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3,788 results for “indicators”
Dataset Reuse Indicators Datasets
<p>This dataset contains two files. </p> <p>1) A python pickle file (github_dataset.zip) that contains Github repositories with datasets. Specifically, using Google’s public dataset copy of Github and the BigQuery service to build a list of repositories that have a CSV or XLSX or XLS file. We then used the GitHub API to collect nformation about each repository in this list. The resulting dataset consists of 87936 repositories that contain at least a CSV, XLSX or XLS file, alongside with information about their features (e.g. number of open and closed issues and license) from GitHub. This corpus had more than two million data files. We then excluded those files withless then ten rows, which was the case for 65537 repositories with a total of 1,467,240 data files.</p> <p>2) A python pickle file (processed_dataset.zip) containing the feature information necessary to train a machine learning model to predict reuse on these Github datasets</p> <p>Source code can be found at: <a href="https://github.com/laurakoesten/Dataset-Reuse-Indicators">https://github.com/laurakoesten/Dataset-Reuse-Indicators</a></p> <p>For a full description of the content see:</p> <p>Koesten, Laura and Vougiouklis, Pavlos and Simperl, Elena and Groth, Paul, Dataset Reuse: Translating Principles to Practice. Available at SSRN: <a href="https://ssrn.com/abstract=3589836">https://ssrn.com/abstract=3589836</a> or <a href="https://dx.doi.org/10.2139/ssrn.3589836">http://dx.doi.org/10.2139/ssrn.3589836</a></p>
JCR Journals, sorted by Impact Factor 2011 with the JCR edition indication
Description of the spreadsheet: “Journals in JCR sorted by IF’11” lists the journals from Thomson Reuters JCR website; it’s sorted by edition (science and social science) and Impact Factor 2011 descending (but not difunded). Fields: Abbreviated Journal title, ISSN, JCR ed. Methodology: 1. We copy and paste from the web pages the list in a unique spreadsheet. 2. We agregate the JCR edition: SCI=1 and SSCI=2. 3. We sort by Edition and Impact Factor and delete this column values. 4. We upload the excel file to data banks
Table of Indications and Regimens from the National Cancer Control Programme, Ireland
<h4>Description:</h4> <p>A table containing all indications published by the National Cancer Control Programme (NCCP), Ireland. Each entry has an indication code, description, and disease; regimen code, name, and URL; and information regarding whether the indication contains molecular diagnostic criteria. Entries were last updated from the NCCP website on 2025-May-27.</p> <h4>Headings:</h4> <ul> <li>IndicationCode: NCCP indication code (ex: 00537a).</li> <li>IndicationDesc: Description of the indication taken from its relevant regimen (ex: "Monotherapy for the treatment of adults with relapsed or refractory CD22-positive B cell precursor acute lymphoblastic leukaemia (ALL). Adult patients with Philadelphia chromosome positive (Ph+) relapsed or refractory B cell precursor ALL should have failed treatment with at least 1 tyrosine kinase inhibitor (TKI).")</li> <li>CancerType: Manual annotation of disease category for the indication (ex: Leukaemia).</li> <li>HasGeneticCriteria: Manual TRUE/FALSE annotation indicating the presence or absence of genetic criteria for the indication, as described in the indication description or regimen document.</li> <li>GeneticCriteria: If HasGeneticCriteria is TRUE, the relevant criteria listed (ex: BCR-ABL1 positive).</li> <li>HasBiomarkerCriteria: Manual TRUE/FALSE annotation indicating the presence or absence of cellular biomarker criteria for the indication, as described in the indication description or regimen document.</li> <li>BiomarkerCriteria: If HasBiomarkerCriteria is TRUE, the relevant criteria listed (ex: CD22+).</li> <li>HasMolecularCriteria: For convenience, column stating TRUE if HasBiomarkerCriteria is True or HasGeneticCriteria is True.</li> <li>RegimenCode: NCCP code for the regimen associated with the indication (ex: 537).</li> <li>RegimenName: NCCP regimen name (ex: Inotuzumab ozogamicin Monotherapy)</li> <li>NCCPRegimenCategories: NCCP disease categories associated with the regimen (ex: Leukaemia/BMT).</li> <li>RegimenURL: URL to the NCCP regimen document.</li> <li>Notes: Miscellaneous notes containing notes from NCCP regimen documents or further explanations.</li> </ul> <p><br> </p>
Accessibility Indicators to services at EU scale - 1km grid indicators
