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About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project.</p>
Connecting U.S. Supreme Court Case Information and Opinion Authorship (SCDB) to Full Case Text Data (CAP), 1791-2011
<p>This dataset was constructed to connect the rich metadata created by the Supreme Court Database (SCDB) to the Caselaw Access Project (CAP) full-text court opinion data. Since the SCDB includes only substantive opinions, it is necessarily a subset of the full range of opinions available through CAP.</p> <p>There are two parts to this data: the map connecting each SCDB ID to its corresponding CAP case number, and a more advanced (but error-prone) version in which the authorship of each opinion text identified for the case in CAP is attributed to the Justice who wrote it. Each of these data products have been hand-corrected to the best of this author's ability.</p> <p><strong>SCDB-CAP map</strong></p> <p>The SCDB->CAP map began as a relatively straightforward automated matching process, based on the US Reports citation for each case as expressed in both SCDB and CAP. Slightly over 80% of SCDB entries found a single CAP data match this way. From there, the data was entirely hand-corrected, with non-matches or duplicate matches individually investigated and manually corrected.</p> <p>Some SCDB entries simply could not be matched to an appropriate CAP text. Initially, the entirety of US Reports volume 44 was missing, but with the help of CAP staff, the volume was located as having been filed in the New York jurisdiction rather that the United States jurisdiction. The case numbers were then added to the map, but until the volume is relocated to the United States jurisdiction, it may be necessary to also incorporate the New York jurisdiction in full text analysis so that the cases from volume 44 can be searched. 108 more missing cases are from US Reports volume 131, which was a "catch up" volume published in the 19th century. These catch-up cases, many heard by the Supreme Court decades prior, were numbered with lowercase roman numerals instead of the ordinary numbers, which is almost certainly why CAP's software dismissed the catch-up section as prefatory material. Many of the rest of the errors seem largely to be examples where the SCDB project recognized a separate court action that CAP did not. Perhaps most of these seem to have been later rehearings for a case previously decided, which in the 19th century particularly were commonly reported out at the end of the first decision text. While SCDB sometimes gave these subsequent but related actions a separate SCDB entry, CAP seems to have largely incorporated them as part of the text of the main case. Additionally, there were a few that simply could not be found, despite a careful look through each database as well as the original US Reports and sometimes adjacent volumes. Finally, the cases were only matched up through the 2011 court term. After the 2011 term, the mismatches between CAP and SCDB were extensive and frequently seemed impossible to resolve.</p> <p>Even so, with the manual correction, the overall error rate is low. Of 28,304 cases, only 191 do not have a match, and of those, 108 are contained within the vol. 131 "catch up" volume. Since most of the rest are extremely short subsequent actions that were separately noted by SCDB, the effect of these non-matched cases would seem to be small in most cases.</p> <p>The typical use case would be that the researcher would generate some kind of results based on searching in the CAP full text, then could use the CAP ID to look up the SCDB ID in the map. With the SCDB ID, of course, the rich metadata from the SCDB can then be connected to each result as needed.</p> <p><strong>Opinion authorship</strong></p> <p>Being able to use the rich metadata of SCDB in conjunction with a case's full text is exciting, but it immediately prompts a further question -- what if the texts could be attributed directly to the Justices who authored them? SCDB produces its data in two forms; one is "case centered," where each record represents one case, and the other is "justice centered," in which each record is the vote of one Justice in one case. CAP, in turn, breaks the total text of the case into distinct opinions, and tries to attribute those opinions to their authors by scraping a string of text from the raw input. Therefore, the challenge was to connect these two sources at the opinion level.</p> <p>Connecting the opinions, like connecting the cases, involved an initial match by machines, followed by manual correction and revision. In this case, the scope of the manual effort was much larger than that posed by the case-level connection, and more errors were noted in both SCDB and CAP.</p> <p>The matching process involved a number of steps. First a list of opinions was generated from the CAP data, then matched to SCDB using the SCDB-CAP connector data described above. (Thus, a case without a CAP match in the SCDB-CAP data will not appear in the opinion author data either.) CAP opinions were numbered in the order they were encountered in each CAP case JSON object, and these numbers are used to distinguish the opinions.