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49 results for “Integrated database”
The International Soundscape Database: An integrated multimedia database of urban soundscape surveys -- questionnaires with acoustical and contextual information
<h1>Introduction</h1> <p>The International Soundscape Database contains the results of a series of soundscape assessment campaigns carried out across Europe and China. The data collection process was conducted according to the <a href="https://www.mdpi.com/2076-3417/10/7/2397">SSID Protocol [1]</a> which integrates in situ questionnaires about users' soundscape experience, with binaural recordings, sound level meter readings, and 360 degree video. The core of this database are individual soundscape questionnaires collected for 3,500+ participants completed in situ in cities across Europe and China, and the psychoacoustic analysis of 30s binaural recordings which can be matched up to each questionnaire.</p> <p>The SSID Protocol was based on the ISO 12913 standard for soundscape data collection [2]. For more information on the specifics of how this data is collected, please see [1].</p> <p>It is the intention that this dataset be added to and augmented with new locations, cities, and contexts in the future. This will be done both by the SSID team at University College London, but we also strongly welcome contributions from other researchers and practicioners. If a soundscape assessment is collected according to the SSID Protocol, it can be integrated with the rest of the database to form a large, cohesive, and ever-growing database of soundscape assessments. </p> <h2>Analysis</h2> <p>Code for exploring and analysing this dataset is included as part of the <a href="https://soundscapy.readthedocs.io/en/latest/">Soundscapy package</a>.</p> <h2>Included Files</h2> <p>This dataset incorporates surveys taken in multiple urban public spaces across several cities in Europe and China. These urban spaces include places like parks, urban squares, green spaces, and market streets. At each location, up to 100 questionnaires were collected over a series of multi-hour long sessions. Therefore the data is organised by LocationID, then SessionID, then GroupID.</p> <p>The basic directory structure and contents can be found below. </p> <h3>Survey Data (.csv)</h3> <p>'ISD v1.0 Data.csv' organises the data according to the labels given above.</p> <h3>Survey Metadata (.xlsx)</h3> <p>In addition a metadata file ('ISD v1.0 Metadata.xlsx') with photos and descriptions of each of the locations is provided. This metadata file also includes Data Dictionaries for each of the survey instrument versions included. These data dictionaries document precisely the questions asked and the available reponse labels and coding, along with the relevant translations.</p> <h3>Psychoacoustic Analysis (.csv)</h3> <p>The compiled csv file is formatted with a row for each individual participant's questionnaire response, then includes the psychoacoustic analysis of the 30s binaural recording taken while the participant was completing the questionnaire. Details about the psychoacoustic analyses is given in the 'Acoustic Settings' tab in the metadata file.</p> <p>The compiled survey and psychoacoustic analysis data is contained in 'ISD v1.0 Data.csv'. This is compiled from raw survey data files contained in 'Survey_Data', with individual cleaned survey and psychoacoustic data files included in 'Survey_Data/Interim_<date>'. The scripts for compiling this data are included in 'Scripts/'.</p> <h3>Sound Level Meter logs (.xlsx)</h3> <p>'SLM_<city>/' folders include session-long (i.e. ~3hrs) sound level meter log data in.xlsx files for each SessionID.</p> <h3>Binaural Recordings (32-bit floating point .wav)</h3> <p>'WAV_<city>/' folders include the ~30s binaural recordings in 32 bit floating point .wav format. Within each city folder are a set of LocationID folders containing their associated recordings. The wav files are titled with its GroupID, which is matched to the corresponding survey GroupIDs. </p> <h3>Cleaning and Compilation Scripts (.py)</h3> <p>Python code for cleaning and compiling the data from the raw survey data (within Survey_Data/source_data) are provided. These can be run within the provided demo notebook, or from the terminal by calling 'python -m ISDv1_main' with the relevant arguments. See the README.md file in this directory for more information.</p> <pre><code><br>├── ISD v1.0 Data.csv ├── ISD v1.0 Metadata.xlsx ├── SLM_Granada │ ├── CampoPrincipe1_SLM.xlsx │ ├── ... ├── SLM_Groningen │ └── Noorderplantsoen1_SLM.xlsx ├── SLM_etc ├── Scripts │ ├── ISDcleanDemo.ipynb │ ├── ISDcleaning.py │ ├── ISDpsycho.py │ ├── ISDv1_main.py │ ├── README.md │ └── pyproject.toml ├── Survey_Data │ ├── Interim_2024-02-08_cleaned │ └── source_data ├── WAV_Granada_1 │ ├── CampoPrincipe │ ├── ... ├── WAV_etc</code></pre> <p><strong>Citation</strong>: If you use the ISD or part of it, please cite our paper describing the data collection protocol [1] and this dataset itself.