<p>This archive makes available <strong>accessibility indicators at EU scale from populated 1km EU grid to towns and cities at EU scale</strong> (512 million travel time by car calculated between origins and destinations). It follows a reproducible, transparent and updatable framework. It uses <strong>only open source and free routing engines (OSRM)</strong>, based on OpenStreetMap (OSM) network. This routing engine makes possible the creation of travel time indicators for a large set of origins and destinations.</p> <p>The EU towns and cities layer has been recently made available and named by the European Commission. This layer is based on a <a href="https://ec.europa.eu/regional_policy/information-sources/maps/urban-centres-towns_en">common methodology</a> for all Europe. Within GRANULAR activities, we consider the towns and cities layer as <strong>a proxy</strong> to discuss on little and medium commercial centralities in Europe.</p> <p>This methodological framework, <strong>implemented with open source solutions (data and code) only and documented in a reproducible way in R notebooks</strong>, could be easily extended to other origins and destinations, if a relevant layer will be identified in the future.</p> <p>Based on travel time matrix, it is possible to compute a large set of indicators. This archive (see readme at the root folder) <strong>describes the input data used, summarises the data processing and provide information and metadata on output indicators created at 1km grid cells.</strong></p> <p>All the output data is also available. </p> <p> </p>
Global Phytoplankton Phenological Indices - 25km resolution
<p>Bloom phenology metrics calculated from OC-CCI v6.0 data at a spatial resolution of 25km. </p> <p>Metrics calculated using three approaches:</p> <ol> <li>Threshold method</li> <li>Cumulative Sum method</li> <li>Rate of Change method</li> </ol> <p> </p> <p><strong>Version 1.0:</strong></p> <ul> <li>Data from 1997 to 2022.</li> </ul> <p><strong>Version 1.1:</strong></p> <ul> <li>Data from 1997 to 2023.</li> </ul> <p> </p>
Relevance of criteria and indicators for sustainable timber harvesting
<p>For well-founded decisions in sustainable timber harvesting, it is important to know the preferences of the different stakeholders. The concept of sustainable timber harvesting is to incorporate economic, social, and environmental criteria in the selection, execution and assessment of harvesting operations. In a previous study, 33 criteria were identified by forest experts as relevant for evaluating sustainability. To assess the importance of these criteria, an online survey was conducted among Austrian stakeholders between April and May 2023, in which 610 people were invited to participate and which resulted in a response rate of 47%.</p> <p>The survey participants were primarily male (94%), with an average age of 47 and an average of 20 years of work experience. The key criteria for sustainable harvesting that were unanimously mentioned by the stakeholders on the basis of a Likert scale, included occupational hazards, residual stand damage, loss of wood quality due to poor work performance, biomass regeneration, water erosion, noise exposure, soil rutting, physical workload, working conditions, and vibration exposure. These identified preferences will inform the development of a decision support model for sustainable timber harvesting using these criteria as input parameters.</p>
European Mean Target Achievement (MTA) Indicator
<p>The EU MTA indicator is one way of assessing the benefits of protected area expansions using information of covered species distributions. It can be interpreted as the average number of species or habitats that are adequately (indicated through a target) conserved by conservation areas (Natura 2000 and/or CDDA sites) within the European union.</p> <p>This repository contains the created MTA indicator values created from an intersection of the Natura2000 and CDDA databases with the sensitive Article 12 and 17 reporting data.</p> <p><strong>Filename explanation: </strong></p> <p>MTA_{target}_{scale}_{directive}_{biodiversity}_{version}.csv</p> <table> <tbody> <tr> <td>target</td> <td>Which target was used for calculations. Currently: 'loglinear'</td> </tr> <tr> <td>scale</td> <td>Which datasets from the reporting was used. Supported currently are EU and MS</td> </tr> <tr> <td>directive</td> <td>Subset either for 'All' species and habitats, or just 'Art12' or 'Art17' data</td> </tr> <tr> <td>biodiversity</td> <td>Subset either for 'All', 'species' or 'habitats' respectively</td> </tr> <tr> <td>version</td> <td>A unique version number for each upload identical with the Zotero version.</td> </tr> </tbody> </table> <p> </p> <p>For further information and a detailed factsheet can be found on the online dashboard here:<br><a title="Online dashboard" href="https://martin-jung.github.io/EUMTA/dashboard.html">https://martin-jung.github.io/EUMTA/dashboard.html</a> </p> <p>---</p> <p>* The MTA indicator calculation and the creation of this dashboard contributes to WP7 of the NaturaConnect project. The information here is provided free of charge and the project takes no responsibility for errors or misuse.</p>