</p> <p>Next, a round of automatic matching was performed. If there was only one opinion, and only one author listed in the SCDB data, then the majority opinion author (as listed in SCDB) was safely assumed to be the author. If there was no author listed in SCDB, "percuriam" was recorded as the author in this data. If there were exactly two opinions and two authors, the process was also straightforward, as the SCDB-identified majority opinion author was assigned to opinion 1, and the remaining author assigned opinion 2.</p> <p>Subsequently, cases with more than two opinions were processed. A potential match (i.e. a "guess") for each opinion in a given case was created by listing each Justice identified by SCDB as having written an opinion in the case. These guesses were then parsed using a semi-automatic procedure with Levenshtein distance fuzzy name matching. With sufficiently conservative parameters, a successful fuzzy match meant that the non-successful guesses for that opinion could be deleted. These sorted guesses were then reviewed manually. Particular care was also taken for any opinion that contained authored opinions by Justices who had similar names (for example, Clark and Black differ by only a single letter). These sorts of cases, as well as instances of co-authorship, were identified and fixed manually.</p> <p>Those opinions whose authorship could not be matched then were fixed by hand. These included some where the CAP author strings were more complicated than SCDB's strict interpretation; others where the OCR in CAP which contained the Justice name was especially bad; and a number of others where "Mr. Chief Justice" couldn't be directly matched with an author name by the machine. After this light manual correction, almost 500 opinions with substantial errors remained to be individually investigated in depth, by examining the CAP record, the SCDB record, and images of the US Reports for that case. For these last tough customers, errors in the source data were commonly the cause of matching problems. Typically these were of three kinds: examples where CAP should have split the text but didn't (e.g. 2 opinions together in one opinion entry in CAP); examples where SCDB either did not identify or mis-identified an author (such as attributing it to Swayne when it was written by Miller); and examples of non-valid opinions (such as where CAP mistakenly split the opinion too early, leaving an opinion fragment).</p> <p>For these errors, a system of codes was created in the author field to signal the error type so that researchers can be suitably cautious. The error code is always at the beginning of the field and is followed by a comma and the names of each author, separated by a comma with no space to facilitate parsing. Note also that co-authors are listed as comma-separated names in this same field with no error code. Researchers will probably want to disaggregate this field to create duplicate records with each individual author for most purposes. The justice number field also contains information about all justices authoring the opinion but the error codes have been omitted here.</p> <ul> <li>!C -- error: multiple opinion texts combined (i.e. CAP splitting error)</li> <li>!X -- error: unattributed or misattributed opinion (not listed in SCDB as writer)</li> <li>!D -- error: extra opinion that should be deleted, i.e. not a valid opinion</li> <li>!W -- error: listed as Writers by SCDB, but should be co-authors</li> </ul> <p> </p> <p><strong>Data file structure</strong></p> <p>"scdb_cap-051820.tsv" is a Tab-separated data file containing 5 columns: SCDB ID, CAP ID, US Reports citation, case date, and case name (the latter three from the SCDB data).</p> <p>"scdb-cap-opinion-authorship_051920.tsv" is a Tab-separated data file containing seven columns: SCDB ID, CAP ID, US Reports citation, case name, opinion number in the case, opinion author, and SCDB justice ID. See above for caveats about disaggregating and error codes in fields six and seven.</p> <p><strong>Errors</strong></p> <p>It is likely that errors remain in this data, and it is also hoped that some of the errors beyond the author's immediate control might be fixed in the upstream data so that they can be corrected here. Authors would be grateful for error reports, and also reports of errors fixed, if any.</p> <p> </p>
Rediscovery Datasets: Connecting Duplicate Reports of LibreOffice and Gentoo
<p>We present two defect rediscovery datasets mined from Bugzilla (supplementary to previously published datasets: Zenodo, http://doi.org/10.5281/zenodo.400614). These datasets capture data for two groups of open source software projects: LibreOffice and Gentoo. The datasets contain information about the inter-relationships among duplicate defects.<br> </p> <p><strong>File Descriptions</strong></p> <ul> <li>libreoffice.csv - LibreOffice Defect Rediscovery dataset</li> <li>gentoo.csv - Gentoo Defect Rediscovery dataset</li> </ul> <p> </p> <ul> <li>libreoffice.relations.csv - Inter-relations of rediscovered defects of LibreOffice</li> <li>gentoo.relations.csv - Inter-relations of rediscovered defects of Gentoo</li> </ul>
