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FIGURE 2 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 2. (Continued).
tellingsounds/lama-data: LAMA Data - Capturing Entities (Persons, Topics, Music, etc.) in Austrian audio(-visual) archive material
<p>Data entered into LAMA (Linked Annotations for Media Analysis), a research software for capturing and visualizing the interaction of music and its contexts, developed by the Telling Sounds project.</p>
Archive of the microtremor data observed at rock/stiff-soil sites and the analysis results
<p>This archive includes the microtremor data observed at 15 rock/stiff-soil sites and the analysis results, which were fully described in a paper "Spatial autocorrelation method for a simple microtremor array survey at rock/stiff-soil sites" by Ikuo Cho (2023, Geophysical Journal International, in press).</p>
Model data archive for a model-data intercomparison of the Eocene-Oligocene transition
<p>This data package contains data used for an model-data intercomparison originally<br> published in:</p> <p>D. K. Hutchinson, H. K. Coxall, D. J. Lunt, M. Steinthorsdottir, A. M. de Boer, M. Baatsen, A. von der Heydt, M. Huber, A. T. Kennedy-Asser, L. Kunzmann, J.-B. Ladant, C. H. Lear, K. Moraweck, P. N. Pearson, E. Piga, M. J. Pound, U. Salzmann, H. D. Scher, W. P. Sijp, K. K. Śliwińska, P. A. Wilson, and Z. Zhang, 2021: <strong>The Eocene-Oligocene transition: a review of marine and terrestrial proxy data, models and model-data comparisons</strong>, Climate of the Past, 17, 269-315.<br> <a href="https://doi.org/10.5194/cp-17-269-2021">https://doi.org/10.5194/cp-17-269-2021</a></p> <p>These data are also used in a further model-data intercomparison of Antarctic temperatures:</p> <p>Emily Tibbett, Natalie J Burls, David K. Hutchinson, Sarah J Feakins, (2023), <strong>Proxy-Model Comparison for the Eocene-Oligocene Transition in Southern High Latitudes, Paleoceanography and Paleocliamtology</strong>, In Review. Pre-print avaiable from:<br> <a href="https://www.authorea.com/doi/full/10.1002/essoar.10511735.2">https://www.authorea.com/doi/full/10.1002/essoar.10511735.2</a></p> <p>The package contains surface air temperature and sea surface temperature from an ensemble of model simulations of the Eocene-Oligocene transition. These data are provided at annual and monthly frequency. They are also provided on the original model grid, and an interpolated common grid used for the intercomparison. (The common grid is based on the HadCM3BL model grid.) All data are provided in NETCDF format with self-describing variable names.</p> <p>The name and explanation of the interpolated data files are contained in:<br> <strong>table_of_experiments.xlsx</strong></p> <p>Please read that spreadsheet to interpret the filenames, and see <strong>Table 2 (p291)</strong> of Hutchinson et al (2021) for experiment descriptions.</p> <p>Please also be mindful to cite the original authors of the simulations when using these data, whose work made this dataset possible. The appropriate citations are listed below:</p> <p>Reference DOI link <br> <strong>Baatsen et al (2020)</strong> <a href="https://doi.org/10.5194/cp-16-2573-2020">https://doi.org/10.5194/cp-16-2573-2020 </a> <br> <strong>Goldner et al (2014)</strong> <a href="https://doi.org/10.1038/nature13597">https://doi.org/10.1038/nature13597</a> <br> <strong>Ladant et al (2014a,b)</strong> <a href="https://doi.org/10.5194/cp-10-1957-2014">https://doi.org/10.5194/cp-10-1957-2014</a> <a href="https://doi.org/10.1002/2013PA002593 ">https://doi.org/10.1002/2013PA002593 </a><br> <strong>Hutchinson et al (2018, 2019) </strong><a href="https://doi.org/10.5194/cp-14-789-2018">https://doi.org/10.5194/cp-14-789-2018</a> <a href="https://doi.org/10.1038/s41467-019-11828-z">https://doi.org/10.1038/s41467-019-11828-z</a> <br> <strong>Kennedy et al (2015)</strong> <a href="https://doi.org/10.1098/rsta.2014.0419">https://doi.org/10.1098/rsta.2014.0419</a> <br> <strong>Zhang et al (2012, 2014)</strong> <a href="https://doi.org/10.5194/gmd-5-523-2012">https://doi.org/10.5194/gmd-5-523-2012</a> <a href="https://doi.org/10.1038/nature13705">https://doi.org/10.1038/nature13705</a> <br> <strong>Sijp et al (2009)</strong> <a href="https://doi.org/10.1175/2009JCLI3003.1">https://doi.org/10.1175/2009JCLI3003.1</a></p>
Dataset related to article "Archival skin biopsy specimens as a tool for miRNA-based diagnosis: Technical and post-analytical considerations".