The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America
<p><strong>Title: </strong>The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America.</p> <p><strong>Authors:</strong> Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz.</p> <p><strong>Contact:</strong> Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>27 Jan 2022 - MANVI v2 was released!</strong> All data were reprocessed and improved. It is advised to re-download the whole series instead of combining v1 and v2. The dataset now covers years 2000-2021.</p> <p><strong>23 May 2019 - MANVI v1 was released.</strong> It covers years 2000-2018.</p> <p> </p> <p><strong>Data:</strong> MODIS (MAIAC) EVI and NDVI indices</p> <p><strong>Scale factor</strong>: 10000</p> <p><strong>Coverage:</strong> South America land</p> <p><strong>Time period:</strong> 2000 to 2021 (starting in 2000, Julian day 64)</p> <p><strong>Spatial resolution:</strong> 1 km</p> <p><strong>Temporal resolution:</strong> 16 days</p> <p><strong>Coordinate reference system:</strong> geographic projection, datum WGS-84</p> <p><strong>Processing details:</strong></p> <ul> <li>The original MODIS (MAIAC) data were described by Lyasputin et al. 2011 (<a href="https://doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>). The daily MODIS (MAIAC) surface reflectance data from collection 6, acquired from Terra and Aqua satellites, are available from the MCD19A1 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1</a>). The Bidirectional Reflectance Distribution Function (BRDF) model parameters are available from MCD19A3 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3</a>)</li> <li>The daily MCD19A1 data at 1 km spatial resolution were normalized using the BRDF parameters and Ross-Thick Li-Sparse (RTLS) model considering a fixed nadir view and a 45 deg. solar zenith angle using the parameters from the MCD19A3 product</li> <li>The daily data were aggregated into 16-day composites by the pixel’s median. The 16-day composites always start from Day Of Year (DOY) 016 and end with DOY 352. Therefore, the remaining days from 352 to 365/366 were not used. This procedure was used to facilitate inter-annual comparisons</li> <li>The tiles that cover the South America were mosaicked and re-projected from sinusoidal to geographic projection</li> <li>The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated using standard formulas. The EVI parameters were: C1 = 6, C2 = 7.5, L = 1, G = 2.5</li> </ul> <p><strong>File(s) format:</strong></p> <ul> <li>Zip files for EVI and NDVI - one per year: <ul> <li>Inside them there are raster files with ".tif" format, one per 16-day window. The filename syntax is "maiac_southamerica_DATA_YYYYDOY.tif", where YYYY is the year (e.g. 2000), and the DOY is the Julian day of the last day of the composite window, i.e. YYYYDOY for January 2005 for DOY from 001 to 016 is 2005016, from DOY 017 to 032 is 2005032, etc.</li> </ul> </li> <li>Csv files with the YYYYDOY and "real" dates for the time period</li> </ul> <p><strong>Code:</strong> <a href="https://github.com/ricds/maiac_processing">https://github.com/ricds/maiac_processing</a></p> <p><strong>Acknowledgements:</strong> This work was funded by São Paulo Research Foundation – FAPESP, Brazil, grant 2015/22987-7. We thank NASA, and especially Yujie Wang and Alexei Lyapustin, for providing the freely available MODIS (MAIAC) data.</p> <p> </p> <p><strong>Dataset usage</strong>: This dataset is a product of the first author's PhD work and lots of hours of coding and patience. It is free to use, but if you use this dataset in your work, please make sure to properly cite the repository. We also welcome users to invite us for collaboration.</p> <p> </p> <p><strong>For use of this dataset please cite:</strong></p> <p>Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America". (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3159487">https://doi.org/10.5281/zenodo.3159487</a></p> <p> </p> <p><strong>More information: </strong>contact Ricardo Dalagnol (ricds@hotmail.com). We also have the MODIS (MAIAC) BRDF-corrected bands 1-8, EVI, NDVI at 1 km with 16-day and monthly aggregation composites.</p>
Replication data for: "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?"
