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3,200 results for “indexicality”
The Standardized Vegetation Optical Depth Index SVODI
<p><strong>Related paper with detailed description: </strong><a href="https://doi.org/10.5194/bg-19-5107-2022">https://doi.org/10.5194/bg-19-5107-2022</a></p> <p><strong>Short summary:</strong> The Standardized Vegetation Optical Depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage and spans the past three decades and is derived from multiple space-borne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data, even if the data is not gap-free.</p> <p><strong>Files: </strong></p> <ul> <li>"SVODI_v01.zip" <ul> <li>Contains the bulk SVODI data, globally, from 1987-07-10 to 2019-12-31. Files are daily global netcdf images, with a 0.25 degree spatial resolution. Is unzipped roughly the same size.</li> </ul> </li> <li>"svodi_v01_0_2006-06-26.nc" <ul> <li>An arbitrary file from SVODI_V01.zip. For your convenience in case you want to see an example first without downloading the whole thing.</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Grid of SVODI, Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Data fields:</strong></p> <ul> <li>"svodi" <ul> <li>The standardized vegetation optical depth index</li> <li>unitless</li> <li>range: -inf, inf</li> </ul> </li> <li>"flag" <ul> <li>Bit-flag indicating which <sensor>-<band> combination contributed to each observation <ul> <li>1: AMSRE-C</li> <li>2: AMSR2-C</li> <li>3: WindSat-C</li> <li>4: AMSRE-X</li> <li>5: AMSR2-X</li> <li>6: WindSat-X</li> <li>7: TMI-X</li> <li>8: AMSRE-Ku</li> <li>9: AMSR2-Ku</li> <li>10: WindSat-Ku</li> <li>11: TMI-Ku</li> <li>12: SSMI-Ku</li> </ul> </li> </ul> </li> </ul>
A longitudinal study of the associations of children's body mass index and physical activity with blood pressure – dataset
<p>B-Proact1v is a longitudinal study examining changes in children’s physical activity and sedentary behaviours as they progress through primary school. In 2012-2013, 1299 Year 1 children (median age: 6 years) were recruited from 57 schools in greater Bristol, UK (total number of eligible children: 2600; recruitment rate: 50.0%). Following this, data were collected from 1223 Year 4 children (median age: 9 years) from 47 of the original schools between March 2015 and July 2016 (total number of eligible children: 2047; recruitment rate: 59.7%). This included 685 children from the original sample.</p> <p> </p> <p>This dataset represents a subset of the B-Proact1v data to examine the longitudinal associations of children’s body mass index and physical activity with blood pressure. Included in this repository is the dataset and a data dictionary. The dataset includes the variables that underlie the findings in a manuscript entitled ‘A longitudinal study of the associations of children’s body mass index and physical activity with blood pressure’ that has been submitted to PLOS ONE. This dataset has been made available so that future researchers can replicate the study findings using the data. If you wish to use the data for any purpose other than replicating the study findings, please contact the Principal Investigator Professor Russ Jago (russ.jago@bristol.ac.uk) to discuss this.</p>
SAM-ENSO Index (SEI)
<p>Note: This v.2 updates the SEI data up to the 01.03.2024 as new SAM and ENSO data became available.</p> <p>The SAM-ENSO climate index (SEI) provided here combines the Southern Anular Mode (SAM) index (Marshall, 2003) with the Oceanic Niño Index (ONI) (Bamston et al., 1997; Huang et al., 2016), taking into account that their opposing phases reinforce each other in their overlapping effects on the wind field around Antarctica (Fogt et al., 2011; McKee et al., 2011; Stammerjohn et al., 2008).</p> <p>\(SEI = {SAM \over std(SAM)} - {ONI \over std(ONI)}\)</p> <p>The SEI has been first employed in Llanillo et al. (2023).