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4,283 results for “Database”
Database Mobbing-UNIPSICO Scale in Spanish Teachers
<p>This dataset contains data of non-university teachers collected by paper and pencil at the workplace between October 2015 and May 2020. These data were collected by employees working in the INVASSAT (Instituto Valenciano de Seguridad y Salud en el Trabajo, Government of the Valencian Community, Spain). The INVASSAT employees went to all educational center and informed the director, union representative, and teachers at each school of the procedure. Then each teacher filled in the questionnaire individually. The questionnaire was done in the presence of the INVASSAT employees to answer any doubts, and the filled questionnaires were given to the INVASSAT employee.</p><p>The file contains demographic variables, the responses to the 20 items of the Mobbing-UNIPSICO scale questionnaire, the responses to the items on alcohol, tobacco and medication use, and the response to the item regarding the necessity of professional support.</p><p>The name of the variables and the value labels have been written in English to facilitate their understanding.</p><p>Data and codebooks are provided in csv format, following the FAIR principles.</p><p>Three files are provided:</p><p>1. Mobbing database, with the data related to sample characteristics and the answers to the items of the questionnaires and the other items.</p><p>2. Database codebook of variables, with information of the labels of the variables of the Database file.</p><p>3. Variable values codebook, with the labels of the values of the variables in the Database file.</p><p> </p>
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
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>
Database of fitted spectra for: Changing-Look AGNs - I. Tracking the transition on the main sequence of quasars
<h3>Results from the spectral fitting for a sample of changing-look active galactic nuclei (AGNs) with SDSS spectroscopy using PyQSOFit.</h3>
IPMWORKS Resource Toolbox - database extraction March 2024
<p><span>The IPMWORKS IPM Resource Toolbox (Toolbox) has been developed as an interactive, online repository of integrated pest management (IPM) resources. Populated with high priority resources for farmers and their advisors during the project, its structure enables additional resources added over time. The repository is a public interactive website, available to anyone looking to access, understand, and implement IPM. Built on an open-source content management system, the toolbox is designed to require minimal post-production site maintenance and support, while being easily expanded to integrate resources from future initiatives.<br>At the core of the Toolbox lies MongoDB, a powerful NoSQL database management system. The schema-less nature of MongoDB allows for flexible data modeling, crucial for accommodating the diverse array of materials within the IPMWORKS ecosystem. Additionally, the integration of GridFS, a feature of MongoDB, facilitates the storage and retrieval of large files like images, PDFs, and documents. This architectural choice ensures optimal performance and efficiency in handling a wide range of materials. <br>We here make available all content uploaded to the IPMWORKS Resource Toolbox up to 18 March 2024. Materials are available in two formats, first as MongoDB files which require users to open them as a Mongo database file, and second as JSON files. Note that the JSON format does not include access to any pdfs attached to Toolbox content, only the associated metadata. </span></p> <p> </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>
MetaFunc Databases: nr-go database
<p>MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.</p>
MetaFunc Databases: Kaiju database
<p>MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.</p>
WageIndicator Collective Agreements Database Dataset with Full Texts and Selected Clauses
<p>Since 2012, the <a href="https://wageindicator.org/">WageIndicator Foundation</a> has maintained a <a href="https://wageindicator.org/cbadatabase">Collective Agreements Database</a>, where the texts of 1600 collective agreements (CBAs) from 61 countries and in 27 languages have been uploaded, coded and annotated. This database is a unique example at global level: collective agreements are documents containing conditions of employment that result from negotiations between independent unions and employers, and their content is often surrounded by an atmosphere of secrecy. Under the <a href="https://sshopencloud.eu/">SSHOC project</a> and with the support of the <a href="https://www.clarin.eu/">CLARIN Research Infrastructure</a>, the agreements have been manually and automatically annotated on several levels: for each agreement, the team answers a series of questions and selects the appropriate piece of text (clause) for each. </p> <p>One of the results of the collective agreements' annotation process is the dataset which is available here and includes all the clauses selected for each variable (WageIndicator_CBADatabase_Selected_Clauses). The full collective agreements' texts are stored in another dataset, also available here (WageIndicator_CBADatabase_Full_Texts_211019). A codebook is also included (210125-wageindicator-cba-codebook.pdf).</p>
