Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Paleo-CO2 Data Archive

These data were compiled from published paleo-CO2 data that have been assembled by an international group of proxy experts supported through an NSF-funded Research Coordination Network. It brings together paleo-CO2 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.  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>).

opencc-by-4.0Apr 2023View details →

Spacekit Data Archive

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

opencc-by-sa-4.0Nov 2023View details →

Energy System Time Series Suite (ESTSS) - Data Archive

<h2>Energy System Time Series Suite - Data Archive</h2>   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. For detailed information, please refer to the corresponding GitHub project: <a href="https://github.com/s-guenther/estss/">https://github.com/s-guenther/estss/</a> For associated research, see <a href="https://doi.org/10.1186/s42162-024-00304-8">https://doi.org/10.1186/s42162-024-00304-8</a> Data is provided in .csv format. The GitHub project includes a Python function to load this data as a dictionary of pandas data frames. 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.   <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> Each `ts_*.csv`, `features_*.csv`, `norm_space_*.csv`, and `info_*.csv` file comes in four versions to accommodate various set sizes: <ul> <li>`*_4096.csv`</li> <li>`*_1024.csv`</li> <li>`*_256.csv`</li> <li>`*_64.csv`</li> </ul> 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.   <h3>Contact</h3> ESTSS - Energy System Time Series Suite Copyright (C) 2023 Sebastian Günther sebastian.guenther@ifes.uni-hannover.de Leibniz Universität Hannover Institut für Elektrische Energiesysteme Fachgebiet für Elektrische Energiespeichersysteme Leibniz University Hannover Institute of Electric Power Systems Electric Energy Storage Systems Section <a href="https://www.ifes.uni-hannover.de/ees.html">https://www.ifes.uni-hannover.de/ees.html</a>

opencc-by-4.0Nov 2024View details →

Coptic Monastic Heritage Archive (CMHA): Photographic Dataset of Monastic Settlement in Wadi Naqqat, Egypt

This dataset was created as part of the Coptic Monastic Heritage Archive (CMHA) at the University of Ljubljana. It includes data and photographs of 14 monastic heritage sites in Wadi Naqqat (Eastern Desert, Egypt). Wadi Naqqat is located approximately 35 miles (55 km) west of the Red Sea town of Hurghada, in the broader region of ancient Mons Porphyrites, and dates to between the 4th and 6th centuries AD. The data was gathered through ground assessments conducted between 2018 and 2019 as part of the project “Endangered Hermitages: Documenting Coptic Monastic Heritage in Middle Egypt and the Eastern Desert”, led by Dr. Jan Ciglenečki (University of Ljubljana) and funded by the Antiquities Endowment Fund (AEF) of the American Research Center in Egypt (ARCE). Photographic documentation of the site was carried out by photographer Matjaž Kačičnik, using a 36-megapixel Nikon D810 digital camera, and by Dr. Jan Ciglenečki, using a 21-megapixel Canon EOS 5D Mark II digital camera. In 2024, this documentation was systematically integrated into the CMHA at the University of Ljubljana. The photographs include EXIF (GPS location, technical specs) and IPTC-IIM (e.g., Credits, Caption) and XMP metadata.

opencc-by-4.0Aug 2021View details →

SuperWASP Variable Star Photometry Archive (VeSPA)

This data set contains the metadata for periodic variable stars that have been classified by Citizen Scientists using the <a href="https://www.zooniverse.org/projects/ajnorton/superwasp-variable-stars">SuperWASP Variable Stars Zooniverse project</a>. The data set is in the same format as custom data exports generated via the <a href="https://www.superwasp.org/vespa/">superwasp.org</a> website. It consists of three files: <ul> <li>export.csv: The main data export in CSV format, containing one row per folded light curve (i.e. multiple rows per source object).</li> <li>fields.yaml: A YAML-format list of the columns included in the CSV export with an English description of each one.</li> <li>params.yaml: A YAML-format copy of the search and filtering parameters which were used to generate the export (in this case this is the full data set with no filtering applied). Also includes a data version number which will be incremented with future data releases or changes to the export format.</li> </ul> Photometry data is also available for download in FITS and JSON format, but this is not included here. URLs for the photometry files are included in export.csv for ease of downloading. Acknowledgements The SuperWASP project is currently funded and operated by Warwick University and Keele University, and was originally set up by Queen’s University Belfast, the Universities of Keele, St. Andrews and Leicester, the Open University, the Isaac Newton Group, the Instituto de Astrofisica de Canarias, the South African Astronomical Observatory and by STFC. The Zooniverse project on SuperWASP Variable Stars is led by Andrew Norton (The Open University) and builds on work he has done with his former postgraduate students Les Thomas, Stan Payne, Marcus Lohr, Paul Greer, and Heidi Thiemann, and current postgraduate student Adam McMaster. The Zooniverse project on SuperWASP Variable Stars was developed with the help of the ASTERICS Horizon2020 project. ASTERICS is supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement n.653477 VeSPA was designed and developed by Adam McMaster as part of his postgraduate work. This work is funded by STFC, DISCnet, and the Open University Space SRA. Server infrastructure was funded by the Open University Space SRA.