<p>microRNA raw data</p>
Dyall double-zeta, triple-zeta, and quadruple-zeta basis set archive files
<p>This archive contains the archive files of the Dyall basis sets for each basis set level (dz, tz, qz) for all blocks of the periodic table from 1s to 7p. Each file is a plain text file that contains a description of the basis set development and recommended use, the atomic orbitals for the reference configuration with the exponents, and lists of correlating, polarization, and diffuse functions. The exponents, in the Dirac program format, are given in a separate location, https://doi.org/10.5281/zenodo.7574628; the reference list is also given at this location.</p>
Image frameworks and archives
<p>Frischknecht, Max: Digital Participatory Knowledge Visualizations</p> <p>Digital Image Archives, Max Frischknecht investigates how visualizations and interfaces in digital cultural collections support participatory use. The research focuses on three aspects: The participation and involvement of researchers at universities and the general public in enhancing the collection through the interface, the use of visualizations in exploring and presenting themes in the collection, and the use of machine learning in communicating the cultural collection on a visual and content level. The results of the study will be incorporated into the development of a new participatory image archive for the Swiss Folklore Society.</p> <p> </p> <p> </p> <p>Julien Raemy: The International Image Interoperability Framework (IIIF) APIs as the backbone of scientific and participatory research</p> <p>Within the multidisciplinary project "Participatory Knowledge Practices in Analogue and Digital Image Archives" funded by the Swiss National Science Foundation (SNSF), we aim to design a visual interface with machine learning-based tools.</p> <p>This interoperable platform will make it easy to annotate, contextualise, organise and link both images and their metadata to deliberately encourage the participatory use of the archives from the Swiss Society for Folklore Studies (Schweizerische Gesellschaft für Volkskunde -SGV).</p> <p>One of the most important components that will support this effort is to leverage the APIs developed by the International Image Interoperability Framework (IIIF), not only to facilitate external exchange but also the interaction between the various in-house software packages that will be deployed.</p> <p>The presentation will be an opportunity to present the data and intertoperability model of the project, currently in the conceptualization phase.</p>
Smart City Tracker: A living archive of smart city prevalence
<p>An interactive platform where users can track and explore local governments' smart city implementation in the United States.</p>
The state of digital archaeological archiving policies and practice [questionnaire survey results]
<p>The digital transition in archaeology is often taken for granted, yet the process is far from complete. The topic of digital archiving has been addressed by both the EAC Working Group for Archaeological Archives and the SEADDA project. These two entities joined forces to produce a special issue of the Internet Archaeology journal, bringing together papers on digital archiving practices in over two dozen countries (Richards et al. 2021). The papers were later analysed by EAC and SEADDA to compare the international situation. The results reveal both shared difficulties associated with the issue of documentary archives worldwide and examples of good practices that help to overcome these problems.</p> <p>A questionnaire survey was also carried out to complement the findings resulting from the interpretation of the published articles with supporting data covering the whole European area in a balanced way. The survey allowed for the compilation of an overview of the situation in 27 countries (30 regions) of Europe. All respondents were experts involved in digital archiving and/or heritage data management in individual countries. Based on the collected information, the disproportion in value of archaeological data and their position within heritage management practice is already proving to be a major shortcoming. Presented dataset contains the questionnaire and the survey results, serving as complementary material to the published interpretation (Novák et al. <em>forthcoming</em>).