Software and suspect database for: "A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences"
<p>This upload contains the pesticide suspect list aggregated among the laboratories of work package 16 of the HBM4EU (https://www.hbm4eu.eu) project for a large-scale pesticide suspect screening and the resolving search templates for each pesticide. Additionally, we provide the used software version of MetAlign applied in this screening.</p>
The MAT_STOCKS database: economy-wide material flows and material stock dynamics around the world
<p>Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.</p> <p>This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (<em>MISO2) </em>(Wiedenhofer et al. 2024)<em>. </em>MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository. </p> <p><strong>The MAT_STOCKS database version 1.0 </strong>provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:</p> <ul> <li>Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)</li> <li>Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)</li> <li>Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)</li> <li>177 countries</li> <li>1900 to 2016 </li> </ul> <p>All units in kilotons. Paramter names are in accordance with the system definition given in the publication.</p> <p>Additionally, this repository includes all data presented in the figures of the related journal article.</p> <p><strong>Further information</strong></p> <p>This dataset complements the following scientific article:</p> <p>Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, <a href="https://doi.org/10.1111/jiec.13575">https://doi.org/10.1111/jiec.13575</a></p> <p>The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <em>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</em></p> <p><strong>Funding</strong></p> <p>This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.<br><br></p>
Taxonomic and ecological database of trees of Western Ghats - TreeGhatsData
<p><em>TreeGhatsData</em> is a compilation of lists of tree taxa found in Western Ghats, South India:</p> <ul> <li>taxa for which the word "tree" appears in habit description in the book <em>Flowering plants of the Western Ghats</em> edited by the Tropical Botanic Garden Research Institute (TBGRI), including planted or cultivated taxa (Nayar, Beegam, and Sibi. 2014);</li> <li>tree taxa described after 2014 in journal articles;</li> <li>taxon names used in forest surveys published by the French Institute of Pondicherry (IFP), in journal articles from 2000, and in the Atlas of endemics of the Western Ghats (Ramesh and Pascal 1997);</li> <li>taxon names reported with "tree" habit in Indian Biodiversity Portal (http://indiabiodiversity.org/).</li> </ul> <p>For each plant name, <em>TreeGhatsData</em> includes the following taxonomic information: family, genus epithet, species epithet, infrataxon rank, infrataxon epithet, authority. Both the family name used in TBGRI book and the corresponding family name according to Angiosperm Phylogeny Group system III (APGIII; Bremer et al. 2009) are provided.</p> <p><em>TreeGhatsData</em> includes the taxonomic status, the reference name and the authority according to TBGRI flora, along with taxonomic status from The Plant List version 1.1 (http://www.theplantlist.org/). From these two sources, a taxonomic status is suggested for each taxon name, with corresponding reference names and authorities.</p> <p><em>TreeGhatsData</em> also includes ecological and biogeographic information from TBGRI and completed by the botanists of French Institute of Pondicherry (IFP).</p> <p>Because most vegetation surveys do not provide taxon names at infraspecific level, <em>TreeGhatsData</em> includes both the infraspecific taxa mentioned in Western Ghats and the corresponding specific binomial names.</p> <p><em>TreeGhatsData</em> is provided as a CSV file with comma separator.</p> <p><strong>Related references</strong></p> <p>Bremer, B., Bremer, K., Chase, M. W., Fay, M. F., Reveal, J. L., Soltis, D. E., Soltis, P. S., Stevens, P. F., Anderberg, A. A., Moore, M. J., Olmstead, R. G., Rudall, P. J., Sytsma, K. J., Tank, D. C., Wurdack, K., Xiang, J. Q. Y. & Zmarzty, S. (2009) An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Botanical Journal of the Linnean Society, 161, 105-121.</p> <p>Nayar, T., Rasiya Beegam, A. & Sibi, M. (2014) Flowering plants of the Western Ghats, India, Volume 1 Dicots; Volume 2 Monocots. Jawaharlal Nehru Tropical Botanic Garden and Research Institute.</p> <p>Ramesh, B. & Pascal, J.-P. (1997) Atlas of endemics of the Western Ghats (India): distribution of tree species in the evergreen and semi-evergreen forests. French Institute of Pondicherry, Pondicherry, India.</p>
LIAS light – A Database for Rapid Identification of Lichens – Subset Switzerland p. pte.