</p> <p><strong>License and reuse</strong>: All ISD recordings are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) License and are free to use. We encourage other researchers to replicate the SSID protocol and contribute new locations to the dataset. We also encourage the use of these recordings and the perceptual data for further soundscape research purposes. Please provide the proper attribution and get in touch with the authors if you would like to contribute new data or for any other collaborations.</p> <p> </p> <p>[1] Mitchell A, Oberman T, Aletta F, Erfanian M, Kachlicka M, Lionello M, Kang J. The Soundscape Indices (SSID) Protocol: A Method for Urban Soundscape Surveys—Questionnaires with Acoustical and Contextual Information. <em>Applied Sciences</em>. 2020; 10(7):2397. <a href="https://www.mdpi.com/2076-3417/10/7/2397">https://doi.org/10.3390/app10072397 </a></p> <p>[2] ISO/TS 12913-2:2018 (2018). “Acoustics – Soundscape – Part 2: Data collection and reporting requirements” International Organization for Standardization, Geneva, Switzerland, 2018</p> <p>[3] Mitchell A, Oberman T, Aletta F, Kachlicka M, Lionello M, Erfanian M, Kang J. Investigating Urban Soundscapes of the COVID-19 Lockdown: A predictive soundscape modeling approach.<em> Journal of the Acoustical Society of America</em>. 2021.</p>
Integrated database on adaptation and mitigation measures in Europe
<p>Climate action is far from meeting the internationally agreed adaptation and mitigation goals. Even though climate action planning has increased since the Paris Agreement in 2015, the implementation rate of those plans remains low. Climate planning literature claims that accounting for long-term planning and implementation times, accurately estimating costs, identifying synergies and trade-offs between measures, or considering justice and equity issues might increase the quality of climate plans and facilitate the further implementation of climate actions.</p> <p>Also, there is no uniform way of responding to the climate crisis. Existing climate action databases typically focus on a particular type of response, sector, hazard, or type. In parallel, national governments and international initiatives provide tools and guidelines to facilitate the development of climate action plans. However, the primary climate action recording and monitoring initiatives and projects do not share the same framework as those tools, resulting in a lost opportunity to improve climate actions' knowledge transferability.</p> <p>Thus, we reviewed nine existing databases of adaptation and five mitigation databases, comprising a total of 7.130 adaptation actions and 11.409 mitigation actions, and detected a lack of alignment with climate planning practices and claims. Furthermore, we revealed a lack of coherency regarding the level of abstraction of climate actions and their role in the implementation process. Not all climate actions are meant to operate similarly from a planning perspective: while some had a direct outcome on the target indicators, others are thought to facilitate their implementation.</p> <p>Ultimately, we created a new integrated database of adaptation and mitigation measures in Europe, focusing exclusively on climate planning and implementation practices. First, we identified specific and transferable mitigation and adaptation measures and instruments through an originally designed decision tree. Second, we harmonised the collection of climate actions in a unique framework based on one of the biggest climate planning initiatives: the Sustainable and Energy Climate Action Plans by the Covenant of Mayors. Our integrated database of adaptation and mitigation measures (1) classifies and relates the different types of climate actions; (2) provides data that may improve the quality of climate plans and facilitate implementation; (3) allows a better perspective of systematic problems by identifying potential synergies and trade-offs; and (4) defines and characterises measures using a framework that draws on actual practice. The database compiles a total of 191 adaptation measures, 188 mitigation measures, and 97 measures that account for each, and a total of 609 associated instruments. For monitoring their outcomes, 93 SDG relevant indicators are included.</p>
Six decades (1959-2022) of water quality in the upper San Francisco Estuary: an integrated database of 16 discrete monitoring surveys in the Sacramento San Joaquin Delta, Suisun Bay, Suisun Marsh, and San Francisco Bay