opencc-by-4.0Oct 2021View details →

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

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

opencc-by-4.0Mar 2022View details →

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

The librecast project states that ``Multicast is, by definition, the most efficient way for multiple nodes to communicate''.  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. The data set covers the experimental runs on the Virtual Wall 1 at IMEC as part of the Fed4Fire+ "SME and NGI Cascaded Experiments" https://www.fed4fire.eu/demo-stories/cc/librecasttesting/ This directory contains raw experiment results as produced by the "run-experiment" script. File names containing ".test." are experiment runs using code changes which we decided not to keep, and are excluded from processing and summarising. File names containing ".partial." 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. Summaries: Results collated by testbed: <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>   Results collated by number of clients: <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>   All results together: <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> All results together, immediate, size 2048: <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> Router results for selected multicast runs and routers <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>

opencc-by-4.0May 2022View details →

MOdern River archivEs of Particulate Organic Carbon: MOREPOC

Modern River Archives of Particulate Organic Carbon (MOREPOC) version 1.1 is a new, open-access, georeferenced, global database, featuring data on POC in suspended particulate matter (SPM) collected at 233 locations across 121 major river systems. This database includes 3,546 SPM data entries, among which 3,053 with POC content, 3,402 with stable carbon isotope (δ13C) values, 2,283 with radiocarbon activity (Δ14C) values, 1,936 with total nitrogen content, and 299 with aluminum-to-silicon mass ratios (Al/Si). The MOREPOC database aims at being used by the Earth System community to build comprehensive and quantitative models for the mobilization, alteration, and fate of terrestrial POC. The supply of particulate organic carbon (POC) associated with terrigenous solids transported to the ocean by rivers plays a significant role in the global carbon cycle. To advance our understanding of the source, transport, and fate of fluvial POC from regional to global scales, databases of riverine POC are needed, including elemental and isotope composition data from contrasted river basins in terms of geomorphology, lithology, climate, and anthropogenic pressure. MOREPOC will benefit the scientific community carrying out research on riverine POC sources, transport, and fate, furthermore, helping inform and validate Earth system models to improve the ability to model and understand the global carbon cycle. Existing environmental raster global datasets for climate, geomorphology, lithology, tectonics, hydrology, and land use, also offer promising prospects for the use of MOREPOC for identifying the controls on POC fluxes and composition, in particular using advanced statistical analysis or machine learning techniques. Moreover, MOREPOC enables a better understanding of sources, transport, and fate of fluvial POC combined with some existing ocean sediment databases. Future updates of MOREPOC should include new bulk POC parameters as well as data on molecular fractions, thermal labile fractions, or specific components such as black carbon or fossil carbon, which should, in turn, provide additional insight into the alteration of riverine POC from source to sink, an essential feature of the global carbon cycle. Data description The MOREPOC database consists of two parts: 1) the master metadata (MOREPOC_v1.1); 2) the summarization of references and methods (MOREPOC_v1.1_RM). A Readme is provided to better understand all parameters provided in the MOREPOC v1.1 database.  MOREPOC_v1.1 includes one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv), and GIS shapefile (compiled in .rar) using WGS84 coordinate system. <ul> <li>MOREPOC_v1.1.xlsx</li> <li>MOREPOC_v1.1.csv</li> <li>MOREPOC_v1.1.rar (GIS shapefile)</li> </ul> MOREPOC_v1.1_RM only provides one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv). <ul> <li>MOREPOC_v1.1_RM.xlsx</li> <li>MOREPOC_v1.1_RM.csv</li> </ul> The database structure of MOREPOC is listed in Table.1 to understand all provided parameters, more information can be found in the companion manuscript. <table> <caption>Table. 