<p>The dataset accompanies the scientific article "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?" and can be used to reproduce the findings presented in this article. This dataset consists of two components, namely (i) the corpus data involving the Dutch semi-copular verb "raken" and (ii) an R analysis script to reproduce the computational steps.</p>
Code for "New land-use-change emissions indicate a declining CO2 airborne fraction"
<p>Data and programming scripts for reproducing the results from the Nature publication titled:</p> <p>"New land-use-change emissions indicate a declining CO2 airborne fraction".</p> <p>Authors: Margreet J. E. van Marle*, Dave van Wees*, Richard A. Houghton, Robert D. Field, Jan Verbesselt, and Guido R. van der Werf<br> * These authors contributed equally.</p> <p>DOI: https://doi.org/10.1038/s41586-021-04376-4</p> <p> </p> <p>This dataset includes the following (All files are preceded by "Marle_et_al_Nature_AirborneFraction_"):</p> <p>- "Datasheet.xlsx": Excel dataset containing all annual and monthly emissions and CO2 time series used for the analysis, and the resulting airborne fraction time series.</p> <p>- "Script.py":<br> BEFORE RUNNING THE SCRIPT: change the 'wdir' variable to the directory containing the provided script and files.<br> NOTE: This script requires the Python module: 'pymannkendall'<br> Python script used for reproducing the results and figures from the paper. The provided Datasheet.xlsx file and the .zip and .npz files are required for this program. In case all these files are found by the script, it should run within several seconds. Successful execution of the script will save Figures 1-4 from the main text and print the data from Table 1. In case script execution takes longer, please check if the .xlsx, .zip and .npz files are correctly present in the assigned 'wdir' directory. Otherwise the script will start recalculating these files, which might take a while (see notes below).</p> <p>- "MC10000_MK_ts_TRENDabs.zip": .zip file containing all results from the Monte-Carlo simulation for trend estimation for Figure 3 (calculated using Python function 'calc_AF_MonteCarlo()'). This .zip file contains multiple .npz files for different emission scenarios and data treatments. This .zip file is managed by the Python script function 'calc_AF_MonteCarlo_filemanager()', there is no need to unzip the file manually. In case the .zip file is not found by the Python script (e.g. because the .zip file was unpacked manually and deleted), the program will start recalculating and save a new .zip file. This can take several minutes dependent on the computer used. Recalculated results could differ very slightly due to the random factor in the Monte-Carlo approach, even though the 10,000 iterations bring this variation to a minimum.</p> <p>- "MC1000_MK_run50x50_TRENDabs.npz": .npz file containing the Monte-Carlo results used for producing Figure 4 (calculated using Python function 'calc_AF_MonteCarlo_ARR()'). In case the .npz file is not found by the Python script (e.g. because it was deleted or not downloaded), the program will start recalculating and save a new file. This can take around 30 hours(!) dependent on the computer used. Recalculated results could differ slightly due to the random factor in the Monte-Carlo approach.</p> <p>- "tol_colors.py": Additional Python module used in script.py, required for producing the colors used in the Main text figures. Source: https://personal.sron.nl/~pault/</p> <p>- Figure files: Figures 1-4 from the Main text saved as .pdf files. Figure 3 is saved as three independent panels. The Figures are also reproduced by script.py if executed successfully.</p> <p> </p>
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Multi-aspect Integrated Migration Indicators (MIMI) dataset
<p>Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. The Multi-aspect Integrated Migration Indicators (MIMI) dataset is a new dataset to be exploited in migration studies as a concrete example of this new approach. It includes both official data about bidirectional human migration (traditional flow and stock data) with multidisciplinary variables and original indicators, including economic, demographic, cultural and geographic indicators, together with the Facebook Social Connectedness Index (SCI). It is built by gathering, embedding and integrating traditional and novel variables, resulting in this new multidisciplinary dataset that could significantly contribute to nowcast/forecast bilateral migration trends and migration drivers.