<p>This subset of the LIAS light database focuses on lichens found in Switzerland, providing comprehensive data for ecological research and taxon identification purposes. Not all taxa and only categorical (but 2 numerical) characters (descriptors) of the recorded taxa are covered. Updates and additional data will be published subsequently.</p>
Nairobi and Istanbul Multi-Hazard Interrelationships Database
<p><em><span>Nairobi and Istanbul Multi-Hazard Interrelationships Database</span></em></p> <p><strong><span>10.5281/zenodo.13220740</span></strong></p> <p><span>This </span><em><span>Nairobi and Istanbul Multi-Hazard Interrelationships Database</span></em><span> </span><span>uses a critical review of 135 sources (academic and grey literature, databases, online and social media), to identify <strong>the breadth of natural hazard types</strong> that might influence Nairobi (19 possible natural hazard types) and Istanbul (23 hazard types). We further identified <strong>hazard interrelationship pairs</strong> (e.g., an earthquake triggering landslides) in Nairobi (88 potential hazard interrelationship pairs) and Istanbul (105 hazard pairs) out of a possible 576 interrelationships. This extensive Excel (140 kb) database accompanies the paper Šakić Trogrlić et al. (2024).</span></p> <p><span>The <em>Nairobi and Istanbul Multi-Hazard Interrelationships Database</em> consists of the following eight tabs (in brackets the number of rows [R] × columns [C] of information):</span></p> <ul> <li><span>Excel Tab A. <span> </span>Single Hazard Evidence Nairobi (87R×16C)</span></li> <li><span>Excel Tab B. <span> </span>Single Hazard Evidence Istanbul (68R×11C)</span></li> <li><span>Excel Tab C. <span> </span>Hazard Interrelationships Nairobi (118R×14C)</span></li> <li><span>Excel Tab D. <span> </span>Hazard Interrelationships Istanbul (122R×13C)</span></li> <li><span>Excel Tab E. <span> </span>Definitions (Evidence Types) (7 definitions)</span></li> <li><span>Excel Tab F. <span> </span>Definitions (Hazards) (31R×5C)</span></li> <li><span>Excel Tab G. <span> </span>Definitions (Hazard Relations) (3 definitions)</span></li> <li><span>Excel Tab H. <span> </span>References</span></li> </ul> <p><span> </span><span>For <span>Nairobi (Tab A) and Istanbul (Tab B)</span>, each row in the database presents a source of evidence of a <strong>single hazard type</strong> influencing Nairobi or Istanbul. We compiled multiple evidence sources for many of the hazard types, each on its own row. In columns, we describe the evidence through various qualifiers, including the following: </span></p> <ul> <li><span>identifying the hazard type (24 possible hazard types)</span></li> <li><span>source information and URL link</span></li> <li><span>source content</span></li> <li><span>hazard interrelationships</span></li> <li><span>anthropogenic influences</span></li> <li><span>video evidence</span></li> <li><span>source reflections</span></li> </ul> <p><span> </span><span>For <span>Nairobi (<strong>Tab C</strong>) and Istanbul (<strong>Tab D</strong>)</span>, each row in the databases presents a source of evidence of a <strong>hazard interrelationship</strong> in Nairobi or Istanbul. In columns, we describe the evidence through various qualifiers, including the following: </span></p> <ul> <li><span>primary hazard (24 hazard types)</span></li> <li><span>secondary hazard (where applicable, the same 24 hazards as for the primary hazard)</span></li> <li><span>the generic description of hazard interrelationship mechanisms</span></li> <li><span>whether the relationship is triggered or increased probability or both</span></li> <li><span>source information and URL link</span></li> <li><span>source content (e.g., interrelationship type, description, and hazard sequence)</span></li> </ul> <p><span>The reader is referred to Šakić Trogrlić et al. (2024) for a detailed description of the methodology by which this database was constructed.</span></p> <p><strong><span>References</span></strong></p> <p><a name="_Hlk154927256"></a><span>Šakić Trogrlić, R., Thompson, H. E., Yahya Menteşe, E., Hussain, E., Gill, J. C., Taylor, F. E., Mwangi, E., Öner, E., Bukachi, V. G., & Malamud, B. D. (2024). Multi-hazard interrelationships and risk scenarios in urban areas: A case of Nairobi and Istanbul. <em>Earth’s Future.</em> 12, e2023EF004413. https://doi.org/10.1029/2023EF004413</span></p>