NDC-SDG Connections: Data on updated NDC submissions (V2)
<p>NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development.</p> <p>The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs.</p> <p>With its second version with data on the updated NDC submissions (V2), the NDC-SDG Connections project opened its data for public re-use. In 2025, the V2 is updated with new data to version 1.2.0. The data is provided in the following formats:</p> <ul> <li>single .csv files (data per SDG)</li> <li>zip .csv file (data per SDG for all SDG in one zip) </li> </ul> <p>Visit the Online Data Visualisation to interact directly with the data<strong>: www.NDC-SDG.info</strong></p> <p> </p> <p><strong>Note:This data set contains data for second NDC submissions (V2)</strong>. The terms ‘First’ and ‘Updated’ do not fully follow the UNFCCC nomenclature. For most countries, updated NDCs are called ‘First updated NDC’ or ‘Enhanced NDCs’, while some countries call their updated NDCs for ‘Second NDC’. In order to make it comprehensible, the tool developers have chosen to distinguish between ‘First’ and ‘Updated’. Detailed description of which version is counted as ‘First’ and which as ‘Updated’ has been documented in the data.<br><br></p> <p> </p> <p><strong>Updated NDCs included in version 1.2.0 of V2 - </strong><strong>New data was added for 27 updated NDCs plus one NDC updated to a newer version:</strong></p> <p>New Updated NDCs:</p> <p>Azerbaijan, Benin, Democratic Republic of the Congo, Grenada, India, Kyrgyzstan, Madagascar, Mali, Montenegro, Nepal, Nigeria, Oman, Qatar, Saint Kitts and Nevis, Saint Lucia, Samoa, Sierra Leone, Singapore, South Sudan, Sri Lanka, Suriname, Togo, Tunisia, Tuvalu, Ukraine, United States of America</p> <p>New version of Updated NDC:</p> <p>Namibia’s NDC has been updated to the most recent version.</p>
List of countries and NDCs in the NDC-SDG Connections: Data on updated NDC submissions (V2)
<p>NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development.</p> <p>The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs.</p> <p>With its second version, the NDC-SDG Connections project opened its data for public re-use. <br><br>This dataset contains a list of the countries and NDCs which are included in the NDC-SDG Connections tool as part of updated NDC submissions (V2), in .xlxs format. It is a complement to the dataset available at: https://doi.org/10.5281/zenodo.11400384 <br><br><strong>Visit the Online Data Visualisation to interact directly with the data: www.NDC-SDG.info</strong></p>
Dataset and Input Files for the "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA" Manuscript
<p>Datasets and input files used for the Ecology and Society manusript "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA". </p>
Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan - Supporting Movie Files
<p>This is a collection of movies related to the manuscript "Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan" by McLaughlin et al to describe kidney organization using 3D light sheet fluorescence microscopy. The preprint manuscript associated with these movies is </p> <p>Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan</p> <p>Liam McLaughlin, Bo Zhang, Siddharth Sharma, Amanda L. Knoten, Madhurima Kaushal, Jeffrey M. Purkerson, Heidy Huyck, Gloria S. Pryhuber, Joseph P. Gaut, Sanjay Jain</p> <p>bioRxiv 2024.07.29.605633; doi: <a href="https://doi.org/10.1101/2024.07.29.605633">https://doi.org/10.1101/2024.07.29.605633</a></p> <p>Movie Legends</p> <p>Movie 1: 3D view of the entire slice showing key structures.<br>3D light sheet fluorescence microscopy 5x movie of reference adult sample SK3, demonstrating glomeruli, collecting<br>ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:10s — Segmentations. Annotations are in the movie.</p> <p><br>Movie 2: Relationship of nerves with glomeruli and juxtaglomerular apparatus.<br>The movie depicts innervation of glomeruli in 2D optical sections, containing glomeruli, Tuj1(labels TUBB3)-stained<br>nerves, and CGRP-stained sensory nerves. 0:22s — Innervation of the JGA. 0:33 s— Innervation of the Macula Densa.<br>0:47s — Innervation of the outer boundary of the Bowman’s Capsules.</p> <p><br>Movie 3: Neuro-nephron connectivity.<br>The movie explores innervation between different structures of the same nephron, and between nephrons in both 3D and<br>2D optical sections, containing glomeruli, Tuj1 (TUBB3)-stained nerves, CGRP-stained sensory nerves, proximal<br>(convoluted) tubule, thick ascending limb, distal convoluted tubule, and collecting duct. 0:00-1:53min — 3D<br>relationships. 0:38s — Innervation of glomerulus JGA. 1:02min — Post-JGA innervation of medullary ray structures.