</p> <p>References:</p> <p>Novák, D. - Oniszczuk, A. - Gumbert, B. <em>forthcoming</em>: Digital archaeological archiving policies and practice in Europe: the EAC call for action. Internet Archaeology 63. <a href="https://doi.org/10.11141/ia.63.7">https://doi.org/10.11141/ia.63.7</a></p> <p>Richards, J.D., Jakobsson, U., Novák, D., Štular, B. and Wright, H. 2021: Digital Archiving in Archaeology: The State of the Art. Introduction, Internet Archaeology 58. <a href="https://doi.org/10.11141/ia.58.23">https://doi.org/10.11141/ia.58.23</a></p>
Factors Affecting African Journals Visibility Data Archive
<p>The file contains the status of African journals indexing on Google Scholar and Scopus. The journals are hosted in a major journal repository in Africa(Sabinet journal repository) and a major journal platform for African journals (African Journal Online, AJOL). There are also factors affecting the journal indexing on Google Scholar and Scopus. </p> <p>The factors affecting journal visibility highlighted include journals' open access status, journals’ presence on the International Standard Serial Number (ISSN), journals’ publisher membership of the Committee on Publication Ethics (COPE), journals’ hosting on the International Network for Advancing Science and Policy (INASP) journal online, geographic location of the journals’ publisher. </p>
Bionomia: a versioned archive of associations between people and the biodiversity data records they worked on. hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2 hash://md5/a69075f7a9a19f069c6d0c6d8f312259
<p>Biodiversity data describe nature in digital form. Bionomia [1] helps to associate the people behind the creation of biodiversity data and the records they worked on. Bionomia is updated constantly, and this publication provides a versioned snapshots of Bionomia data products, such as:</p> <p>https://bionomia.net/data/bionomia-public-profiles.csv</p> <p>and </p> <p>https://bionomia.net/data/bionomia-public-claims.csv.gz</p> <p>The data downloads are versioned using Preston [2], a biodiversity data tracker, by running the following command:</p> <pre><code class="language-bash">preston track\ https://bionomia.net/data/bionomia-public-claims.csv.gz\ https://bionomia.net/data/bionomia-public-profiles.csv</code></pre> <p>The history of this publication is:</p> <pre><code><hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/22afc7a3e4e1c3bc289ce39573463331d3b594a11512c1233e39436973aea974> . <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/22afc7a3e4e1c3bc289ce39573463331d3b594a11512c1233e39436973aea974> .</code></pre> <p>as obtained via </p> <pre><code class="language-bash">preston history\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2</code></pre> <p>The aliases include are </p> <pre><code><https://bionomia.net/data/bionomia-public-claims.csv.gz> <http://purl.org/pav/hasVersion> <hash://sha256/9d345ffa98f2556aa77609fa3604e61efbd8e53c067153038a65c8b4d3705ac1> <urn:uuid:05efc676-b344-45f8-a109-de4df5b85fd2> . <https://bionomia.net/data/bionomia-public-profiles.csv> <http://purl.org/pav/hasVersion> <hash://sha256/80265c7f885a261396df909163ad8df6bc32246b55350a8bc65c20a05b30a04c> <urn:uuid:b3437df4-7dd5-44b3-a154-2e06320ade2a> . <https://bionomia.net/data/bionomia-public-claims.csv.gz> <http://purl.org/pav/hasVersion> <hash://sha256/cca558f470657a3c3fb99be70907d5705e7b5c20d12412073307fc9146e94394> <urn:uuid:fa6c6542-57bc-405a-b518-01c225f474b1> . <https://bionomia.net/data/bionomia-public-profiles.csv> <http://purl.org/pav/hasVersion> <hash://sha256/80265c7f885a261396df909163ad8df6bc32246b55350a8bc65c20a05b30a04c> <urn:uuid:36c4c1ff-3baf-4250-8c1f-fbfb2f33dfaf> .