td>Single LAN</td> <td>51</td> <td>14</td> </tr> <tr> <td>All testbeds</td> <td>51</td> <td>14</td> </tr> </tbody> </table> <p>&nbsp;</p> <p>All results together:</p> <table> <tbody> <tr> <th>File size</th> <th>Runs</th> </tr> <tr> <td>32</td> <td>104</td> </tr> <tr> <td>128</td> <td>104</td> </tr> <tr> <td>512</td> <td>104</td> </tr> <tr> <td>2048</td> <td>104</td> </tr> <tr> <td>All</td> <td>416</td> </tr> </tbody> </table> <p>All results together, immediate, size 2048:</p> <table> <tbody> <tr> <th>Update</th> <th>Runs</th> </tr> <tr> <td>multicast</td> <td>104</td> </tr> <tr> <td>scp</td> <td>104</td> </tr> <tr> <td>tcp</td> <td>104</td> </tr> <tr> <td>udp</td> <td>104</td> </tr> </tbody> </table> <p>Router results for selected multicast runs and routers</p> <table> <tbody> <tr> <th>Testbed</th> <th>Run</th> </tr> <tr> <td>S1R3L10H</td> <td>20220309222056</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310074238</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310172944</td> </tr> <tr> <td>S1R3L10H</td> <td>20220311033431</td> </tr> <tr> <td>S1R3L5B</td> <td>20220128192622</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129114604</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129215606</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130060831</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130144549</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130224025</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131063956</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131164414</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310001954</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310093547</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310193037</td> </tr> <tr> <td>S1R3L10H</td> <td>20220311052100</td> </tr> <tr> <td>S1R3L5B</td> <td>20220128210828</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129132315</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129234328</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130075126</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130162044</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131002021</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131083908</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131181549</td> </tr> </tbody> </table> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Mar 2022View details →
zenodo48/100

Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering

<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso&rsquo;s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE!&nbsp;The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier&#39;s journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2017View details →
zenodo48/100

FAOSTAT AgLU data Archive for gcamfaostat v1.0.1 (Download Oct 16 2024)

<p>This repository contains data files needed to run the <em>gcamfaostat (v1.0.1)</em> package. All the data are publicly available from FAOSTAT and they are downloaded from&nbsp;<a href="https://www.fao.org/faostat/en/#data">FAOSTAT</a> in October 2024. This repo serves as an archive of the source data as FAOSTAT continues updating the data. Note that the zip files provide a snapshot of FAOSTAT data since the historical data may also be revised by FAO.&nbsp;</p> <p>These data&nbsp;should be placed in<em> inst/extdata/FAOSTAT</em> in the R package <em>gcamfaostat v1.0.1</em>. They are used in the package to generate data used in the <em>aglu/FAO</em>&nbsp;folder in <em>gcamdata</em> for GCAM. The package structure ensures the processing is transparent, traceable, and reproducible. <em>gcamfaostat v1.0.1 generates data for GCAM v7.3+.</em></p> <p>&nbsp;</p> <p><strong><em>We have now included more data from FAOSTAT (beyond gcamfaostat needs) and changed the archive version by date. E.g., version 2024.10.16 is downloaded around that date.</em></strong></p> <p><em>Note that gcamfaostat v1.0.0 used a version of FAOSTAT data downloaded in Fall 2022, which produced data in GCAM v7.0.&nbsp;</em></p> <p>&nbsp;</p>

opencc-by-sa-4.0Oct 2024View details →
edi48/100

Plot-level field data and model simulation results, archived to accompany Turner et al. manuscript; reports data from summer 2017 sampling of short-interval fires that burned during summer 2016 in Greater Yellowstone.