</p> <p>Thanks to this variety of knowledge, experts from several research fields (demographers, sociologists, economists) could exploit MIMI to investigate the trends in the various indicators, and the relationship among them. Moreover, it could be possible to develop complex models based on these data, able to assess human migration by evaluating related interdisciplinary drivers, as well as models able to nowcast and predict traditional migration indicators in accordance with original variables, such as the strength of social connectivity. Here, the SCI could have an important role. It measures the relative probability that two individuals across two countries are friends with each other on Facebook, therefore it could be employed as a proxy of social connections across borders, to be studied as a possible driver of migration. </p> <p>All in all, the motivations for building and releasing the MIMI dataset lie in the need of new perspectives, methods and analyses that can no longer prescind from taking into account a variety of new factors. The heterogeneous and multidimensional sets of data present in MIMI offer an all-encompassing overview of the characteristics of human migration, enabling a better understanding and an original potential exploration of the relationship between migration and non-traditional sources of data.</p> <p> </p> <p>The MIMI dataset is made up of one single CSV file that includes 28,821 rows (records/entries) and 876 columns (variables/features/indicators). Each row is identified uniquely by a pairs of countries, built from the joining of the two ISO-3166 alpha-2 codes for the origin and destination country, respectively. The dataset contains as main features the country-to-country bilateral migration flows and stocks, together with multidisciplinary variables measuring cultural, demographic, geographic and economic variables for the two countries, together with the Facebook strength of connectedness of each pair. </p> <p> </p> <p><strong>Related paper: </strong>Goglia, D., Pollacci, L., Sirbu, A. (2022). Dataset of Multi-aspect Integrated Migration Indicators. <a href="https://doi.org/10.5281/zenodo.6500885">https://doi.org/10.5281/zenodo.6500885</a></p>
Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - images
<p>This resource contains images that are framegrabs from video recorded by submarine deployed by the MY Arctic Sunrise during their Antarctica expeditions. The first took place in 2018 and focused within the Gerlache Strait and along the western Antarctic Peninsula and the Antarctic Sound in January 2018. Dives were conducted beginning 19th to 27th January 2018. This resource supplement the images for “Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - data”</p>
MedAID_Key performance indicators for gilthead seabream on-growing under summer temperatures
<p>This data set contains the results from a nutritional trial performed at the Centre of Marine Sciences of Algarve (CCMAR, Faro, Portugal) within the framework of WP2 of the MedAID project (Mediterranean Aquaculture Integrated Development, Horizon 2020, GA number 727315). This experiment evaluated the impacts of protein to lipid ratios in low fishmeal/fish oil diets on key performance indicators of gilthead seabream (<em>Sparus aurata</em>, initial weight: 100 g ± 7 g) on-grown during summer temperature conditions. For further information on experimental conditions, please refer to the publication: “Aragão C., Cabano M., Colen R., Teodósio R., Gisbert E., Dias J. and Engrola S., 2022. Modulation of dietary protein to lipid ratios for gilthead seabream on-growing during summer temperature conditions. Aquaculture Reports, 25: 101262. doi:10.1016/j.aqrep.2022.101262”. In addition to properly cite this dataset, it would be appreciated that when using this dataset in a publication the original publication is cited.</p>
Pyu unique non-Indic akṣaras
<p>This comma-delimited .csv file contains all 1,702 unique akṣaras in non-Indic vocabulary in the Corpus of Pyu Inscriptions (http://hisoma-huma-num.fr/exist/apps/pyu/index2.html). The transliterations of akṣaras are by Arlo Griffiths, Marc Miyake, and Julian K. Wheatley. The analysis of akṣaras into components and the notes in the file are by Marc Miyake.<br> <br> Each akṣara is split into a maximum of eight components:</p> <ol> <li>preinitial consonant</li> <li>initial consonant</li> <li>vowel</li> <li>coda</li> <li>subscript dot</li> <li>anusvāra</li> <li>visarga</li> <li>zed (Z transliterates a mysterious character presumably indicating prosody)</li> </ol> <p>Each component has a column.</p> <p>Slashes separate multiple possible readings.</p> <p>Triple slashes indicate lost text before an akṣara-final character.</p> <p>Brackets indicate unclear portions of an akṣara.</p> <p>Parentheses indicate editorial restoration of lost text.</p> <p>Angle brackets indicate editorial addition of omitted text.</p> <p>Double angle brackets indicate scribal insertions.</p> <p>C indicates an unknown consonant.</p> <p>V indicates an unknown vowel.</p> <p>The "usable" column at the far left has two values: 0 for akṣaras which are fully legible (i.e., usable for studies) and x for akṣaras that are only partly legible, are of uncertain interpretation, or have editorial alterations (i.e., less usable for studies).</p> <p>The "notes" column at the far right is for akṣaras with unusual characters or character combinations. Numbers in this column in the format X.Y refer to inscription and line numbers in the Corpus of Pyu Inscriptions.</p> <p> </p>
Dissecting the FAIR Guiding Principles - Key Categories, Core Concepts, Focus Elements, and Harmonized Indicators
<p>A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, requirements, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.</p>
Value chains under the framework of life cycle assessment indicators