</p> <p> </p> <p><strong>References:</strong></p> <p>Bamston, A. G., Chelliah, M., & Goldenberg, S. B. (1997). Documentation of a highly enso-related sst region in the equatorial pacific: Research note. <em>Atmosphere - Ocean</em>, <em>35</em>(3), 367–383. https://doi.org/10.1080/07055900.1997.9649597</p> <p>Fogt, R. L., Bromwich, D. H., & Hines, K. M. (2011). Understanding the SAM influence on the South Pacific ENSO teleconnection. <em>Climate Dynamics</em>, <em>36</em>(7), 1555–1576. https://doi.org/10.1007/s00382-010-0905-0</p> <p>Huang, B., Thorne, P. W., Smith, T. M., Liu, W., Lawrimore, J., Banzon, V. F., Zhang, H. M., Peterson, T. C., & Menne, M. (2016). Further exploring and quantifying uncertainties for extended reconstructed sea surface temperature (ERSST) version 4 (v4). <em>Journal of Climate</em>, <em>29</em>(9), 3119–3142. https://doi.org/10.1175/JCLI-D-15-0430.1</p> <p>Llanillo, P.J., Kanzow, T., Janout, M. and Rohardt, G. (2023): The Deep-Water Plume in the northwestern Weddell Sea, Antarctica: Mean state, seasonal cycle and interannual variability influenced by climate modes. <em>JGR-Oceans (accepted).</em></p> <p>Marshall, G. J. (2003). Trends in the Southern Annular Mode from observations and reanalyses. <em>Journal of Climate</em>, <em>16</em>(24), 4134–4143. https://doi.org/10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2</p> <p>McKee, D. C., Yuan, X., Gordon, A. L., Huber, B. A., & Dong, Z. (2011). Climate impact on interannual variability of Weddell Sea Bottom Water. <em>Journal of Geophysical Research: Oceans</em>, <em>116</em>(5), 1–17. https://doi.org/10.1029/2010JC006484</p> <p>Stammerjohn, S. E., Martinson, D. G., Smith, R. C., Yuan, X., & Rind, D. (2008). Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and Southern Annular Mode variability. <em>Journal of Geophysical Research</em>, <em>113</em>(C3), C03S90. https://doi.org/10.1029/2007JC004269</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
The 0.1° stem area index dataset over Tibetan Plateau from 1981 to 2018
<p><strong>Description:</strong></p> <p>Based on the method of Zeng et al. (2002), we built the monthly stem area index (Ls) data product over the Tibetan Plateau (TP) at 0.1°×0.1° spatial resolution from 1981-09 to 2018-12 by using leaf area index data (LAI) from GLASS (Liang et al., 2021) and grass fractional cover data from Lawrence and Chase (2007). Here, We revised the method, considering that grass is completely green (no WGS) from May to August, the Ls,min is set to 0; in September when the grass begins to wither (Xiao et al., 2023), its Ls value is obtained by subtracting the Lgv in September from that in August; from October (when grass turns completely withered) to the April of next year, the Ls is calculated without adding of withered leaves, considering the small magnitude of LAI (<0.2) in non-growing season; the monthly remaining rate of withered leaves and stems (α) is obtained from the observed total area of leaf and stem data of Xiao et al. (2023), which actually represents the neutralization of monthly removal of dead leaves and the withering part, especially in October. The calculation of Ls starts from September, 1981. Based on the above, the Ls is calculated on the sub-grid scale, then it multiplies by the fractional vegetation cover of grass to obtain the stem area index on the grid scale.</p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 75º~105ºE, 25º~40ºN;</p> <p> Temporal Coverage: Sep. 1981-Dec. 2018;</p> <p> Spatial Resolution: 0.1º;</p> <p> Temporal Resolution: Monthly;</p> <p> Data Format: NetCDF.</p> <p><strong>Citation</strong><strong>:</strong></p> <p>Qi, Q., Yang, K., Li, H., Ai, L., Wang, C., Wu, T. (2024). Negative impacts of the withered grass stems on winter snow cover over the Tibetan Plateau. Agric. For. Meteorol., 352, 110053. https://doi.org/10.1016/j.agrformet.2024.110053</p> <p>If you have any questions, please contact <strong>Dr. Kai Yang (yangkai@lzu.edu.cn)</strong></p>
A dataset of trophic state index for nation-scale lakes in China
<p>The trophic state index (TSI) serves as a key indicator for understanding the spatial pattern and temporal variation in lake eutrophication, which has not been fully explored for long-term environmental monitoring, especially for small and medium lakes. Landsat satellites provide the capability of monitoring the temporal and spatial distribution of small lakes with a 30 m spatial resolution, which offers an effective complement. Here we generated a dataset of TSI for lakes in China (CNLTSI) using Landsat archives (Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI) from 1984 to 2023, which contains 2693 predominantly small to medium nationwide lakes in China in the past 4 decades. The dataset produced has a spatial resolution of 30 m and varying temporal resolution due to differences in satellite overpasses and cloud interference. </p>