Subalpine forests in the northern Rocky Mountains have been resilient to stand-replacing fires that historically burned at 100–300-yr intervals. Fire intervals are projected to decline drastically as climate warms, and forests that reburn before recovering from previous fire may lose their ability to rebound. We studied recent fires in Greater Yellowstone (Wyoming, USA) and asked whether short-interval (less than 30 yrs) stand-replacing fires can erode lodgepole pine (Pinus contorta var. latifolia) forest resilience via increased burn severity, reduced early postfire tree regeneration, reduced carbon stocks, and slower carbon recovery. During 2016, fires reburned young lodgepole pine forests that regenerated after wildfires in 1988 and 2000. During 2017, we sampled 0.25-ha plots in stand-replacing reburns (n=18) and nearby young forests that did not reburn (n=9). We also simulated stand development with and without reburns to assess carbon recovery trajectories. Nearly all prefire biomass was combusted ("crown fire plus") in some reburns in which prefire trees were dense and small (≤ 4 cm basal diameter). Postfire tree seedling density was reduced six-fold relative to the previous (long-interval) fire, and high-density stands (greater than 40,000 stems ha-1) were converted to sparse stands (less than 1,000 stems ha-1). In reburns, coarse wood biomass and aboveground carbon stocks were reduced by 65% and 62%, respectively, relative to areas that did not reburn. Increased carbon loss plus sparse tree regeneration delayed simulated carbon recovery by greater than 150 yrs. Forests did not transition to nonforest, but extreme burn severity and reduced tree recovery foreshadow an erosion of forest resilience.

openCC (other)Apr 2019View details →
edi48/100

California Harmful Algal Bloom Monitoring and Alert Program Data (Darwin Core Archive format)

The Harmful Algal Bloom Monitoring and Alert Program (HABMAP) was formed in 2008 and provides updates on current algal blooms and facilitates information exchange among scientists, federal and state managers, and the general public in California. A major component of this program is regional HAB monitoring with support from the Southern California Coastal Ocean Observing System (SCCOOS) and the Central and Northern California Ocean Observing System (CeNCOOS). Water samples and net tows are collected once per week at piers to monitor for HAB species, the neurotoxin domoic acid, and water quality data including temperature, chlorophyll-a, and nutrients. The sampling stations represented in this dataset include Santa Cruz Wharf, Monterey Wharf, Cal Poly Pier, Stearns Wharf, Santa Monica Pier, Newport Beach Pier, and Scripps Pier. These data are consolidated and reformatted into Darwin Core Archive (DwC-A) format from the level 1 site-specific datasets hosted on the SCCOOS ERDDAP server: (https://erddap.sccoos.org/erddap/tabledap/index.html?page=1).

openCC (other)Jun 2022View details →
zenodo44/100

Data archive: CICT for single cell RNA-seq network inference

<p>This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions" (Shojaee and Huang, Brief in Bioinform 2023. DOI: 10.1093/bib/bbad370). The CICT code is available at the GitHub repo (https://github.com/hlab1/scRNAseqWithCICT/).</p><p>The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021), SCENIC (Aibar et al., Nat Methods 2017), Inferelator 3.0 (Gibbs et al., Bioinformatics 2022), and CellOracle (Kamimoto et al., Nature 2023). The output directory names are (subdirectories within each dataset):</p><p>* CICT_ewMIshrink_RFmaxdepth10_RFntrees20/: CICT for simulated data<br>* CICT_v2/: CICT for experimental data<br>* CELLORACLEDB/: CellOracle for experimental data<br>* DEEPDRIM72_ewMIshrink_RFmaxdepth10_RFntrees20/: DEEPDRIM for simulated data<br>* DEEPDRIM72_v2/: DEEPDRIM for experimental data<br>* INFERELATOR38_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-Prior for simulated data<br>* INFERELATOR38_v2/: Inferelator-Prior for experimental data<br>* INFERELATOR34_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-NoPrior for experimental data<br>* INFERELATOR34_v2/: Inferelator-NoPrior for experimental data<br>* GENIE3/: GENIE3<br>* GRNBOOST2/: GRNBOST2<br>* LEAP/: LEAP<br>* PIDC/: PIDC<br>* PPCOR/: PPCOR<br>* SCENICDB/: SCENIC for experimental data<br>* SCNS/: SCNS<br>* SCODE/: SCODE<br>* SCRIBE/: SCRIBE<br>* SINCERITIES/: SINCERITIES<br>* SINGE/: SINGE<br>* RANDOM/: RANDOM</p><p>The methods were benchmarked against two kinds of scRNA-seq datasets:<br>* Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts with shape parameter k=6.5 and rate parameter q=10, 30, 50, 70, 80.&nbsp;<br>* Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, with three types of ground truth networks.<br>&nbsp; &nbsp; * Evaluation levels:<br>&nbsp;&nbsp; &nbsp; &nbsp; &nbsp;* L0: 500 highly varying genes plus TFs<br>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;* L1: 1000 highly varying genes plus TFs<br>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;* L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1.<br>&nbsp; &nbsp; * Types of ground truths:<br>&nbsp;&nbsp; &nbsp; &nbsp; &nbsp;* Cell-type-specific ChIP-seq ground truth (L0, L1, L2)<br>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;* Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns)<br>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;* Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof)</p><p>The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For complete details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).</p><p>&nbsp;</p>