BioReCer Main biological feedstock flows database
<p>BioReCer aims at assessing and complementing current certification schemes for biological resources according to the new EU sustainability goals to enhance bio-based circular systems.</p> <p>This will be achieved by including new criteria that align with EU taxonomy and EU corporate due diligence regulations into guidelines for certifying biological resources’ sustainability, origin, tracking and traceability (T&T), and by ensuring applicability at EU and global scale.</p> <p>By promoting the sustainability and trade of biological resources, BioReCer will increase the added value, use, as well as social acceptance of bio-based products.</p> <p>Part of the specific objectives of the project is to map the current European biomass flows in 4 main sectors:</p> <ol> <li>Fishery</li> <li>Urban waste and wastewater</li> <li>Agriculture</li> <li>Forestry</li> </ol> <p>This database presents information on over 30 biomass feedstocks from across this sectors and is estimated to cover approx. 90% of the available secondary biological feedstocks in the EU. </p> <p>Table 1 shows the amount of secondary biomass produced by feedstock and their fates.</p> <p>Table 2 shows the amount of primary biomass prodused, imported and exported to and from Europe.</p> <p>This database is based on the work done for derivable 2.1 - Main biological feedstocks flows.</p>
Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database
<p>This <em>Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database</em> uses a systematic review of blended evidence types (academic literature, grey literature, media, databases, and social media) to compile single hazard and multi-hazard interrelationship exemplars of natural hazards in the context of Kathmandu Valley.</p> <p>We identify 58 sources of evidence for single hazard types and 21 sources of evidence for multi-hazard interrelationships. These sources evidence 21 single hazard types across six hazard groups, and 83 multi-hazard interrelationships that could influence Kathmandu Valley. Of these multi-hazard interrelationships, 12 have direct case study evidence of previous influence in Kathmandu Valley.</p> <p>This Excel database accompanies the paper Thompson et al. (2024).</p> <p>The <em>Kathmandu Valley Single Hazards and Multi-Hazard Interrelationships Database </em>comprises the following sheets: <br>A. Single Hazards Evidence <br>B. Hazard Interrelationships Evidence <br>C. Hazard Interrelationships Matrix <br>D. Matrix Evidence <br>E. Definitions (Source Types) <br>F. Definitions (Hazards) <br>G. Definitions (Interrelationships) <br>H. References </p> <p>In Sheet A, each row in the database describes a separate source of evidence of a single hazard influencing Kathmandu Valley. In each column, we describe the evidence using the qualifiers outlined below:</p> <ul> <li>Hazard type</li> <li>Source information and link</li> <li>Source content</li> <li>Hazard interrelationships and anthropogenic processes</li> <li>Video evidence</li> <li>Source reflections</li> <li>Major event typical frequency reflection</li> <li>Any other reflection on a single hazard</li> <li>Impact</li> </ul> <p>In Sheet B, each row in the database describes a separate source of evidence of a multi-hazard interrelationship influencing Kathmandu Valley. In each column, we describe the evidence using the qualifiers outlined below:</p> <ul> <li>Hazard type</li> <li>Source information and link</li> <li>Source content</li> <li>Hazard sequence</li> <li>Source reflections</li> <li>Impact</li> <li>Input from practitioner stakeholders</li> <li>Input from practitioner stakeholders - prioritisation</li> </ul> <p>We refer the reader to Thompson et al. (2024) for details of the methodology used to populate this database.</p> <p><strong>References</strong></p> <p>Thompson, H. E., Gill, J. C., Šakić Trogrlić, R., Taylor, F. E., and Malamud, B. D.: A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to Kathmandu Valley, Nepal, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2024-101, in review, 2024.</p>