<p>Tables included in the article "Monitoring the bioeconomy: value chains under the framework of life cycle assessment indicators"</p>
Drug Indications Extracted from FAERS
<p>This dataset contains drug indications extracted from the FDA Adverse Event Reporting System (<a href="https://www.fda.gov/drugs/guidancecomplianceregulatoryinformation/surveillance/adversedrugeffects/">FAERS</a>).</p> <p>Source code here: <a href="https://github.com/stuppie/faers">https://github.com/stuppie/faers</a></p> <p><strong>Method Outline</strong></p> <ul> <li>Data files are extracted from zip files, parsed from csvs, and imported into a MySQL database (see parser.py).</li> <li>Duplicate records are then de-duplicated by taking only the most recent version for each case ID (see dedupe.py).</li> <li>Indications are normalized by matching to UMLS terms by string matching. Cross-references to Human Phenotype Ontology are pulled from UMLS and xrefs to Monarch Disease Ontology (MONDO) are pulled from MONDO using the UMLS xrefs. (See normalize_indications.py)</li> <li>Drugs names are normalized first by applying a few simple string cleaning operations (strip, fix slashes and periods). Then they are attempted to be matched to rxnorm by exact string matching. Those that don't match are run against rxnorm's <a href="https://rxnav.nlm.nih.gov/RxNormAPIs.html#uLink=RxNorm_REST_getApproximateMatch">approximate matching service</a>, and are accepted if the score is higher than 67/100. The matched rxnorm CUIs are then mapped to the their Ingredient level rxnorm ID. (See normalize_drugs.py)</li> <li>Indications are then retrieved for each drug ingredient and filtered to require a minimum of 20 individual occurances. (See get_indications.py)</li> </ul>
Results from retrospective Baltic Sea biodiversity indicator status assessment using BEAT 3.0 tool
<p>Here we present all our results from retrospective Baltic Sea biodiversity indicator data analysis using BEAT 3.0. The BEAT tool (Nygård et al. 2018) is an R coded software (Murray & Nygård 2018, available online: <a href="https://zenodo.org/record/1288315#.XRxNp2cXYg4">https://zenodo.org/record/1288315#.XRxNp2cXYg4</a>) developed for analyzing marine biodivesity status. It follows the strucuture of EU's Marine Strategy Framework Directive. For more detailed metadata about the tool, see Nygård et al. 2018.</p> <p>We used data from various biodiversity indicators in two areas of the Baltic Sea: Bothnian Sea and Gulf of Finland. BEAT integrates indicators to ecosystem components and aggregates them spatially (more details can be found in Nygård et al. 2018). We produced retrospective time series of integrated and aggregated indicators. The yearly assessments are done using the moving average of the indicator status of the past 5 years in order to gain a more robust assessment result. The assessment follows the protocol of biodiversity assessment in the HELCOM Holistic assessment <a href="http://www.helcom.fi/baltic-sea-trends/holistic-assessments">http://www.helcom.fi/baltic-sea-trends/holistic-assessments</a></p> <p>In the results table, all different spatial levels as well as ecosystem components are shown. All indicator results have a value between 0 and 1. If the indicator has a value over 0.6, it is considered to be in a good environmental status. Below are short description of the different columns:</p> <ul> <li>SAUID: ID of the spatial assessment unit (SAU) used. The largest SAU is Baltic Sea with an ID 1. It is divided to smaller SAUs and all individual SAUs have their own ID.</li> <li>SAUlevel: The highest possible level of SAU is the Baltic Sea and it is the level 1. The sea basins (for example Bothnian Sea) are the level 2 and so on.</li> <li>ECID: Ecosystem component ID. All possible indicators have their own ID. See the list of ecosystem components in the input files of the tool (Murray & Nygård 2018).</li> <li>EClevel: Ecosystem component level. The level 1 is biodiversity, in the level 2 it is divided to pelagic habitat, birds, fish, benthic habitat and mammals and so on.</li> <li>EcosystemComponent: this column tells the ecosystem component. It can be higher level e.g. biodversity or an individual indicator for certain taxa.</li> <li>EQR: ecological quality ratio. The status of the certain ecosystem component in a certain SAU. The value varies between 0 and 1. If it is over 0.6, the ecosystem component is considered to be in a good status.</li> <li>Columns H-T: These refer to certain descriptors of Marine Strategy Framework Directive. If the ecosystem component is considered to have a link to a certain descriptor, an EQR value is given.</li> <li>year: year of the assessment. Note: all the yearly values are a moving average of past 5 years.</li> </ul> <p> </p>
STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.2 System Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the system level of the new monitoring system. The data is part of the D3.2 "Report on additional indicators<br>of monitoring system". The indicators are organised by category, principles, criteria, requirements and references. </p> <p> </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.