<br>1:54min-end — 2D relationships. 2:38min — Interglomerular/internephron innervation.</p> <p><br>Movie 4: Neurovascular – nephron patterns in the medulla.<br>The movie shows innervation pattern within the medulla. 0:00s—5x adult medullary innervation pattern in 3D;<br>0:34s—in 2D also showing Vasa Recta and Collecting Duct; 0:48s—in 3D at 20x resolution. 1:04min—20x adult<br>medullary innervation of proximal tubule, thick ascending limb, and Vasa Recta in 3D; 1:31min—view if the previous in<br>2D. 2:11min—5x adult medullary innervation pattern in 3D; 2:40min—in 2D also showing Vasa Recta and Collecting<br>Duct; 2:54min—in 3D at 20x resolution; 3:15—in 2D at 20x resolution.</p> <p><br>Movie 5: Network motifs.<br>Exploring 3D neuroglomerular networks at 5x and 20x resolution. 0:00s — Raw 5x signal from young adult sample SK2.<br>0:16s— Segmented 5x SK2 with 20x coregistrations. 0:26 — Exploring SK2 20x FOV. 0:38 — 20x network featuring<br>hourglass motif. 1:19 5x “Type I” network in SK2. 1:41 — Segmented 5x adult SK3 sample featuring a “Type 2” network<br>containing a lattice motif. 2:23 — Exploring SK3 20x FOV, featuring a network with a lattice motif.</p> <p><br>Movie 6: LSFM movie of pediatric kidney<br>3D lightsheet 5x image of neonatal sample SK414, containing glomeruli, collecting ducts, nerves, and blood vessels.<br>0:00s — Raw signal. 0:22s — Segmentations. 1:34min — Overlayed segmentations.</p> <p><br>Movie 7: Neuronephron connectivity time course<br>Exploring neuronephro-networks across a life time course in 1mm3 20x images. 0:00 — Raw neonatal. 0:17sec —<br>Segmented neonatal. 0:47 sec— Raw infant. 0:54 — Segmented infant with network. 1:13min — Raw young adult.<br>1:23min — Segmented young adult with network. 1:33min — Raw adult. 1:43min — Segmented adult featuring network<br>with keychain motif. 1:49min — Raw aged. 1:59min — Segmented aged with network featuring pyramid motif.</p> <p> </p> <p>Movie 8: Mother Glomeruli</p> <p>Evaluating distributions and innervation of mother glomeruli in neuroglomerular networks. 0:00 - Large 20x 3D Network sample SK1 FOV8. 0:13 - Sample SK1 5x Network in 2D. 0:36 - Mother glomerulus neural quantifications SK1. 0:41 Large 20x 3D Network sample SK3 FOV12. 0:56. Large 20x 2D Network sample SK3 FOV12. 1:21 - Mother glomerulus neural quantifications SK3.</p> <p> </p> <p>Movie 9: Segmentations</p> <p>Demonstrating accuracy of segmentations that combine supervised ML with manual validation in Sample SK2 FOV5. 0:00 - AQP2 labelled Collecting Duct and NPHS1 labelled Glomerulus. 0:26 - Tuj1 labelled nerve.</p> <p> </p> <p>metadata_analyzed_images:</p> <p>Metadata for images that were analyzed. Includes metadata .txt files for all stitched, downsampled samples, as well as .csv metadata for certain raw .czi files (pre-processing).</p>
Systematic review data on the role of urban planning in the context of sustainability transformations and human-nature connections
<p>This data publication belongs to the following research paper:<br>Harms, P., Hofer, M. & Artmann, M. Planning cities with nature for sustainability transformations — a systematic review. Urban Transform 6, 9 (2024). <br>https://doi.org/10.1186/s42854-024-00066-2 </p> <p>We conducted a systematic literature review according to the PRISMA Statement 2020 (Page et al. 2021). The list shows the steps performed and the names of the corresponding datasets available here:</p> <p>Step A - Identification of Records<br>A_01_PRISMA-protocoll.pdf<br>A_02_searchstring.txt<br>A_03_recordsidentified.ris</p> <p>Step B - Screening of Records<br>B_01_recordsscreened-title-keywords.ris<br>B_02_recordsscreened-abstract.ris<br>B_03_recordsscreened-fulltext.ris<br>B_04_studiesincluded.ris<br>B_05_screeningdecisions-overview.xlsx</p> <p>Step C - Qualitative Analysis<br>C_01_codingscheme.xlsx</p> <p> </p>
Research compendium for 'Refitting the Context: A Reconsideration of Cultural Change among Early Homo sapiens at Fumane Cave through Blade Break Connections, Spatial Taphonomy, and Lithic Technology'
<div> <h3>Compendium DOI:</h3> <p><a href="../doi/10.5281/zenodo.10965413">https://zenodo.org/doi/10.5281/zenodo.10965413</a> </p> </div> <p>The content available at the above provided URL will reproduce the results as documented in the publication. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Refitting-The-Context">https://github.com/ArmandoFalcucci/Refitting-The-Context</a> represent the developmental versions and might have undergone modifications since the paper's publication.