Monthly Standardized Precipitation Evapotranspiration Index (SPEI) for Australia at 0.05 degree from 1982 to 2014
<p>This monthly SPEI dataset in 1-48 scale is calculated using R's <a href="https://cran.r-project.org/web/packages/SPEI/index.html">SPEI </a>package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with <a href="http://www.csiro.au/awap/">AWAP'</a>s monthly rainfall and <a href="http://www.bom.gov.au/water/landscape/">ALWB</a>'s potential evapotranspiration.</p>
Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research"
<p>Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research" published in <em>Frontiers in Marine Science</em></p>
Air Quality Index Scores by CBSA with Population
<p>The AQI describes the five main types of air pollution regulated by the Clean Air Act: sulfur dioxide, nitrogen dioxide, carbon monoxide, ground-level ozone, and particle pollution. The EPA and its partners take regular readings of these pollutants and converts the results into a number ranging from 0 to 500, along with a specific color corresponding to a level of health concern. Generally, if the air quality is good, the air quality index is low (0 to 50) or moderate (51-100), and the color associated with it is green or yellow. As the air quality gets worse, the numbers go up, and the color linked with it goes from orange, to red, to purple, all the way to a dark shade of maroon for hazardous (300+).</p> <p>This dataset contains the AQI scores by metropolitant area (CBSA) during 2017. I've enhanced some publically available data from the EPA's <a href="https://airnow.gov/">airnow</a> website with census data, to be able to provide context about the number of people who are actually impacted when an <a href="https://www.cleanairresources.com/resources/how-do-i-read-the-air-quality-index">AQI score</a> is high or low in a given area.</p> <p>Related datasets on <a href="https://www.cleanairresources.com/data#sources">relative composition of air pollution by source: typical distribution and during wildfire season</a> are available here.</p>
Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data
<p>This is a release of the Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data.<br> <br> ERuDIte contains over 11,000 training resources on data science including courses (MOOCs), video tutorials, conference talks, and other materials. The metadata of these resources is described uniformly using schema.org. In addition, we use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology (DSEO), which we developed to further describe the contents of the training resources. Resource relevance and tags are curated by experts to ensure high quality. Finally, we map the references to people and organizations in the learning resource metadata to entities in DBpedia, DBLP, and ORCID, thus embedding our collection in the web of linked data. Our collection is continually growing. We hope that ERuDIte will provide a framework to foster open linked educational resources on the web.<br> <br> Distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/)</p>
Global Soil Moisture Agricultural Drought Index (SMADI)
<p><br> This repository contains the global Soil Moisture Agricultural Drought Index (SMADI) spanning from June 2010 to December 2015 at fortnightly (bi-weekly) rate. The product is gridded in a 0.05deg Climate Modeling Grid.<br> <br> The data are provided in Matlab format (.mat). Each file is a 3600x7200 matrix size, where 3600 is the number of pixels in Latitude coordinates and 7200 to Longitude coordinates, both in the WGS84 system. The center-pixel coordinates are variables "Latitude" and "Longitude". No data values (NaN) correspond to pixels where the retrieval was not possible.<br> <br> <br> SMADI is computed using satellite time series of MODIS Normalized Difference Vegetation Index (NDVI, computed from daily reflectances MOD09CMG v.6), MODIS Land Surface Temperature (LST, product MOD11C1 v.6), and SMOS BEC L3 soil moisture data v2.0 (which corresponds to SMOS L2 v.620, average of ascending and descending orbits). <br> <br> The IGBP land cover (MODIS MOD12C1 product) is used prior to SMADI calculation to select only grassland and cropland/natural vegetation mosaic pixels as representative of the primary agro-ecosystems.</p>