</code></pre> <p>as obtained via</p> <pre><code class="language-bash">preston alias\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2</code></pre> <p>The first 5 lines of the tracked content are:</p> <pre><code>Subject,Predicate,Object https://gbif.org/occurrence/1839364365,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804907,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804727,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804529,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611</code></pre> <p>as obtained via:</p> <pre><code class="language-bash">preston cat\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2\ https://bionomia.net/data/bionomia-public-claims.csv.gz\ | gunzip\ | head -n5 </code></pre> <p> </p> <p>and </p> <pre><code>Family,Given,Particle,OtherNames,Country,Keywords,wikidata,ORCID,URL Page,Roderic,,R. D. M. Page|Roderic D. M. Page|Rod Page,United Kingdom,"",,0000-0002-7101-9767,https://bionomia.net/0000-0002-7101-9767 Chatzimanolis,Stylianos,,S. Chatzimanolis|Stelios Chatzimanolis|Stelianos Chatzimanolis,United States,Taxonomist,,0000-0001-9008-0611,https://bionomia.net/0000-0001-9008-0611 Bachman,Steven,,Steven P Bachman|Steven Philip Bachman|Steve Bachman,United Kingdom,Red List|Conservation|Plants|Plantae|GIS|English,,0000-0003-1085-6075,https://bionomia.net/0000-0003-1085-6075 Robbins,Tod,,Todd Robbins,"",libraries|archives|Mormon studies|linked data|digital humanities|digital asset management,,0000-0002-6752-9721,https://bionomia.net/0000-0002-6752-9721</code></pre> <p>obtained via </p> <pre><code class="language-bash">preston cat\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2\ https://bionomia.net/data/bionomia-public-profiles.csv\ | head -n5</code></pre> <p> </p> <p>This data publication was created in part in context of "Bee-hind the Scenes: Documenting Digital Traces of Prominent Natural History Bee Specimens" (see https://beehind.org), and is used together with an exhaustive list of record identifiers and associated institution, collection and catalog information [3].</p> <p><strong>References</strong></p> <p>[1] Shorthouse DP (2020) Slinging With Four Giants on a Quest to Credit Natural Historians for our Museums and Collections. Biodiversity Information Science and Standards 4: e59167. <a href="https://doi.org/10.3897/biss.4.59167">https://doi.org/10.3897/biss.4.59167</a></p> <p>[2] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2020.101132">https://doi.org/10.1016/j.ecoinf.2020.101132</a></p> <p>[3] Poelen, Jorrit. (2023). Global Biodiversity Informatics Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130 (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7789866</p> <p> </p>
Evaluation of archival HIV DNA in brain and lymphoid tissues
<p>HIV reservoirs persist in anatomic compartments despite antiretroviral therapy (ART). Characterizing HIV reservoirs in central nervous system (CNS) and other tissues is crucial to inform cure strategies. We evaluated paired autopsy brain – frontal cortex (FC), occipital cortex (OCC), basal ganglia (BG) – and peripheral lymphoid tissues from 63 people with HIV. Participants passed away while virally suppressed on ART and without evidence of CNS opportunistic disease. We quantified HIV DNA and, from a subset of 14 participants, we obtained full-length HIV-envelope (FL HIV-env) sequences. We detected HIV DNA (<em>gag</em>) in most brain (65.1%) and all lymphoid tissues. Lymphoid tissues had higher HIV DNA levels compared to brain (p<0.01). Levels of HIV <em>gag</em> between BG and FC were similar (p>0.2), while OCC had the lowest levels (p=0.01). Females had higher HIV DNA levels in tissues than males (<em>gag</em>: p=0.03; 2-LTR, p=0.05), suggesting possible sex-associated mechanisms for HIV reservoir persistence. Most FL HIV-env sequences (n=143) were intact, while 42 were defective. Clonal sequences were found in eight participants; one participant had the same clonal defective sequences in brain and spleen, suggestive of cell migration. From 10 donors with paired brain and lymphoid sequences, we observed evidence of compartmentalized sequences in two participants. Our data contribute to the idea that the brain is a site for HIV reservoirs during ART where compartmentalized proviral populations may occur in a subset of people. Future studies accessing FL HIV-provirus and replication competence in vitro are needed to further evaluate the HIV reservoirs' composition in tissues.</p>
Beetle Team Tribolium Data Archive