</p> <div> <h3>Maintainer of this repository:</h3> </div> <p>Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>)</p> <div> <h3>Published paper:</h3> </div> <p>Armando Falcucci, Domenico Giusti, Filippo Zangrossi, Matteo De Lorenzi, Letizia Ceregatti, Marco Peresani. Refitting the Context: Revisiting the Aurignacian sequence at Fumane Cave through blade fragment connections, spatial taphonomy, and lithic technology. <em>Journal of Paleolithic Archaeology</em> (2024). DOI: <a href="https://doi.org/10.1007/s41982-024-00203-0" rel="nofollow">10.1007/s41982-024-00203-0</a></p> <div> <h3>Abstract:</h3> </div> <p>High-resolution stratigraphic frameworks are crucial for unraveling the biocultural processes behind the dispersals of Homo sapiens across Europe. Detailed technological studies of lithic assemblages retrieved from multi-stratified sequences allow archaeologists to precisely model the chrono-cultural dynamics of the early Upper Paleolithic. However, it is of paramount importance to verify the integrity of these assemblages before building explanatory models of cultural change. In this study, multiple lines of evidence suggest that the stratigraphic sequence of Fumane Cave in northeastern Italy experienced minor post-depositional reworking, establishing it as a pivotal site for exploring the earliest stages of the Aurignacian. By conducting a systematic search for break connections between blade fragments and applying spatial analysis techniques, we identified three well-preserved areas of the excavation containing assemblages suitable for renewed archaeological investigations. Subsequent technological analyses, incorporating attribute analysis, reduction intensity, and multivariate statistics, have allowed us to discern the spatial organization of the site during the formation of the Protoaurignacian palimpsest A2–A1. Moreover, diachronic comparisons between three successive stratigraphic units prompted us to reject the hypothesis of techno-cultural continuity of the Protoaurignacian in northeastern Italy after the onset of the Heinrich Event 4. Based on the variability of the lithic and osseous artifacts, the most recent assemblage analyzed, D3b alpha, is now ascribed to the Early Aurignacian, aligning the evidence from Fumane with the current understanding of the development of the Aurignacian across Europe. Overall, this study demonstrates the high effectiveness of the break connection method when combined with detailed spatial analysis and lithic technology, providing a methodological tool particularly amenable to be applied to sites excavated in the past with varying degrees of recording accuracy.</p> <div> <h3>Keywords:</h3> </div> <p>Protoaurignacian; Early Aurignacian; Lithics; Refittings; Assemblage integrity; Spatial analysis; Italy</p> <div> <h3>Overview of contents and how to reproduce:</h3> </div> <p>Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the blank and core datasets from the Aurignacian of Fumane Cave and the dataset of the blade fragment connection study. To replicate the results, download the entire repository and employ <code>Refitting-The-Context.Rproj</code> and open the folder <code>script</code>. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here.</p> <div> <h3>Licenses:</h3> </div> <p>Code: <strong>MIT</strong> <a href="http://opensource.org/licenses/MIT" rel="nofollow">http://opensource.org/licenses/MIT</a>, copyright holder: Armando Falcucci (2024).</p> <p>Data and intellectual work: <strong>Creative Commons Attribution 4.0 International License</strong> (<a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).</p>
Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity
<p>Per-trial dataset accompanying the publication Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity.<br> Additionaly, preprocessing code is provided as Codebase.zip.</p>
Anonymized Graph Data with Friend Connections of 189505 VKontakte Users
<p>The dataset contains anonymized graph data with friend connections of 189505 VKontakte users. The dataset was used in <a href="http://github.com/filipp134/vk_bot_detection">this</a> Github project on the detection of social bots on VKontakte. The script which was used for the collection of the dataset is <a href="https://github.com/filipp134/vk_bot_detection/blob/main/Collecting%20datasets%20and%20merging%20them%20into%20one/collect_graph_data.py">here</a>.</p> <p>The dataset was collected in the following 2 steps by using the official <a href="https://dev.vk.com/api/getting-started">VKontakte API</a>:</p> <p>1. Friend connections of 11766 VKontakte users, who had 177739 unique friends, were collected.</p> <p>2. Friend connections of these 177739 users were collected. </p> <p>The dataset is in JSON format and is quite heavy: 424.5 MB.</p> <p> </p>
Dataset for "Effects of Fracture Connectivity on Rayleigh Wave Dispersion"
<p>The scripts on the main directory reproduce figures 3 to 11 in the paper. The dataset is separated into two folders which should be extracted to the directory of the scripts, frac_dist and parrot_output. frac_dist contains .mat files with the fracture distribution of samples and parrot_output contains the results from the upscaling procedure (Favino et al., 2020). A summary of each code is provided in the readme.</p>
Multi-omic analysis of the Arabidopsis clock activator mutant rve 4 6 8 reveals connections to carbohydrate metabolism and proteasome regulation