opencc-by-nc-sa-4.0Jun 2023View details →
zenodo44/100

Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"

<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option)&nbsp;<br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option)&nbsp;<br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option)&nbsp;</p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>

opencc-by-4.0Nov 2023View details →
zenodo44/100

UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph hash://md5/10663911550bb52a0f5741993f82db9d hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c

<p>A biodiversity dataset graph: UCSB-IZC</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.</p> <p>This dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] between 2021-10-08 and 2021-11-04 using [preston track &quot;https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0&quot;].</p> <p>This archive contains 14349 images related to 32533 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].</p> <p>The images were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> &nbsp;| grep -o -P &quot;.*depict&quot;\<br> &nbsp;| sort\<br> &nbsp;| uniq\<br> &nbsp;| wc -l</p> <p>And the occurrences were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> &nbsp;| grep -o -P &quot;occurrence/([0-9])+&quot;\<br> &nbsp;| sort\<br> &nbsp;| uniq\<br> &nbsp;| wc -l</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.</p> <p>To retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a &quot;data&quot; folder. Alternatively, you can use the Preston [2,3] command-line tool to &quot;clone&quot; this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files,https://zenodo.org/record/5660088/files/</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> &lt;urn:uuid:0659a54f-b713-4f86-a917-5be166a14110&gt; &lt;http://purl.org/pav/hasVersion&gt; &lt;hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36&gt; .<br> &lt;hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c&gt; &lt;http://purl.org/pav/previousVersion&gt; &lt;hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36&gt; .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command &quot;preston verify&quot; produces lines as shown below, with each line including &quot;CONTENT_PRESENT_VALID_HASH&quot;. Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c&nbsp;&nbsp;&nbsp; file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c&nbsp;&nbsp;&nbsp; OK&nbsp;&nbsp;&nbsp; CONTENT_PRESENT_VALID_HASH&nbsp;&nbsp;&nbsp; 66438&nbsp;&nbsp;&nbsp; hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c<br> hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844&nbsp;&nbsp;&nbsp; file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844&nbsp;&nbsp;&nbsp; OK&nbsp;&nbsp;&nbsp; CONTENT_PRESENT_VALID_HASH&nbsp;&nbsp;&nbsp; 4093&nbsp;&nbsp;&nbsp; hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844<br> hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef&nbsp;&nbsp;&nbsp; file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef&nbsp;&nbsp;&nbsp; OK&nbsp;&nbsp;&nbsp; CONTENT_PRESENT_VALID_HASH&nbsp;&nbsp;&nbsp; 5746&nbsp;&nbsp;&nbsp; hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef<br> hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b&nbsp;&nbsp;&nbsp; file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b&nbsp;&nbsp;&nbsp; OK&nbsp;&nbsp;&nbsp; CONTENT_PRESENT_VALID_HASH&nbsp;&nbsp;&nbsp; 6147&nbsp;&nbsp;&nbsp; hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b</p> <p>Note that a copy of the java program &quot;preston&quot;, preston.jar, is included in this publication. The program runs on java 8+ virtual machine using &quot;java -jar preston.jar&quot;, or in short &quot;preston&quot;.</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston [2,3] v0.3.1. --<br> preston.jar</p> <p>-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a</p> <p>-- example image and meta-data --<br> sample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)<br> sample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .<br> [3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132<br> [4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c</p>