<p>The Beetle Team Tribolium Data Archive is a collection of 10 data sets created and/or analyzed over a 20-year period by a multidisciplinary group of collaborators, the "Beetle Team" (Jim Cushing, R. F. Costantino, Brian Dennis, Robert A. Desharnais, Shandelle M. Henson, Aaron A. King, and Jeffrey Edmunds). The data are from population laboratory experiments using the flour beetle <em>Tribolium castaneum</em>. Their research program was focused on providing biological evidence of complex nonlinear population dynamics.</p>
Archive for Master's thesis
<p>This archive contains model forcing and output for the Shyft model, along with scripts of the related data processing. The structure of the archive (folders) is as follows:</p> <ol> <li>"lalm" and "elverum: contain the meteorological forcing data for the two catchments Lalm and Elverum, respectively.</li> <li>"model forcing for historical periods": contains the bias corrected climate model data representing the three historical periods.</li> <li>"model simulation output": contains the model output data for the simulations of the three historical periods.</li> <li>"shyft workspace": contains the data processing scripts. </li> </ol> <p>1. This dataset consists of the folders: "senorge", "era5" and "hysn5". The included data variables are: temperature, precipitation, wind speed, relative humidity and radiation. Temperature and precipitation are found in "senorge". Wind speed is found in "era5". Lastly, relative humidity and radiation are found in "hysn5". The dataset is of the netCDF-format. The folders contain data that was downloaded from the sources: SeNorge2018 (The Norwegian Meteorological institute, 2022), ERA5-land (Muñoz, 2019; Muñoz, 2021) and HYSN5 (Haddeland, 2022). The data is described as follows:</p> <p>temperature: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>precipitation: </p> <ul> <li>Description: daily mean precipitation</li> <li>Unit: mm/day</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>wind speed: </p> <ul> <li>Description: daily mean wind speed </li> <li>Unit: m/s</li> <li>Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)</li> <li>Grid mapping: EPSG:4326</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>relative humidity: </p> <ul> <li>Description: daily mean near-surface relative humidity </li> <li>Unit: %</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>radiation: </p> <ul> <li>Description: daily mean surface downwelling shortwave radiation</li> <li>Unit: W/m<sup>2 </sup></li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>2. This dataset contains climate model data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD). The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: temperature (temp), precipitation (prec), wind speed (wind), relative humidity (humi) and radiation (radi). The dataset is of the netCDF-format. The related source data is not published here, due to licences. Contact Lu Li at the NORCE research centre regarding data accessibility. The data is described as follows:</p> <p>temperature: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>precipitation: </p> <ul> <li>Description: daily mean precipitation</li> <li>Unit: mm/hour</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>wind speed: </p> <ul> <li>Description: daily mean wind speed </li> <li>Unit: m/s</li> <li>Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>relative humidity: </p> <ul> <li>Description: daily mean near-surface relative humidity </li> <li>Unit: -</li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>radiation: </p> <ul> <li>Description: daily mean surface downwelling shortwave radiation</li> <li>Unit: W/m<sup>2 </sup></li> <li>Spatial resolution: 1x1 km</li> <li>Grid mapping: UTM Zone 33</li> <li>Dimension: time, latitude and longitude </li> </ul> <p>3. This dataset contains time series data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD), which are output from the Shyft model. The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: discharge, temperature, precipitation, wind_speed, relative_humidity and radiation, snow water equivalent (SWE) and snow covered area (SCA). The dataset is of the csv-format. </p> <p>NB: the datetime index of the data suggests that the data covers the period of 1700-1850, however this is only true for