Heatwave indexes 1980-2018
<p>Heatwaves were defined as minimum temperature exceeding the 99th percentile of minimum temperature for the reference period (1986-2005) for four or more days.</p> <p>From this definition, four heatwave indicators are derived:</p> <p>Number of heatwave instances</p> <p>Total number of days of heatwave</p> <p>Heatwave degree-days over threshold, being the sum of the differences between the daily temperatures and the 99th percentile threshold</p> <p>Mean length of heatwaves</p> <p>Heatwave mean degrees over threshold, being the heatwave degree-days divided by the total number of days.</p> <p> </p> <p>These definitions were applied to ECMWF ERA5 temperature data at 0.5˚ grid resolution for the years 1980-2018. Each indicator is provided in a separate file.</p> <p> </p>
An Index to Roscher's Lexicon of Mythology
<p>compiled by Jonathan Groß <br><a href="https://orcid.org/0000-0002-2564-9530" target="_blank" rel="noopener">ORCID 0000-0002-2564-9530</a> <br><a href="mailto:jgross85@gmail.com" target="_blank" rel="noopener">jgross85 [AT] gmail [DOT] com</a></p> <h2>1. Introduction</h2> <h3>1.1. General Disclaimer</h3> <p>This index file was created as a private research project with the goal to make the wealth of information in Wilhelm Heinrich Roscher's "Detailed Lexicon of Greek and Roman Mythology" (Ausführliches Lexikon der griechischen und römischen Mythologie) more accessible to everybody.</p> <p>Roscher's Lexicon, originally published by B. G. Teubner in Leipzig from 1884 to 1937, is the most complete resource on Greek and Roman mythological names to date and also encompasses mythological (and religious) subjects from Sumeran, Akkadian, Babylonian, Hittite, Egyptian, Celtic, Germanic and other neighbouring cultures.</p> <p>The Lexicon was reprinted three times in the latter half of the 20th century (by Georg Olms in Hildesheim), even after its pictorial content had been superseded by the Lexicon Iconographicum Mythologiae Classicae (1981–1999, 2009). Unfortunately, since the last reprint of 1992/1993, Roscher's Lexicon has been out of stock at both publishers (Olms and De Gruyter Brill).</p> <p>Since the late 2000s, Roscher's Lexicon (the 6 main volumes and 4 supplements) was digitised by initiatives such as Google Books and the Internet Archive, and its contents can now be viewed there (with OCR text). One prominent use case of these scans is the German Wikipedia, where more than 2,500 pages use Roscher's Lexicon as a reference with a link to a scanned page in the Internet Archive.</p> <h3>1.2. Licensing</h3> <p>This dataset is released under the CC0 1.0 Universal License (https://creativecommons.org/publicdomain/zero/1.0/deed.en). I chose this license in order to maximise the usefulness of the data to everybody.</p> <p>Use and reuse of this data is strongly encouraged, and one use case has already been initiated by the author:<br>-https://mythogram.wikibase.cloud/wiki/Project:Roscher%27s_Lexicon_of_Mythology (presentation of the information from the index file as Linked Open Data, finished on 28 May 2024 with emendations until 5 August 2024)</p> <p>Although not technically required by the licensing agreement, the author would appreciate being informed about other uses of the data.</p> <p>The contents of the Lexicon themselves are mostly in the Public Domain as of 2024. Additionally, many of the smaller entries do not reach the threshold of originality. This includes most of the cover addenda.</p> <h2>2. Description of Data</h2> <p>The index file is formatted as tabular data. This file was created with LibreOffice Calc (originally in 7.6.6.3, in LibreOffice Calc 24.2 as of version 1.1 of this file) and is stored in its native .ods format. For convenience, an .xlsx version is also provided. Both files are practically identical, but the .ods file is to be regarded as the 'original'.</p> <p>Data is stored in several tabs:<br>(A) 'main alphabet' with the headwords of the main work (excluding addenda and corrigenda from the covers; for these see below).