1 Description of the parameters of the MOREPOC v1.1 database.</caption> <tbody> <tr> <td>Parameter</td> <td>Description</td> <td>MOREPOC column name</td> </tr> <tr> <td>River name</td> <td>Name of the major river basin</td> <td>bas_id</td> </tr> <tr> <td>Sub river name</td> <td>Name of the sampled river/stream</td> <td>riv_id</td> </tr> <tr> <td>Country</td> <td>Name of country or places</td> <td>country</td> </tr> <tr> <td>Continent</td> <td>Name of the continent</td> <td>cont</td> </tr> <tr> <td>Sampling site/code</td> <td>Expedition sampling ID</td> <td>code</td> </tr> <tr> <td>Sampling date</td> <td>Time (month/day/year) when the SPM sample was collected</td> <td>time_m/d/y</td> </tr> <tr> <td>Latitude</td> <td>Decimal latitude using WGS 1984</td> <td>lat</td> </tr> <tr> <td>Longitude</td> <td>Decimal longitude using WGS 1984</td> <td>lon</td> </tr> <tr> <td>Sampling technique</td> <td>Method of SPM sampling</td> <td>type_spm</td> </tr> <tr> <td>Size fraction of SPM</td> <td>Reported size fractions analyzed</td> <td>fra_spm</td> </tr> <tr> <td>SPM concentration (mg/L)</td> <td>The total dry weight of SPM in mg per liter water column</td> <td>conc_spm</td> </tr> <tr> <td>POC concentration (mg/L)</td> <td>The total dry weight of POC in mg per liter water column</td> <td>conc_poc</td> </tr> <tr> <td>POC content (%)</td> <td>The total POC content of SPM in wt %</td> <td>per_poc</td> </tr> <tr> <td>POC content uncertainty (1σ)</td> <td>The analytical uncertainty for POC content (1σ)</td> <td>perc_poc_1sd</td> </tr> <tr> <td>δ13C (&permil;)</td> <td>δ13C values of POC (carbonate removed) in &permil;</td> <td>d13C_poc</td> </tr> <tr> <td>δ13C uncertainty (1σ)</td> <td>The analytical uncertainty for δ13C of POC</td> <td>d13C_1sd</td> </tr> <tr> <td>Δ14C (&permil;)</td> <td>Δ14C values of POC (carbonate removed) in &permil;</td> <td>D14C_poc</td> </tr> <tr> <td>Δ14C uncertainty (1σ)</td> <td>The analytical uncertainty for Δ14C of POC</td> <td>D14C_1sd</td> </tr> <tr> <td>Fraction modern (Fm)</td> <td>Fraction modern of POC</td> <td>F14C</td> </tr> <tr> <td>Radiocarbon ages (year)</td> <td>Radiocarbon ages before present (1950)</td> <td>age_14C</td> </tr> <tr> <td>TN content (%)</td> <td>The total nitrogen content of SPM in wt %</td> <td>perc_tn</td> </tr> <tr> <td>Corg/N mass ratio</td> <td>Mass ratio of POC to TN in SPM</td> <td>cn_ratio</td> </tr> <tr> <td>Al/Si mass ratio</td> <td>Mass ratio of Al to Si in SPM</td> <td>alsi_ratio</td> </tr> <tr> <td>Reference</td> <td>Full list of citations of the data source</td> <td>ref</td> </tr> <tr> <td>Complete reference</td> <td>Complete information for cited references</td> <td>ref_c</td> </tr> <tr> <td>Measured parameters</td> <td>Summarization of elemental and isotopic carbon parameters measured</td> <td>para_m</td> </tr> <tr> <td>Calculated parameters</td> <td>Summarization of elemental and isotopic carbon parameters calculated</td> <td>para_c</td> </tr> <tr> <td>Filter</td> <td>Filter used to obtain SPM</td> <td>filter</td> </tr> <tr> <td>Acid</td> <td>The acid type used to remove carbonate in SPM</td> <td>acid</td> </tr> <tr> <td>Carbonate removal method</td> <td>The method used to remove carbonate in SPM</td> <td>m_acid</td> </tr> <tr> <td>Acid concentration</td> <td>The concentration of adopted acid to remove carbonate in SPM</td> <td>conc_acid</td> </tr> <tr> <td>carbonate removal temperature</td> <td>The environmental temperature for acid to remove carbonate in SPM</td> <td>temp_acid</td> </tr> <tr> <td>Carbonate removal duration</td> <td>The reaction time used for acid to remove carbonate in SPM</td> <td>time_acid</td> </tr> <tr> <td>Note</td> <td>Additional information for carbonate removal process</td> <td>note</td> </tr> </tbody> </table> Contributing Data Please contact Yutian Ke at <a href="mailto:yutianke@caltech.edu">yutianke@caltech.edu</a> or  <a href="mailto:yutian.ke@universite-paris-saclay.fr">yutian.ke@universite-paris-saclay.fr</a> if you are interested in contributing your published or unpublished data to MOREPOC. Citation Ke, Y. T., Calmels, D., Bouchez, J., Cécile, Q.: MOdern River archivEs of Particulate Organic Carbon: MOREPOC, Dataset version 1.1, Zenodo [dataset], <a href="https://doi.org/10.5281/zenodo.6541925">https://doi.org/10.5281/zenodo.7055970</a>.