IT. This inconsistency is caused by a limitation of datetime64 in pandas, which does not handle dates prior to the year 1678. </p> <p>The data is described as follows:</p> <p>discharge: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Dimension: time</li> </ul> <p>temperature: </p> <ul> <li>Description: daily mean air temperature </li> <li>Unit: degrees Celsius</li> <li>Dimension: time</li> </ul> <p>precipitation: </p> <ul> <li>Description: daily mean precipitation</li> <li>Unit: mm/hour</li> <li>Dimension: time</li> </ul> <p>wind_speed: </p> <ul> <li>Description: daily mean wind speed </li> <li>Unit: m/s</li> <li>Dimension: time</li> </ul> <p>relative_humidity: </p> <ul> <li>Description: daily mean near-surface relative humidity </li> <li>Unit: -</li> <li>Dimension: time</li> </ul> <p>radiation: </p> <ul> <li>Description: daily mean surface downwelling shortwave radiation</li> <li>Unit: W/m<sup>2 </sup></li> <li>Dimension: time</li> </ul> <p>SWE: </p> <ul> <li>Description: daily mean snow water equivalent </li> <li>Unit: mm</li> <li>Dimension: time</li> </ul> <p>SCA: </p> <ul> <li>Description: daily mean snow covered area (% of total catchment area)</li> <li>Unit: -</li> <li>Dimension: time</li> </ul> <p>4. The scripts make up the workflow of the thesis. In order to reproduce the results, the first script has to be run firstly, then the second script is applied on the output from the first etc. Keep in mind that manual adjustments inside the scripts are required in order to obtain some of the results. The scripts are described as follows:</p> <ol> <li>"Subsetting_data.ipynb": This script subsets forcing data (temperature, precipitation, wind speed, relative humidity and radiation) from the sources (SeNorge2018, ERA5-Land and HySN5) to the catchments of Lalm and Elverum.</li> <li>"convert_netcdf.ipynb": This script converts netCDF-files of temperature, precipitation, wind speed, relative humidity and radiation to fit as model forcing to the Shyft modeling framework. It also creates a cell data file containing information about the catchments (Lalm and Elverum) forest, lake and glacier fraction, which are required in Shyft. </li> <li>"QDM_lalm.ipynb" and "QDM_elverum.ipynb": These scripts perform the bias correction approach, Quantile Delta Mapping (QDM), on the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).</li> <li>"extract_historical_periods.ipynb": This script extracts the three historical periods of 1000-1150 (Medieval Warm Period), 1600-1750 (Little Ice Age) and 1800-1950 (Industrial Time) from the climate model data (temperature, precipitation, wind speed, relative humidity and radiation). </li> <li>"calibration_lalm.ipynb" and "calibration_elverum.ipynb": Scripts that runs the calibration of Lalm and Elverum catchment using the Shyft model, respectively.*</li> <li>"simulation_lalm.ipynb" and "simulation_elverum.ipynb": Scripts that runs the simulation of Lalm and Elverum catchment using the Shyft model, respectively.*</li> <li>"data_analysis.ipynb": This script contains the data analysis performed on the Shyft model simulation output. The analysis includes: calculations of mean monthly values of the climate variables (discharge, temperature, precipitation, snow water equivalent and snow covered area), decadal time series of the climate variables, calculations of mean floods and 100-year floods, flood and extreme precipitation frequency analysis, calculation of season index, estimation of flood generating processes, plotting of flood roses and estimation of Standardised Precipitation Index. </li> </ol> <p>*For the Shyft model configuration, simulation and calibration files (yaml-files) are included in the folder "yaml_lalm" and "yaml_elverum" for the two catchments. These yaml-files are described as follows: </p> <ul> <li>simulation.yaml: is used for configuration of the model simulation </li> <li>calibration.yaml: is used for configuration of the model calibration</li> <li>calibrated_model.yaml: contains the calibrated model parameters</li> <li>datasets.yaml: contains the paths to the data variables </li> <li>interpolation.yaml: contains the interpolation methods and parameters</li> <li>region.yaml: contains the modeling domain</li> </ul> <p>References: </p> <p>Haddeland, I. (2022). HySN2018v2005ERA5 (Version 1) [Data set]. Zenodo. (Accessed on: 19-09-2022). doi: https://doi.org/10.5281/zenodo.5947547.