<br>(B) 'cover addenda' with the additional entries<br>(C) 'authors' with information on the authors<br>(D) 'fascicles' with information on the individual issues of the Lexicon</p> <p>The Tabs are available separately as .csv files (with tab separation, so strictly speaking it should be .tsv).</p> <p>Tabs A and B are almost identical in structure, with the columns:<br>A id = unique entry ID (not authoritative, just a means to identify individual entries)<br>B headword = lemma of the entry as stated by the Lexicon<br>C subject_type = classification scheme for the subject matter of the article (again, not authoritative and in places even contentious)<br>D vol = volume number<br>E fascicle = issue number (not found in most exemplars, assigned according to my own research)<br>F date = publication date of the entry (inferred from the issue date of the fascicle)<br>G–H col1,2 = start and end column<br>I colspan = span of columns<br>J–M author1,2,3,4 = author of the entry (please refer to Tab C, column A)<br>N entry_type = classification of entry (article, cross-reference, addendum, correction)<br>O scan = URL to a scan of the start column in the Internet Archive<br>P Wikidata = ID of the Wikidata item representing the subject (incomplete as of Version 1.1)<br>Q FactGrid = ID of the FactGrid item representing the subject (mostly missing as of Version 1.1)<br>R Mythogram = ID of the (bibliographic) Mythogram item representing the Lexicon entry<br>S redirect_target = target headword as stated, if the entry is a cross-reference<br>T remarks = remarks on the entry or subject (such as 'non-entity', 'duplicate', 'double lemma')<br>U PD = if the entry is in the Public Domain (either 'yes' or year where it enters the PD)</p> <p>Tab B has two additional columns, which are mostly empty as of version 1.1<br>V referring_to = target entry (in the main alphabet) of the correction or addenda<br>W excerpt = textual excerpt from the entry</p> <p>Tab C has information on the authors:<br>A short_name = for sorting reasons<br>B full_name = full name<br>C Wikidata = Wikidata item<br>D FactGrid = FactGrid item<br>E Mythogram = Mythogram item<br>F yob = year of birth<br>G yod = year of death<br>H vols = volumes contributed to<br>I article_count = number of articles written (not counting corrections and addenda from Tab B)<br>J–L namestring1,2,3 = name as written in the Lexicon<br>M remarks = remarks on completeness and certainty of data</p> <p>Tab D informs about the individual fascicles of the Lexicon as they appeared from 1884 to 1937:<br>A no. = fascicle number<br>B vol = volume(s) the fascicle belongs to<br>C colspan = column span of the fascicle<br>D headwords = headwords contained in the fascicle as advertised on the cover page<br>E issue_date = date of publication of the fascicle as stated on the cover page<br>F quires = quire numbers of the fascicle<br>G quire_count = quire count of the fascicle (calculated from column numbers: in some cases, at the end of a volume, quires were shortened, returning rational numbers here)<br>H remarks = remarks (in German)</p> <h2>3. Version History and Change Log</h2> <p>--------------------------------------------------<br>Version 1.1 (August 11th, 2024)<br>-Tab A, column P (Wikidata Q-ids): added 2,380 out of 15,489 = 15.4%)<br>-Tab A, column R (Mythogram Q-ids): completed<br>-Tab C: added data for author Wilhelm Windisch (translated Cumont's article on 'Mithras')<br>-minor corrections to some entries (typos)<br>-volume number changed from 3.2 to 3.1 for 125 entries (Pasikrateia–Peirithoos)<br>-fascicle number changed from 104/105 to 106/107 for the last 12 entries (Tameobrigus–Kerberos [Nachtrag])<br>-addition of 11 missed entries, values in column A renumbered accordingly</p> <p>--------------------------------------------------<br>Version 1.0 (May 4th, 2024)<br>-Tabs A–B with complete and checked data for columns A–N and S<br>-Tab A with complete data for column O<br>-Tabs C and D with complete data</p> <p>--------------------------------------------------<br>Prior to publication:</p> <p>-collection and checking of data (roughly 376 hours of work, started in July 2023 and finished on Star Wars Day 2024)</p>
answered questionnaire to Bachelor Thesis "Wie sinnvoll ist die Ergänzung des Resource Discovery Systems an der Bibliothek des Max-Planck-Instituts für evolutionäre Anthropologie durch einen zusätzlichen, externen Index?"