Prosopographical Database of Judeans in Babylonia (outside Yahudu and the Murašû Archive)
<p>This is a prosopographical database of Judean persons in Babylonia outside the Yahudu corpus and the Murašû archive. It relates to Tero Alstola, 2020, <em>Judeans in Babylonia: A Study of Deportees in the Sixth and Fifth Centuries BCE</em> (Culture and History of the Ancient Near East 109. Leiden: Brill). For further information, see the readme file.</p>
SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). </p> <p>A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a></p>
Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease
<p>Movement data was collected through smartwatches to monitor motor competence in subjects with Parkinson's Disease (PD). The data set collected for the Monipar study consists of triaxial acceleration data from 21 subjects with PD and 7 healthy control subjects when performing a set of physical exercises while wearing an off-the-shelf smartwatch. Each participant performed the complete set of eight exercises once a week, commonly on the same day and at a similar time. Three Matlab files are provided that contain the raw data of the experimental subgroups: (1) Supervised, (2) Remote, and (3) Healthy control. Additionally, two Matlab files are provided containing the Tremor Labels for selected subjects in the experimental subgroups: (1) Supervised and (2) Remote.</p><p>While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:</p><p>Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.</p><p>Supervised group: This subgroup comprised PD patients who underwent the experimental protocol under circumstances similar to the remote group. Additionally, clinical scoring (MDS-UPDRS) is reported for this group in the file "MONIPAR SUBJECTS DATA.xlsx"</p><p>Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.</p><p>Data was collected using a sample rate of 50Hz and expressed in m/s^2.</p><p>Check the "Monipar_README.txt" file for details about this dataset. Further details are contained in the following reference -- if you use this dataset, please cite:</p><p>Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "<strong>Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones</strong>". <i>Frontiers in Neurology</i>, <i>14</i>, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a></p><p>References:</p><p>Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879</p><p>Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.</p><p><strong>Funding:</strong></p><p>This research was funded by the following projects:</p><p>(1) "Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP).</p><p>(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.</p>
ΔG-RDKit: Solvation Free Energy Database
<p>We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy".</p> <p>This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy Δ<em>G</em><sub>solv</sub> values. Data entries were collected from two separate databases. The <a href="https://link.springer.com/article/10.1007/s10822-014-9747-x">FreeSolv</a> library, with 642 experimental aqueous Δ<em>G</em><sub>solv </sub>determinations and the <a href="https://mediatum.ub.tum.de/1452571?v=1">Solv@TUM</a> database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties.</p> <p>Experimental Δ<em>G</em><sub>solv</sub> values range from -14 to 4 kcal mol<sup>-1</sup> and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using <a href="http://http://www.rdkit.org/">RDKit</a> software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<em>G</em><sub>solv</sub>.</p> <p>Through Machine Learning regression algorithms, our models were able to make Δ<em>G</em><sub>solv</sub> predictions with high accuracy, based on the information encoded in each chemical feature.</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.