The upper San Francisco Estuary (SFE) is simultaneously a central hub of water delivery in California and home to commercially important and endangered fishes, such as Chinook Salmon, Green Sturgeon, and Delta and Longfin Smelt. Extensive ecological monitoring has been conducted for over 50 years, mainly under the auspices of the Interagency Ecological Program for the San Francisco Estuary (https://iep.ca.gov/). We integrated water quality data from 16 boat-based long-term monitoring surveys in the upper SFE. This integrated dataset includes measurements of temperature (surface and bottom), conductivity (surface), salinity (surface), Secchi depth, qualitative concentration of the toxic alga Microcystis (surface), Chlorophyll-a concentration (surface), nutrients (surface), and other parameters from 1959 - 2022. The component surveys range in sampling frequency from thrice weekly to monthly and range in duration from 5 – 60 years. Most component surveys sample at fixed stations, but the Enhanced Delta Smelt Monitoring survey uses random sites and some stations (with “EZ” in the station name) of the Environmental Monitoring Program follow the salinity field. It is highly recommended to inspect the documentation of the component surveys for more information on their methods.
Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.
<p><strong>ABSTRACT </strong></p> <p>It contains the data of drug targets (gene names), uniprot identifiers, secondary linked data sources (e.g., PharmGKB), market drug name, chembl identifier, and pubchem compound identifier obtained from DGIdb.</p> <p><strong>Instructions: </strong></p> <p>Data were cleaned and duplicates were removed. Data were all categorical features.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "DRG_DEPOT" summer 2023 team project. It is used for constructing R2G dataset, which will map drugs to their drug targets (gene -> protein = drug target)</p> <p><strong>Acknowledgements</strong></p> <p>Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, Griffith M, Griffith OL, Wagner AH. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021 Jan 8;49(D1):D1144-D1151. doi: 10.1093/nar/gkaa1084. PMID: 33237278; PMCID: PMC7778926.</p> <p>https://www.dgidb.org/</p> <p><strong>U-BRITE last update date:</strong> 06/09/2023</p>
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
Data for Integrated Database, ver.1.0
<p>Analyzed data from MD simulations of small RNA motifs, comparison of predicted and measured NMR observables, performance of common RNA force fields, water models and ion parameters. The data are divided into directories by molecule. Simulations are stored in .dat files while .txt files contain a summary of all. The raw-data folder contains the calculated distances and dihedrals of the defined atoms. The dataset also includes an image to visualize molecule or a pdb file to use it in the Mol* Viewer software.</p>
Integrated Canadian Hail Database (2005-2022)
<p>This dataset combines hail reports in Canada between 2005 and 2022 from two internal sources in Environment and Climate Change Canada. Time is in UTC. See <a href="https://en.wikipedia.org/wiki/Provinces_and_territories_of_Canada">Provinces and territories of Canada - Wikipedia</a> for province codes. Common reference objects are used to compare with the diameter of the largest hail stone in vicinity. </p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs human dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://github.com/bhklab/ToxicoGx">https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
Dataset for 'A Resource Hub For Interoperability And Data Integration In Heritage Research: The H-Setis Database'
<p> Data and scripts for charts and maps published in "A Resource Hub For Interoperability And Data Integration In Heritage Research: The H-Setis Database".</p>
Combat-TB-NeoDB: fostering Tuberculosis research through integrative analysis using graph database technologies.
<p>NeoDB is a free and open source integrated M.tuberculosis ‘omics’ knowledge-base. NeoDB is based on Neo4j and enables researchers to execute complex federated queries by linking well-known, curated and widely used biological data resources, and supplementary TB variants data from published literature.</p> <p>Documentation can be found at https://combat-tb-db.readthedocs.io</p>
Text-fig. 1. "Plant screen" scheme of complete results of the IPR-vegetation analysis derived from the database. in The Integrated Plant Record Vegetation Analysis: Internet Platform And Online Application
Text-fig. 1. "Plant screen" scheme of complete results of the IPR-vegetation analysis derived from the database.
