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Dataset results
433 results for “readiness”
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
ALL-READY Questionnare on potential drivers and barriers to the adoption of innovation management, open science, and Intellectual Property Rights (IPR) among the members of the Pilot Network
<p><strong>Background & Summary</strong>: </p><p>The ALL-READY project unites a diverse consortium of Research Infrastructures (RI) and Living Labs, instrumental in developing new methodologies and technologies in agroecology. The project focuses on effective management of innovation, adherence to open science principles, and strategic application of Intellectual Property Rights (IPR). Task 6.4 of the project, which concentrates on Innovation and IPR Management, seeks to understand the dynamics influencing the adoption of these practices among its members. Recognizing the need for end-to-end data management, the project emphasizes standardized data collection and management while adhering to FAIR principles.</p><p><strong>Methods</strong>: </p><p>The questionnaire was developed by LifeWatch ERIC to capture data reflecting current practices and perceptions in agroecology. It included 26 questions divided into four sections, focusing on existing practices, potential drivers, and barriers in innovation management, open science, and IPR. The survey was disseminated via an online platform to the ALLREADY Pilot Network, ensuring a representative sample from diverse organizations. The data collection process was closely monitored, and the responses were analyzed using a mixed-methods approach to extract meaningful insights.</p><p><strong>Data Records of the ALLREADY Project Questionnaire</strong>: </p><p>The dataset, collected through an online survey platform, underwent a meticulous process of data preparation, download, formatting, and anonymization. It consists of one text file containing metadata (Readme.txt) and a single CSV file encompassing all questionnaire responses. The dataset provides a comprehensive view of innovation management, open science adoption, and IPR handling within the agroecology sector, particularly among the network of RIs and Living Labs involved in the project.</p><p><strong>Technical Validation of the ALLREADY Project Questionnaire</strong>: </p><p>Several critical steps were taken to ensure the accuracy, reliability, and overall quality of the data collected. This included development and testing of the questionnaire, rigorous monitoring of the data collection process, and thorough checks for data quality and completeness. The representativeness of the sample was analyzed specifically with respect to the Pilot Network rather than the broader population involved in agroecology. Strategies were employed to counter survey fatigue and maintain respondent engagement.</p><p><strong>Usage Notes for the ALLREADY Project Questionnaire</strong>: </p><p>The dataset's proper usage is vital for ensuring the validity and reproducibility of research. Researchers are advised to consider the nature of the data, the representativeness of the dataset, and its generalizability. The dataset allows for comprehensive analysis and integration of different sections, and analysts have the flexibility to handle open and write-in responses according to their research needs. Additional information to facilitate analysis is provided in a separate documentation file.</p><p> </p>
Bridge2AI Grand Challenge AI-Readiness Evaluation Data Year 2 of 4
<p>This excel workbook and set of radar plots contains current and projected AI-readiness evaluation datasets of four NIH Bridge2AI Program Grand Challenges in Functional Genomics, Clinical Care Informatics, Precision Public Health, and Return to Health (Salutogenesis). These evaluations were collected in late 2024, at the conclusion of Year 2 of the 4-year Bridge2AI program, by Grand Challenge (GC) representatives on the Bridge2AI Standards Working Group, in consultation with their GC leadership team, and will be updated in subsequent years and the program progresses. They assess biomedical AI readiness of existing collected data only. </p>
Modelling of ready biodegradability based on combined public and industrial data sources
<p>The European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB). In-silico prediction is a valid alternative to expensive and time-consuming experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues.</p> <p>In this work we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (BA = 0.74 – 0.79) and data coverage (83 – 91 %).</p> <p>The Generative Topographic Mapping approach was employed to compare the chemical space of the various data sources: several chemotypes and structural motifs unique to the industrial dataset were identified, highlighting for which chemical classes currently available models may have less reliable predictions.</p> <p>Finally, public and industrial data were merged into Global dataset containing 3146 compounds and including a significant subset of compounds coming from the industrial context. This is the biggest dataset reported in the literature so far which covers some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has much larger applicability domain than related models built on publicly available data. The developed model is available for the user on the Laboratory of Chemoinformatics website.</p> <p>This dataset is only the "All-Public" set, since the industrial compounds cannot be disclosed.</p> <p>This update contains additional entries from [J. Chem. Inf. Model. 52 (2012), pp. 655–669] and [J. Chem. Inf. Model. 53 (2013), pp. 867–878]</p>
DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014
<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> </p> <p> </p>