<p>Plants are able to sense changes in their light environments, such as the onset of day and night, as well as anticipate these changes in order to adapt and survive. Central to this ability is the plant circadian clock, a molecular circuit that precisely orchestrates plant cell processes over the course of a day. REVEILLE proteins (RVEs) are recently discovered members of the plant circadian circuitry that activate the evening complex and PRR genes to maintain regular circadian oscillation. The RVE 8 protein and its two homologs, RVE 4 and 6, have been shown to limit the length of the circadian period, with rve 4 6 8 triple-knockout plants possessing an elongated period along with increased leaf surface area, biomass, cell size and delayed flowering relative to wild-type Col-0 plants. Here, using a multi-omics approach consisting of phenomics, transcriptomics, proteomics, and metabolomics we draw novel connections between RVE8-like proteins and a number of core plant cell processes. In particular, we reveal that loss of RVE8-like proteins results in altered carbohydrate, organic acid and lipid metabolism, including a starch excess phenotype at dawn. We further demonstrate that rve 4 6 8 plants have lower levels of 20S proteasome subunits and possess significantly reduced proteasome activity, potentially explaining the increase in cell-size observed in RVE8-like mutants. Overall, this robust, multi-omic dataset, provides substantial new insights into the far reaching impact RVE8-like proteins have on the diel plant cell environment.<br> <br> This dataset has the raw search outputs for the mass-spec analysis for this manuscript. </p>
Data and code for: A conceptual model-based sediment connectivity assessment for patchy agricultural catchments
<p>Authors: Pedro V G Batista, Peter Fiener, Simon Scheper, Christine Alewell</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Abstract</p> <p>The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. In specific, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the WaTEM/SEDEM model using high resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km<sup>2</sup>) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Metainformation</p> <p>This dataset includes:</p> <p>1 - The input data used for running the WaTEM/SEDEM model in the Baldegg catchment.</p> <p>2 - The discharge and sediment concentration data used for producing the sediment rating curves for the tributaries of the Lake Baldegg.</p> <p>3 - The model and sediment rating curve output data.</p> <p>4 - The R scripts for running the WaTEM/SEDEM model in the Baldegg catchment, the code for producing the sediment rating curves, and the code for summarising and analysing the model output data.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The sediment concentration and water discharge data were supplied by Robert Lovas, from the Department of Environment and Energy of the Canton of Lucerne.</p> <p>The model input data were adapted from freely available ©swisstopo geodata products:</p> <p>Swisstopo. SwissALTI3D. Das hoch aufgelöste Terrainmodell der Schweiz, 2014.</p> <p>Swisstopo. Swiss Map Vector 25 Beta, Das digitale Landschaftsmodell der Schweiz. 2018.</p> <p>Swisstopo. SwissTLM3D. Das grossmassstäbliche Topografische Landschaftsmodell der Schweiz, 2020.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further information we refer to our preprint: https://doi.org/10.5194/hess-2021-231</p> <p> </p> <p> </p>
Good Practices and Success Cases for Short Food Supply Chains connecting consumers and producers
<p>The agroBRIDGES Good Practices (GPs) is a compilation of 51 Good Practices prepared by TEAGASC in collaboration with other agroBRIDGES project partners, where the Good Practices, success cases and opportunities related to Short Food Supply Chains (SFSCs) across Europe are highlighted. The Good Practices are identified and collated from SFSCs in Denmark, France, Greece, Italy, Latvia, Lithuania, the Netherlands, Poland, Turkey, Finland, Ireland, Spain and other international representations across Europe.</p> <p>The dataset contains a spreadsheet containing the 51 Good Practices collected. The included cases are presented in alphabetical order and numbered from 1 -51:</p> <p>1. "Bios Coop" Social Consumer Cooperative </p> <p>2. AGFORISE (AGroFOod clusters platform with common long-term Research and Innovation) </p> <p>3. Agriloops </p> <p>4. Airfield Estate </p> <p>5. Benedicto Turgus (Benedict’s Market) </p> <p>6. Bienvenue à la ferme </p> <p>7. Bioalverde </p> <p>8. Burgerboerderijen </p> <p>9. CASETTA ROSSA Solidarity Purchase Group / Gruppo di Acquisto Solidale (GAS) Casetta Rossa </p> <p>10. Castlegregory Market </p> <p>11. Cloughjordan Community Farm </p> <p>12. Conservas artesanales contigo </p> <p>13. Digital Agriculture Market </p> <p>14. Drumanilra </p> <p>15. Evidence of Public Procurement of local food in the EU </p> <p>16. Fru Møllers Mølleri </p> <p>17. Gastroteca </p> <p>18. Kindergarten "Duzgynelis" </p> <p>19. Københavns Fødevarefællesskab (KBHFF)/Copenhagen Food Co-operative (CPH) </p> <p>20. La Colmena que dice sí </p> <p>21. La OSA Coop </p> <p>22. La