opencc-by-4.0Dec 2017View details →

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

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’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data. NOTE! 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's journals are included within this datasheet. Data is in CSV. format

opencc-by-sa-4.0Jun 2018View details →

Example files to A FAIR archive based on the CERIF model

An archival structure based on the CERIF model is proposed. The archive tree is represented by cfProjects and the archived objects by cfResult* entities with their descriptive metadata given in attached CERIF entities. Archival preservation metadata is stored in the Premis format inside attached cfMeasurment entities. An example in which EPrints repository items are transferred to the archive is presented. When CERIF is employed in relevant archive processes, a FAIR compliant archive is easier to achieve.

opencc-zeroJul 2019View details →

Prosopographical Database of Judeans in the Murašû Archive

This is a prosopographical database of Judean persons attested in the Mura&scaron;û archive. It relates to chapter 5 in Tero Alstola, Judeans in Babylonia: A Study of Deportees in the Sixth and Fifth Centuries BCE (Culture and History of the Ancient Near East. Leiden: Brill). For further information, see the readme file.

opencc-by-sa-4.0Oct 2024View details →

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

This repository contains data files needed to run the gcamfaostat (v1.0.1) package. All the data are publicly available from FAOSTAT and they are downloaded from <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.  These data should be placed in inst/extdata/FAOSTAT in the R package gcamfaostat v1.0.1. They are used in the package to generate data used in the aglu/FAO folder in gcamdata for GCAM. The package structure ensures the processing is transparent, traceable, and reproducible. gcamfaostat v1.0.1 generates data for GCAM v7.3+.   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. Note that gcamfaostat v1.0.0 used a version of FAOSTAT data downloaded in Fall 2022, which produced data in GCAM v7.0.

openCC (other)Oct 2021View details →

SBC LTER: Reef: Annual time series of biomass for kelp forest species, ongoing since 2000 (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/281/3, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sbc/50/10. The abstract below was extracted from the Level 0 data package and is included for context: These data are annual estimates of biomass of approximately 225 taxa of reef algae, invertebrates and fish in permanent transects at 11 kelp forest sites in the Santa Barbara Channel (2-8 transects per site). Abundance is measured annually (as percent cover or density, by size) and converted to biomass (i.e., wet mass, dry mass, decalcified dry mass, ash free dry mass) using published taxon-specific algorithms. Data collection began in summer 2000 and continues annually in summer to provide information on community structure, population dynamics and species change. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. See Methods for more information. The primary research objective of the Santa Barbara Coastal LTER is to investigate the importance of land and ocean processes in structuring giant kelp (Macrocystis pyrifera ) forest ecosystems. As in many temperate regions, the shallow rocky reefs in the Santa Barbara Channel, California, are dominated by giant kelp forests. Because of their close proximity to shore, kelp forests are influenced by physical and biological processes occurring on land as well as in the open ocean. SBC LTER research

openCC (other)Apr 2019View details →

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.