opencc-zeroNov 2021View details →
zenodo44/100

Words are monuments data-only archive

<p>These are the data that go with the paper <a href="https://besjournals.onlinelibrary.wiley.com/doi/full/10.1002/pan3.10302">&quot;Words are monuments: Patterns in US national park place names perpetuate settler colonial mythologies including white supremacy&quot;</a>&nbsp;(available 6 April 2022) by the above authors.&nbsp;Our code (and these data) are available at&nbsp;<a href="https://doi.org/10.5281/zenodo.5712009">https://doi.org/10.5281/zenodo.5712009</a>. This archive is here for folks who just want the data. This is the&nbsp;<strong>full dataset with name explanations</strong> and references for the explanations, traditional Indigenous place names for settler colonial place names (where&nbsp;available),&nbsp;additional categories for sorting, and more.&nbsp;</p> <p>Note: .csv files can be opened in Excel and Google Sheets.</p> <p>column info:</p> <p>A: Unique ID per row</p> <p>B: National Park place name is in</p> <p>C: Place name according to NPS visitor map</p> <p>D: Feature type (mountain, island, picnic area, etc.)</p> <p>E: Name type (person, non-human animal, plant, etc.)</p> <p>F: Natural or human constructed (human constructed includes so-called &quot;ruins&quot;, as well as visitor centers, etc.)</p> <p>G: Is the word from an Indigenous or western language?</p> <p>H: Is it a traditional Indigenous place name?</p> <p>I: If it is Indigenous is it the name of an Indigenous person or people?</p> <p>J: Is it a translation of a traditional Indigenous PN?</p> <p>K: Word meaning class (similar to column E)</p> <p>L: Erasure -- see paper for definitions of each class below and decision trees</p> <ul> <li>Yes</li> <li>potentially</li> <li>no information</li> <li>not erasure - evidence it is a traditional Indigenous PN or settler built with western PN</li> <li>translation of traditional IPN)</li> </ul> <p>M: Dimensions of racism and colonialism&nbsp;-- see paper for definitions of each class below and decision trees</p> <ul> <li>No (traditional Indigenous PN, western built with western PN, or erasure as only problem)</li> <li>Name itself promotes racist ideas and/or violence against a group</li> <li>Named after person who supported racist ideas (but not physically violent)</li> <li>Named for a person who directly or use their power to indirectly perpetrate&nbsp;violence against a racial group</li> <li>Western use of Indigenous name (Appropriation)</li> <li>Other - truly does not fit any other classes</li> <li>No info - cannot find explanation</li> <li>Colonialism - memorializes colonialism</li> <li>Relevant western use of Indigenous name (i.e., appropriation of traditional name)</li> </ul> <p>N: Derogatory</p> <ul> <li>Yes</li> <li>Potentially</li> <li>No info</li> </ul> <p>O: explanation of name found in research</p> <p>P: Link to resources explaining name (for full citation for books cited, e.g., &quot;name year&quot; entries, see Table S1 in the paper).</p> <p>Q: Link to resources explaining name (if second link or source available)</p> <p>R: Indigenous name found in research</p> <p>v1.0.0 did not include columns O-R by mistake; corrected in this version update.</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