</p> <p>Muñoz Sabater, J. (2019). ERA5-Land hourly data from 1981 to present [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac.</p> <p>Muñoz Sabater, J. (2021). ERA5-Land hourly data from 1950 to 1980 [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac.</p> <p>The Norwegian Meteorological institute, MET Norway (2022). Norwegian observational gridded climate datasets [Data set]. Thredds.met. (Accessed on: 05-09-2022). url: https://thredds.met.no/thredds/catalog/senorge/seNorge_ 2018/Archive/catalog.html.</p>
Moving Image Archive - Data Foundry - National Library of Scotland
<p>This dataset was created in October-December 2022 for the National Library of Scotland's Data Foundry by <a href="https://data.nls.uk/projects/the-national-librarians-research-fellowship-in-digital-scholarship-2022-23/">Gustavo Candela, National Librarian’s Research Fellowship in Digital Scholarship 2022-23</a>.</p> <p>This output is based on the <a href="http://data.nls.uk/data/metadata-collections/moving-image-archive/">Moving Image Archive</a> dataset and is the result of the transformation to RDF described in a research article published in the <a href="https://doi.org/10.1177/01655515231174386">Journal of Information Science</a>.</p> <p>For more information about the project, visit the <a href="https://data.nls.uk/projects/the-national-librarians-research-fellowship-in-digital-scholarship-2022-23/">Data Foundry Fellowship page</a>.</p> <p><strong>References</strong></p> <p>Candela, G. (2023). Towards a semantic approach in GLAM Labs: The case of the Data Foundry at the National Library of Scotland. <em>Journal of Information Science</em>. <a href="https://doi.org/10.1177/01655515231174386">https://doi.org/10.1177/01655515231174386</a></p>
Entangled Ecologies. Interactive Documentary between Living Archive, Responsible Witnessing and Relational Co-Creation
<p>The videoessay is also accessible: <a href="https://trametrami.avinus.org/publikationen/3-2023">https://trametrami.avinus.org/publikationen/3-2023</a></p> <p>Recent re-conceptualizations of participation as well as theories of digital transformation culture have shifted the notion of the archive seeing not so much as a static, institutional body but rather as a dynamic, living epistemic environments. Building on these approaches, this presentation discusses emerging phenomena in interactive documentary focusing on ecological emergency – seeing this crisis itself as a complex ecology of issues where images and conceptualization past, present and future meet. Taking paradigmatic projects addressing the issue of climate change – <em>The Shore Line</em> (2017) and <em>Climate Witness Project </em>(2019) – I suggest tentative answers to the question in which way i-docs can contribute to tackle complex change. Are there images from the past which help us to better cope with the present and to envision a more sustainable future? How can one raise awareness of immanent ecological risks when menace is almost invisible, unprecedented and un-imaginable? Is there a way to negotiate multifaceted entanglements through documentaries which are neither paralyzing ecodystopian narratives nor naive ecotopian success-stories? The hypothesis underlying my approach is that one possible solution resides in documentaries which are built on principles of pluri-perspectivity and polyphony to transcend dualisms and to build bridges leading from history to the forthcoming. Discourses from various traditions are brought into dialogue: theories of documentary film meet ecocriticism; network theory encounters reflections on the epistemic dimension of non-fiction; and concepts of co-creation and intervention are related to processes of witnessing, doing documentary and responsible action-taking.</p> <p> </p> <p>more information: <a href="https://did.avinus.org/">https://did.avinus.org/</a> and <a href="https://trametrami.avinus.org/publikationen">https://trametrami.avinus.org/publikationen </a></p>
Sequencing data for: Tracking climate-change induced biological invasions over 4 decades by metabarcoding archived natural eDNA samplers