OSA Coop </p> <p>23. La Ruche qui dit Oui/Boeren en Buren/L'Alveare Che Dice SÍ </p> <p>24. Local food program </p> <p>25. M**bun </p> <p>26. Manna Organic Store </p> <p>27. Más Que Lechugas </p> <p>28. Mobilus Turgelis </p> <p>29. Naturecode </p> <p>30. Né d'une seule ferme </p> <p>31. Podkarpacki E-bazarek </p> <p>32. Podkarpackie Smaki (Subcarpathian Flavors Cluster) </p> <p>33. Pora Na Pola </p> <p>34. American Farm School of Thessaloniki </p> <p>35. Public Food Services </p> <p>36. QualityLowInputFood (QLIF) </p> <p>37. Rechtstreek </p> <p>38. REKO food collective </p> <p>39. School milk and fruit </p> <p>40. Self picking berries </p> <p>41. Streeckgenoten</p> <p>42. SVAIGI - online shop </p> <p>43. Telaraki.com </p> <p>44. The Agricultural Marketing Platform </p> <p>45. The Cold Chain of Raw Milk </p> <p>46. The Eco-Cabinet (Økoskabet) </p> <p>47. The Little Cheese Shop </p> <p>48. Tiešā pirkšana - Direct shopping group </p> <p>49. TomaTomate </p> <p>50. Traditional United Europe Food (TRUEFOOD) </p> <p>51. Via Amerina and Forre Bio-District</p>
Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS
<p>A portable fNIRS system Brite MKII (Artinis, NE) was placed on the PFC of the self-experimenting participant (Male, 43 years). Ten sources and eight detectors are combined into 22 long separation channels (30mm) and two short-separation channels (SSC) to cover the PFC (Figure 1A). The experiment lasted 392s where the participant was subject to pornographic video clips (V) and performed genital self-stimulation (M) until orgasm was reached (O).<br>Citation of the article related to this dataset:</p> <div> <div><strong>Guevara, E.</strong> (2024). <em>Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS</em> [Preprint]. OSF. <a href="https://doi.org/10.31219/osf.io/6y2ze">https://doi.org/10.31219/osf.io/6y2ze</a></div> </div>
Functional Near-Infrared Spectroscopy Reveals Delayed Hemodynamic Changes in the Primary Motor Cortex During Fine Motor Tasks and Decreased Interhemispheric Connectivity in Parkinson's Disease Patients
<p>This dataset contains functional near-infrared spectroscopy (fNIRS) data from 20 patients with Parkinson’s disease and 20 age- and sex-matched healthy subjects without movement disorders. There are 3 folders, each corresponding to a different task: a 10-second finger-tapping task, a 2-minute walking task, and a 6-minute resting-state. When using this dataset, please cite our work:</p> <div> <div>Guevara, E., Rivas-Ruvalcaba, F. J., Kolosovas-Machuca, E. S., Ramírez-Elías, M., Zapata, R. D. de L., Ramirez-GarciaLuna, J. L., & Rodríguez-Leyva, I. (2024). Parkinson’s disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: A functional near-infrared spectroscopy study. <em>Neurophotonics</em>, <em>11</em>(2), 025004. <a href="https://doi.org/10.1117/1.NPh.11.2.025004">https://doi.org/10.1117/1.NPh.11.2.025004</a></div> <div> <div> <div>Guevara, E., Solana-Lavalle, G., & Rosas-Romero, R. (2024). Integrating fNIRS and machine learning: Shedding light on Parkinson’s disease detection. <em>EXCLI Journal</em>, <em>23</em>, 763–771. <a href="https://doi.org/10.17179/excli2024-7151">https://doi.org/10.17179/excli2024-7151</a></div> <div> <div> <div> <div> <div>Guevara, E., Kolosovas-Machuca, E. S., & Rodríguez-Leyva, I. (2024). Exploring motor cortex functional connectivity in Parkinson’s disease using fNIRS. <em>Brain Organoid and Systems Neuroscience Journal</em>, <em>2</em>, 23–30. <a href="https://doi.org/10.1016/j.bosn.2024.04.001">https://doi.org/10.1016/j.bosn.2024.04.001</a></div> </div> </div> </div> </div> </div> </div> </div>
Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication: insertion data and derivative models
<p>This data repository provides access to the LINE-1 (L1) insertion site data from Flasch, et al., 2019:<br><a href="https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub</a></p> <p>Please see file <strong>L1_actual_and_random_insertions_help.docx</strong> for more detailed information.</p> <p>Files <strong>weighted_model_142_102917_corrected.txt</strong> and <strong>weighted_model_142_102917_uncorrected.txt</strong> carry a list of all possible 7-mer insertion sites, one per row, with site weights calculated based on observed L1 insertion sites. The files are either corrected or uncorrected for the frequency of those sites as found in the human genome, respectively. A header line defines the columns.</p> <p>The weight files described above were used to construct the simulated <strong>hg19 </strong>insertions sets described below.</p> <p>Archive <strong>all_insertion_sets.tar</strong> contains a series of insertion files, each with the complete data set used to analyze insertions from the named cell line.</p> <p>Column 5 is the iteration number, where:</p> <ul> <li>iteration == 0 identifies the actual, observed insertions</li> <li>each iteration > 0 identifies one round of simulation, up to 10K total simulations</li> <li>each iteration has the same number of insertions</li> </ul> <p>Column 4 is the insertion number within each actual or simulated insertion set.</p> <p>Please see the companion Zenodo data set:<br>10.5281/zenodo.12538130<br>for more information about creating insertion site models from your own insertion data.</p>
Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication: creating new models
<p>This data repository provides code and general instructions for creating new LINE-1 (L1) insertion site models as described in Flasch, et al., 2019:<br><a href="https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S0092867419302338?via%3Dihub</a></p> <p>Please see file <strong>L1_Simulation_Files_help.docx</strong> for more detailed information.</p> <p>File <strong>L1Simulation_filelist.xlsx</strong> lists the files described in the Word file and found in <strong>L1Simulations.tar.gz</strong>.</p> <p>Required large file <strong>ChrmsALL_table_sortEN.bed.gz</strong> is found individually. Importantly, like the paper analysis, this file is specific to the <strong>hg19 </strong>version of the human genome.</p> <p>Please see the companion Zenodo data set<br>10.5281/zenodo.8233858<br>if you only want to access our insertion data and the resulting models we created.</p> <p> </p>
Earthquake Connection Indicator (ECI) table for the locations of the cult of Poseidon with earthquake-related epithets in the ancient Aegean Sea region
<p>The rationale behind this table is to categorize the spatial attachment of the evidence of the earthquake-related epithets of Poseidon to earthquake proxies in the region of the ancient Aegean Sea.</p> <h2>Attributes</h2> <h3><strong>Location</strong></h3> <p>States the location of a findspot of the evidence of an earthquake-related epithet of Poseidon.</p> <h3><strong>Epithet</strong></h3> <p>Identifies a specific earthquake-related epithet of Poseidon at a particular location.</p> <h3><strong>Earthquake_connection_indicator</strong></h3> <p>Each location of the cult of Poseidon with an earthquake-related epithet is attributed with a number from 1 to 3 based on its spatial ties to earthquake proxies. Earthquake proxies consist of active fault lines (Ganas, A., I. A. Oikonomou, and C. Tsimi. 2013. ‘NOAfaults: A Digital Database for Active Faults in Greece’. Bulletin of the Geological Society of Greece 47 (2): 518–30. https://doi.org/10.12681/bgsg.11079), and locations of ancient earthquake reports (Guidoboni, Emanuela, Graziano Ferrari, Gabriele Tarabusi, Giulia Sgattoni, Alberto Comastri, Dante Mariotti, Cecilia Ciuccarelli, Maria Giovanna Bianchi, and Gianluca Valensise. 2019. ‘CFTI5Med, the New Release of the Catalogue of Strong Earthquakes in Italy and in the Mediterranean Area’. Scientific Data 6 (1). https://doi.org/10.1038/s41597-019-0091-9; National Geophysical Data Center. 1972. ‘Global Significant Earthquake Database’. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5TD9V7K).</p> <p>ECI 1: A location with an attested cult of Poseidon with an earthquake-related epithet located more than 5 kilometers from the nearest active fault or an ancient earthquake report.</p> <p>ECI 2: A location with an attested cult of Poseidon with an earthquake-related epithet located within a 5-kilometer radius from an active fault line.</p> <p>ECI 3: A location with an attested cult of Poseidon with an earthquake-related epithet located either a) within a 5-kilometer radius from an active fault line with an ancient earthquake report anywhere along the particular fault; or b) within a 5-kilometer radius from an ancient earthquake report itself.</p> <h3><strong>Lat</strong></h3> <p>Latitude</p> <h3><strong>Long</strong></h3> <p>Longitude</p> <h3><strong>DB MAP testimony #</strong></h3> <p>ID number of a testimony of Poseidon with an earthquake-related epithet based on Bonnet C. (dir.), ERC Mapping Ancient Polytheisms 741182 (DB MAP), Toulouse 2017-2023: <a href="https://base-map-polytheisms.huma-num.fr/">https://base-map-polytheisms.huma-num.fr</a>. DOI: <a href="https://doi.org/10.34847/nkl.1e19sne6">https://doi.org/10.34847/nkl.1e19sne6.</a></p> <h3><strong>PHI ID</strong></h3> <p>IDs of inscriptions mentioning Poseidon with an earthquake-related epithet from the Searchable Greek Inscriptions (PHI, https://inscriptions.packhum.org/) as listed in the Greek Inscriptions in Space and Time dataset (GIST, Kaše, Vojtěch, Petra Heřmánková, and Adéla Sobotková. 2023. ‘GIST’. Zenodo. https://doi.org/10.5281/zenodo.10139110.). To search the ID at PHI, put the number at the end of the URL in the following format https://epigraphy.packhum.org/text/32602.</p> <h3><strong>Thely</strong></h3> <p>Indicates whether the evidence is listed in the book Thély, Ludovic. 2016. Les Grecs face aux catastrophes naturelles: savoirs, histoire, mémoire. Bibliothèque des Écoles françaises d’Athènes et de Rome : BEFAR. Athènes, Paris: École française ; diffusion De Boccard.</p>
ScienceDex guides
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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.
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.