Integrated Effect Database for Toxicological Observations (INTOB)
<p>This dataset contains morphological observations of zebrafish embryos from toxicological experiments from the department of Ecotoxicology at the Helmholtz-Center for Environmental Research (UFZ). Data was recorded via <a href="https://www.ufz.de/intob/index.php?en=51326">INTOB</a>, a software to collect and manage data and metadata from toxicological observations.</p> <p>The dataset contains two directories, <code>data/</code>which contains experimental data, and <code>scripts/</code> which contains all code needed to run the analysis described in the paper, as well as a compiled HTML file. <code>results/</code> contains a table with EC50 and LC50 values as visualised in Figure 3 of the paper.</p> <p><code>data/intob_data/</code> contains several csv files:</p> <ul> <li><code>effect_list_info.csv</code> contains all possible effects of the database and their relation to each other</li> <li><code>obs_phenotypes.csv</code> contains all individual observations</li> <li><code>metadata.csv</code> lists metadata about the experiments and substances</li> <li><code>plate_layout.csv</code> contains experimental setups, i.e. layouts of substances and concentrations in well-plates or vials</li> <li><code>phys_chem_prop.csv</code> contains the physico-chemical observations of the substances</li> </ul> <p><code>data/data_for_analysis/</code> contains files required in the script and <code>data/zotero_phenotype_library/</code> contains the results of the literature review described in the paper in JSON format.</p>
PaleoRiada: A New Integrated Spatial Database of Palaeofloods in Spain
<p>PaleoRiada is the first national geographic database that compiles data on palaeoflood records published in scientific journals, book chapters, conference presentations, and publicly accessible scientific-technical reports. This database has been implemented through a Database Management System (Microsoft Access). </p> <p>Funding:</p> <p>Grants 2022-2023 and 2023-2026, signed between the Spanish General Directorate for Water (DGA-MITERD) and the Spanish Research Council (CSIC-MCIU), which include actions 20223TE003 and 20233TE012 (Tarquín project in IGME-CSIC).</p> <p>Community of Madrid (Predoctoral research grant PIPF-2022/ECO-24879)</p> <p> </p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
Open the record for dataset details and reuse information.
International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases
<p>In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers' areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to "improve the efficiency of all social systems," "create new industries," and "increase intellectual productivity." For the analysis, we used 19-year bibliographic data (2000–2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea.</p>
Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts
<p>The Drug-Gene Interaction Database (DGIdb, <a href="http://www.dgidb.org/">www.dgidb.org</a>) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.</p>
International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases
Open the record for dataset details and reuse information.
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets
<p>This page links to the data associated with the publication "ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets ". The data have been curated and analyzed using our open-source R package, ToxicoGx (https://github.com/bhklab/ToxicoGx) and are available publicly in the ToxicoDB web application (www.toxicodb.ca). Please see the included DOIs below, or download the .csv file which contains the names, dates and DOIs of all datasets listed here.</p> <p>The TGGATES data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023.<br> <br> Data:</p> <ul> <li>TGGATEs humanldh (<a href="https://doi.org/10.5281/zenodo.3762812">https://doi.org/10.5281/zenodo.3762812</a>)</li> <li>TGGATEs humandna (<a href="https://doi.org/10.5281/zenodo.4024859">https://doi.org/10.5281/zenodo.4024859</a>)</li> <li>TGGATEs ratldh (<a href="https://doi.org/10.5281/zenodo.3762817">https://doi.org/10.5281/zenodo.3762817</a>)</li> <li>TGGATEs ratdna (<a href="https://doi.org/10.5281/zenodo.4024918">https://doi.org/10.5281/zenodo.4024918</a>)</li> </ul> <p>This Drug Matrix data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413.</p> <p>Data:</p> <ul> <li>Drug Matrix (<a href="https://doi.org/10.5281/zenodo.3766569">https://doi.org/10.5281/zenodo.3766569</a>)</li> </ul>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs rat dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://github.com/bhklab/ToxicoGx">https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</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.