<p>The dataset contains the answers that were given in the online questionnaire that was conducted as part of the Bachelor Thesis "Wie sinnvoll ist die Ergänzung des Resource Discovery Systems an der Bibliothek des Max-Planck-Instituts für evolutionäre Anthropologie durch einen zusätzlichen, externen Index?"</p> <p>The questionnaire and further information can be found in the Bachelor Thesis, which is linked uner Related Works.</p>
QIceRadar Antarctic Index of Radar Depth Sounding Data
<p>Database of known Antarctic radar depth sounding data. </p> <ul> <li>qiceradar_antarctic_index.gpkg - Database with ground tracks, citation info, and URLs of available data</li> <li>qiceradar_antarctic_index.qlr - QGIS style file that organizes the transects into campaign/institution and styles them based on radargram availability.</li> </ul> <p>Download both files to the same directory, then drag qiceradar_antarctic_index.qlr into QGIS.</p> <p>(Do not rename them!)</p> <p>----------</p> <p>v0.2.0: Removed outlier points from CReSIS & UTIG surveys; imported tracks of SOAR Vostok survey</p> <p>v0.1.0: Support for BAS, CReSIS and most released UTIG data. Still missing some citations/references, waiting to hear back from data providers.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Law Indexes: Pennsylvania
<p>As part of efforts to expand the Local Geohistory Project, which aims to educate users and disseminate information concerning the geographic history and structure of political subdivisions and local government, this repository has been created to disseminate law index data for the Commonwealth of Pennsylvania. This index currently covers a portion of the private and special laws only from 1700 through 1892.</p><p>Private and special laws concern one or several individuals, entities, or localities, unlike public or general laws, which apply to all similarly situated individuals, entities, and localities within a jurisdiction. Prior to the Constitution of 1874, private and special laws were a common method used to alter municipal and county boundaries and forms of government. The law index is released as a tab-separated values (TSV) file, <strong>output/PaLawIndex.tsv</strong>.</p><p>This repository does not contain the full text of the laws, nor does it currently contain links to the full text. Because the index was created using OCR technology, it may contain uncaptured errors. This preliminary release is subject to additional caveats explained in the Methodology.</p>
CD5 index of works published in the period 1945-2018
<p>The xz-compressed data file (2.7 GiB uncompressed) contains 44,008,797 records comprising two fields separated by a "|" character:</p> <ol> <li>Publication DOI</li> <li>Five-year consolidation / disruption (CD₅) index (when available)</li> </ol> <p>The data set was derived from the <a href="https://www.crossref.org/blog/2024-public-data-file-now-available-featuring-new-experimental-formats/">Crossref 2024 public data file</a> using <a href="https://github.com/dspinellis/alexandria3k">Alexandria3k</a> and <a href="https://github.com/dspinellis/fast-cdindex">fast-cdindex</a>. More information about the process can be found in the following papers.</p> <ul> <li>Diomidis Spinellis. Open reproducible scientometric research with Alexandria3k. <em>PLoS ONE</em>, 18(11):e0294946, November 2023. <a href="https://dx.doi.org/10.1371/journal.pone.0294946">doi:10.1371/journal.pone.0294946</a></li> <li>Diomidis Spinellis. Efficient graph processing. <em>IEEE Software</em>, 42(1):22–25, January 2025. <a href="https://dx.doi.org/10.1109/ms.2024.3477013">doi:10.1109/ms.2024.3477013</a></li> </ul> <p>In common with the latest revision of the previously released 1945–2016 data set, this one incorporates into the calculation works lacking a reference list, but not calculating a CD₅ index for them. It also excludes works published after 2018, as they lack five years of citations to them.</p>
Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach
<p>Main data used for the scientific paper entitled: "Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach".</p> <ol> <li>"Danta_dem_10cm_px.tif": orthomosaic-derived DEM</li> <li>"Danta_rgb_2.2cm_px.tif": ortophoto </li> <li>"GPS points": list of GPS samples points</li> <li>"Main data": field vegetation data and indexes used for the regressions</li> <li>"Raw PointCloud". Lidar original dataset</li> <li>"Pre-processed PointCloud": Lidar dataset after pre-processing (see paper's methods) </li> <li>"DTM_DantaGround_grid50cm_minimo": Output (TIFF); the LiDAR-derived DTM showed in the paper</li> <li>"LAI": Output (Shapefile); the LiDAR-derived LAI showed in the paper.</li> </ol> <p> </p>
Traffic network routing index for the Czech Republic
<p><strong>Graph representation of road network of the Czech Republic</strong></p> <p>This dataset contains graph of the entire Czech road network. The data are derived from the Open Street Map project and stored in a HDF5 file. The file contains graph topology and metadata for edges and vertices. </p> <p>Spatial index is also included for the purpose of point snapping and other spatial queries. The spatial index is stored in a SQLite file with SpatiaLite extension.</p> <p>Creation of this dataset has been supported by the <a href="http://antarex-project.eu">Antarex project</a>.</p> <p><strong>Routing Index in HDF5</strong></p> <p><strong>File: </strong><a href="/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180724134457.hdf?versionId=3294ae0b-c3b2-4155-ad36-311d2bb06ed9">CZE-1528295206-proc-20180724134457.hdf</a></p> <p>The index is divided into parts according to geographical boundaries defined by country borders. In this case it contains only single part - CZE. This information along with the creation time is stored as an attribute in the root group of the file.</p> <p>The graph topology is stored in the following way. Nodes have assigned a row index in the edges dataset which points to an outbound edge plus a number of the subsequent edges which are also output to this node. The edge metadata are stored in the EdgeData dataset to avoid redundancy. NodeMap dataset provides a convenient way to query nodes based on their uniqe identifiers.</p> <p><strong>File structure:</strong></p> <pre><code class="language-javascript">HDF5 "CZE-1528295206-proc-20180724134457.hdf" { GROUP "/" { GROUP "Index" { ATTRIBUTE "CreationTime" { DATATYPE H5T_STD_I64LE DATASPACE SCALAR DATA { (0): 1535451896 } } ATTRIBUTE "PartsCount" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 1 } } ATTRIBUTE "PartsInfo" { DATATYPE H5T_COMPOUND { H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "id"; H5T_STD_I64LE "nodeCount"; H5T_STD_I64LE "edgeCount"; } DATASPACE SIMPLE { ( 1 ) / ( 1 ) } DATA { (0): { "CZE\000", 904085, 2223222 } } } GROUP "CZE" { ATTRIBUTE "PartInfo" { DATATYPE H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "CZE\000" } } DATASET "Edges" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "edgeId"; H5T_STD_I32LE "nodeIndex"; H5T_STD_I32LE "computed_speed"; H5T_STD_I32LE "length"; H5T_STD_I32LE "edgeDataIndex"; } DATASPACE SIMPLE { ( 2223222, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "Nodes" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_I32LE "latitudeInt"; H5T_STD_I32LE "longtitudeInt"; H5T_STD_U8LE "edgeOutCount"; H5T_STD_I32LE "edgeOutIndex"; H5T_STD_U8LE "edgeInCount"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } DATASET "EdgeData" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_U8LE "speed"; H5T_STD_U8LE "funcClass"; H5T_STD_U8LE "lanes"; H5T_STD_U8LE "vehicleAccess"; H5T_STD_U16LE "specificInfo"; H5T_STD_U16LE "maxWeight"; H5T_STD_U16LE "maxHeight"; H5T_STD_U8LE "maxAxleLoad"; H5T_STD_U8LE "maxWidth"; H5T_STD_U8LE "maxLength"; H5T_STD_I8LE "incline"; } DATASPACE SIMPLE { ( 2838, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "NodeMap" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "nodeId"; H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "partId"; H5T_STD_I32LE "nodeIndex"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } } }</code></pre> <p><strong>Spatial index</strong></p> <p><strong>File: </strong><a href="https://zenodo.org/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180829135251.sqlite?versionId=56bb1e60-ff7c-407f-8041-3c628ef78db0">CZE-1528295206-proc-20180829135251.sqlite</a><br> </p> <p>SQLite database contains two primary tables. Table <em>nodes</em> and table <em>segments</em>, that resluts of selection and projection from the tables of the same name in primary db. Both tables are suitable for <em>searching nearest lines</em> task and <em>searching closest node of nearest line</em> task. </p> <pre><code class="language-sql">CREATE TABLE nodes ( gid INTEGER, part_gid INTEGER, node_type INTEGER, geom POINT ); CREATE TABLE segments ( gid INTEGER, node_gid_from INTEGER, node_gid_to INTEGER, frc TEXT, transition_time DOUBLE, computed_speed REAL, geom_length DOUBLE, geom LINESTRING ); </code></pre> <p>There are three more tables. Table <em>segments_rt</em> is a copy of table <em>segments</em>, that exclude loops in segments, hence it supports network routing task in SpatiaLite. Next table is <em>rt_network</em> (static graph generated from table <em>segments_rt</em> suitable for routing). The last one is table <em>virtual_rt_network</em>, that is an interface for routing query. </p> <p> </p>
Compressed COBS indexes (XZ) - Full 661k - Part 2
<p><strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – <a href="https://doi.org/10.5281/zenodo.7313926">part1</a>, <a href="https://doi.org/10.5281/zenodo.7313942">part2</a>, <a href="https://doi.org/10.5281/zenodo.7315499">part3</a></p> <p>Part 2 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on <a href="https://brinda.eu/mof/">https://brinda.eu/mof/</a>.</p> <p><strong>Citation:</strong></p> <blockquote> <p>K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <a href="https://doi.org/10.1101/2023.04.15.536996"><strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong></a>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong><a href="https://doi.org/10.1101/2023.04.15.536996">https://doi.org/10.1101/2023.04.15.536996</a></strong></p> </blockquote>
Compressed COBS indexes (XZ) - Full 661k - Part 1
<p><strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – <a href="https://doi.org/10.5281/zenodo.7313926">part1</a>, <a href="https://doi.org/10.5281/zenodo.7313942">part2</a>, <a href="https://doi.org/10.5281/zenodo.7315499">part3</a></p> <p>Part 1 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on <a href="https://brinda.eu/mof/">https://brinda.eu/mof/</a>.</p> <p><strong>Citation:</strong></p> <blockquote> <p>K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <a href="https://doi.org/10.1101/2023.04.15.536996"><strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong></a>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong><a href="https://doi.org/10.1101/2023.04.15.536996">https://doi.org/10.1101/2023.04.15.536996</a></strong></p> </blockquote>
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.