<p><span>In a time of unprecedented global environmental change, understanding the response of biodiversity is paramount. However, our knowledge of anthropogenic impacts on ecosystems is limited by a lack of standardized retrospective biomonitoring data. Here, we use four-decade time series of archived blue mussels to trace spatiotemporal biodiversity change in coastal ecosystems. The filter-feeding mussels can serve as natural eDNA samplers, carrying an imprint of the surrounding aquatic community at the time of sampling. By sequencing the preserved DNA, we characterize highly diverse mussel-associated communities and reconstruct the invasion trajectory of an invasive species to the detriment of native taxa uncovering repeated population collapses and reinvasions after cold winters. Time series of natural eDNA samplers provide highly resolved temporal data on community assembly and global warming-driven invasion processes and overcome critical shortfalls in our understanding of biodiversity change in the Anthropocene.</span></p>
As We May Remember. The Future of Remembrance from the Perspective of Documentary Archives
<p>Can we already discern the structures of future memory cultures? From this fundamental question, I will examine the mediated forms of current memory culture, with a particular focus on documentary films. Digital technologies are currently leading to significant changes in our knowledge culture, which will inevitably impact the shaping of memory cultures and the structures of media memory in the near future. These changes manifest in two opposing processes: on the one hand, in the archival situation, characterized by non-accessibility, poor archiving, or even the physical decay of documentary material (such as analog video), and on the other hand, attempts at digital preservation or even reconstitution of archives through various media transformation processes - particularly re-mediatizations, which are shaped by new forms of media expression (i-docs, VR/AR-technologies etc.). The latter is not only a challenge for media historiography, but opens up new possibilities for the memory work of GLAM and memorial sites. In this contribution, I will explore the tension between disappearing archive material, using the example of the archival situation of German documentary films, and selected new digital forms of re-mediatization, focusing on themes such as the Holocaust and the Nazi era.</p> <p> </p> <p>See also: Weber, Thomas. (2023, June 30). As We May Remember. The Future of Remembrance from the Perspective of Documentary Archives. In TraMeTraMi: Vols. 4-2023. <a href="https://trametrami.avinus.org/publikationen/4-2023">https://trametrami.avinus.org/publikationen/4-2023</a> </p> <p> </p> <p><strong>Thomas</strong> <strong>Weber</strong> is Professor for media studies at the University of Hamburg. He was one of the leaders of the DFG-project “History of the german documentary film after 1945” and leads several other projects in the field of documentary film (see<a href="http://www.dokartlabor.avinus.de/"> www.dokartlabor.avinus.de</a>) His books include: <em>Webdokumentationen</em> 2021; <em>Medienkulturen des Dokumentarischen</em> 2017 (ed. with Carsten Heinze); <em>Mediale Transformationen des Holocausts</em> 2013 (ed. with Ursula von Keitz); “Documentary Film in Media Transformation”, InterDisciplines – Journal of History and Sociology. Vol 4, No 1 (2013). Further information see<a href="http://www.thomas-weber.avinus.de/"> www.thomas-weber.avinus.de</a></p> <p> </p>
Data Archive for the NASA DAILI CubeSatMission:NASA Award Number: 80NSSC18K0973
<p>These are the operational data collected by the NASA DAILI CubeSat Mission from March 2 2022 to June 4 2022.</p> <p>The readme file describes the data in detail</p>
Archive of ASAPbio's list of preprint servers: policies and practices across platforms
<p>These are the raw data behind ASAPbio's list of of preprint servers, found at https://asapbio.org/preprint-servers</p> <p>An earlier version of this dataset was reported in https://zenodo.org/record/3700874#.ZD2SC3bMIQ8</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.