Data archive for Anaerobic methane oxidation in a coastal oxygen minimum zone: spatial and temporal dynamics

<p>Data collected during annual sampling campaigns to the coastal oxygen minimum zone of Golfo Dulce, carried out in January-February 2018, 2019 and 2020. Methods and results are presented and discussed in Steinsd&oacute;ttir et al. 2022.&nbsp;Anaerobic methane oxidation in a coastal oxygen minimum zone: spatial and temporal dynamics. Environmental Microbiology, in press, doi: 10.1111/1462-2920.16003</p> <p>The content of files is as follows:</p> <p>nutrient_and_methane_concentrations.csv - Concentrations of methane, nitrite, nitrate, and ammonium.</p> <p>methane_oxidation_rates.csv - Rates of anaerobic methane oxidation.</p> <p>kinetics_of_anaerobic_methane_oxidation.csv&nbsp;- Kinetics of anaerobic methane oxidation, carried out in 2019.</p> <p>methylococcales.fa&nbsp;- Methylococcales 16S rRNA amplicon sequences</p> <p>methanofastidiosa.fa&nbsp;- Methanofastidiosa 16S rRNA amplicon sequences</p>

opencc-by-4.0Apr 2022View details →
zenodo44/100

Pop-up satellite archival tagging data of Atlantic bluefin tuna in the Gulf of Lions, Northwestern Mediterranean Sea

<p>24 Atlantic bluefin tuna (Thunnus thynnus) individuals (117&ndash;158 cm fork length) were tagged with pop-up archival tags in the Gulf of Lion, NW-Mediterranean Sea between 2015 and 2016.</p> <p><strong>Tag programming and data</strong></p> <p>The tags applied (miniPATs by Wildlife Computers, https://wildlifecomputers.com) can record depth and temperature time series (denoted hereafter as DepthTS and TempTS, respectively) at a temporal resolution of 3&ndash;5 s (depending on the predefined deployment duration) and a vertical resolution of 0.5 m. Based on these data, the tag calculates and stores additional data products such as PAT-style Depth&ndash;Temperature profiles (PDT), time at depth data, and time at temperature. After pop-up, the tags transmit user-defined data products and subsets from the recorded data sets. All our tags were configured to transmit the following data products: daily light curves, DepthTS, and PDT. In order to maximize data coverage of the transmitted datasets, we decreased the temporal resolution of the DepthTS and PDT data after the first tagging campaign in 2015 from 150 to 600 s and 6 to 24 h, respectively. For both years, deployment durations were set to 150 and 90 d during spring (April&ndash;May) and summer (August&ndash;September), respectively. A description of the electronic tagging procedure can be found in <a href="https://doi.org/10.1093/icesjms/fsaa083">Bauer et al. (2020)</a>.</p> <p>Seven tags were physically recovered, providing the complete archived time series data at a resolution of 3&ndash;5 s. Nineteen tags provided more than 7 d of complete DepthTS data (i.e. without transmission gaps). Three tags from 2016 had deployment durations of &lt;1 week (#15P0983, #15P0985, and #15P0986) because of hardware failure.</p> <p>Provided files contain raw tag data (transmitted and recovered datasets) from the Wildlife Computers Data Portal as well as related GPE3 model runs (geolocation estimates).</p> <p>We thank the crews of the Cyngali and Roussillon Fishing recreational fishing vessels for their cooperation during the tagging cruises. This tagging study was part of the BLUEMED project and funded by the French National Research Agency (ANR; Project-ID ANR-14-ACHN-0002).</p> <p>&nbsp;</p>

opencc-by-4.0Apr 2022View details →
zenodo44/100

Supplementary data for manuscript titled: "Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction"

<p>Supplementary data for manuscript titled: &quot;Radiolitid Rudists: An Underestimated Archive for Cretaceous Climate Reconstruction&quot;</p> <p>Containing raw stable isotope and trace element data used in the study</p>

opencc-by-4.0Jun 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record