Darwin Core Archive: Santa Barbara Channel fish surveys at deep reefs: Footprint, Piggy Bank, Anacapa Passage

The dataset contains fish surveys from deep natural reefs in the northern Santa Barbara Channel Islands, Southern California, mainly at reefs named Piggy Bank, Footprint (local names) and Anacapa Passage. Data collection began in 1995. Reefs are located at depths between 30 and 360 m (100 and 1,180 feet). Sampling was by the manned submersibles Delta and DualDeepworker and an unmanned Remotely Operated Vehicle (ROV). These sites included a wide range of such habitats as banks, ridges, and carbonate reefs, ranging in size from a few kilometers in length to less than a hectare in area. On these features, we focused on hard bottom macrohabitats, including kelp beds, boulder and cobble fields, and bedrock outcrops. Transects were not deliberately revisited; some reefs were surveyed as many as four times per year. All transects are 2 m wide; transect length varied (see data). Fishes were identified to lowest possible taxon (usually species), and verified against the WoRMs database (http://www.marinespecies.org/). This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data.

Darwin Core Archive: Santa Barbara Channel fish surveys at shallow outcrops

Nine nearshore, shallow-water rock outcrops, seven along the mainland and two off Anacapa Island, were monitored annually from 1995 to 2000. All locations were surveyed using scuba, and belt transects was were conducted at each site. These natural outcrops are geographically distributed across the Santa Barbara Channel providing opportunities for spatial and temporal comparisons between natural reef and oil/gas platforms. Fish surveys at platforms were conducted using scuba within a few days of these surveys at rock outcrops; the data can be viewed at: <link xlink:href="https://portal.lternet.edu/nis/mapbrowse?scope=edi&identifier=113">https://portal.lternet.edu/nis/mapbrowse?scope=edi&identifier=113</link> This scuba project was conducted and reported under a cooperative agreement (Agreement 1445-CA09-95-0836) between the U. S. Geological Survey (Biological Resources Division) and the University of California, Santa Barbara. Citation of report: Love, M. S., D. M. Schroeder, and M. M. Nishimoto. 2003. The ecological role of oil and gas production platforms and natural outcrops on fishes in southern and central California: a synthesis of information. U. S. Department of the Interior, U. S. Geological Survey, Biological Resources Division, Seattle, Washington, 98104, OCS Study MMS 2003-032. <link xlink:href="http://www.lovelab.id.ucsb.edu/Report.pdf">http://www.lovelab.id.ucsb.edu/Report.pdf</link>

Darwin Core Archive: Santa Barbara Channel fish surveys at shallow regions of oil and gas platforms (SCUBA)

This dataset included fish counts that were surveyed in the shallow sections (0 – 40 meters) of the oil and gas platforms using Scuba. The oil and gas platforms are located in the Santa Barbara Channel, California, USA. Each of the eleven platforms (GILDA, GINA, HOLLY, IRENE, HERMOSA, HIDALGO, GAIL, GRACE, HARVEST, C, and HENRY) were surveyed multiple times a year from 1995 to 2000. This scuba project was conducted and reported under a cooperative agreement (Agreement 1445-CA09-95-0836) between the U. S. Geological Survey (Biological Resources Division) and the University of California, Santa Barbara. This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. Citation of report: Love, M. S., D. M. Schroeder, and M. M. Nishimoto. 2003. The ecological role of oil and gas production platforms and natural outcrops on fishes in southern and central California: a synthesis of information. U. S. Department of the Interior, U. S. Geological Survey, Biological Resources Division, Seattle, Washington, 98104, OCS Study MMS 2003-032. http://www.lovelab.id.ucsb.edu/Report.pdf These fish surveys at platforms were conducted within a few days of surveys at rock outcrops, which can be viewed at: https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=112 The deeper sections of the oil platform were surveyed using the research submarine Delta; the data can be viewed at: https://portal. edirepository.edu/nis/mapbrowse?scope=edi&identifier=111

Santa Barbara Channel Marine BON Darwin Core Archive: Nearshore kelp forest integrated benthic cover, 1980-ongoing

The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains cover of kelp forest sessile invertebrates, understory macroalgae, and substrate types by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. Divers collect data on using either uniform point contact (UPC) or random point contact (RPC) methods. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. Percent cover is recorded for taxa where individuals cannot be counted. Cover can be calculated from the data here as the fraction of total points at which the taxon was present x 100. With UPC and RPC methods, multiple species can be recorded at any given point. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. See Methods for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your