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Science ready spectra of star clusters and their best-fitting models described in the research paper "Using Star Clusters as Tracers of Star Formation and Chemical Evolution: the Chemical Enrichment History of the Large Magellanic Cloud" by Chilingarian & Asa'd
<p>Science ready spectra of star clusters in the Large Magellanic Cloud and their best-fitting templates (alpha-enhanced MILES based simple stellar population models) obtained using the NBursts full spectrum fitting code. Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For each cluster, 5 spectra are provided, which correspond to [alpha/Fe] values from 0.0 to 0.4 dex with a step of 0.1 dex. The only exception is NGC2249, for which only 3 models are provided. The alpha-enhancement value of a model grid used in the fitting procedure is given in the FITS keyword MGFEGRID.</p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction
<p>This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover.</p> <p>This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture.</p> <p>The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data.</p> <p>Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.</p>
Deliverable 2.3: Biodiversity impact estimates and documentation for indicators on multi-dimensional biodiversity aspects ready for use in WP3
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km2 resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a improve in previous deliverable 2.2.</p>
Investment Readiness Level - Self-assessment - BIOBEC
<p><span>This document will provide you with some guidelines on how the investment readiness level assessment was conducted for each of the BBECs, which is part of Economic Requirements for BBEC task to identify the financial needs of the BBECs and to develop a tailored financial plan for each of them</span></p> <p><span>The overall goal is to provide innovative pathways and strategies with regard to access to financial resources, to encourage the economic sustainability of the implementation and maintenance phase of the BBECs, and eventually ensure their lifelong continuation of them.<br><br></span></p> <p><span>By using the self-assessment, individuals and organizations can gain a better understanding of the current state of innovative education centers, as well as identify areas for improvement or potential opportunities for growth. This can help individuals and organizations make more informed decisions about which innovative education centers to invest in, or how to improve existing education centers in order to maximize their impact on the bio-based economy.</span></p> <p><span> </span></p> <p><span><span>A)<span> </span></span></span><strong><span>Competence and structure of the BBEC: </span></strong><span>This dimension assesses the competency and structure of the leadership team and staff of the innovative education center. The questions in this dimension are designed to evaluate the level of expertise and experience of the CEO, board members, and employees, as well as the overall structure and organization of the education center.</span></p> <p><span> </span></p> <p><span><span>B)<span> </span></span></span><strong><span>Markets</span></strong><span>: This dimension evaluates the market potential of the innovative education center. The questions in this dimension are designed to assess the size and growth potential of the target market, as well as the unique value proposition that the education center offers to its target market.</span></p> <p><span> </span></p> <p><span><span>C)<span> </span></span></span><strong><span>Talent</span></strong><span>: This dimension assesses the quality and availability of talent within the innovative education center. The questions in this dimension are designed to evaluate the education center's strategy for attracting and retaining top talent, as well as its culture of learning and professional development.</span></p> <p><span> </span></p> <p><span><span>D)<span> </span></span></span><strong><span>Technology landscape:</span></strong><span> This dimension evaluates the education center's use of technology in its teaching practices and curriculum. The questions in this dimension are designed to assess the education center's access to technology tools and hardware, as well as its plan for integrating technology into its teaching practices.</span></p> <p><span> </span></p> <p><span><span>E)<span> </span></span></span><strong><span>Financials</span></strong><span>: This dimension assesses the financial sustainability of the innovative education center. The questions in this dimension are designed to evaluate the education center's business model, as well as its projected revenue, expenses, and profits.</span></p> <p><span> </span></p> <p><span><span>F)<span> </span></span></span><strong><span>Economic</span></strong><span>: This dimension evaluates the potential economic impact of the innovative education center on the local community. The questions in this dimension are designed to assess the education center's ability to create jobs and increase productivity, as well as its potential risks or challenges.</span></p> <p><span> </span></p>
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini.
Global ML-ready dataset for mining areas in satellite images
<p>This dataset is a global resource for machine learning applications in mining area detection and semantic segmentation on satellite imagery. It contains Sentinel-2 satellite images and corresponding mining area masks + bounding boxes for 1,210 sites worldwide. Ground-truth masks are derived from <a href="https://doi.org/10.1594/PANGAEA.942325" target="_blank" rel="noopener">Maus et al. (2022)</a> and <a href="https://doi.org/10.5281/zenodo.6806817" target="_blank" rel="noopener">Tang et al. (2023)</a>, and validated through manual verification to ensure accurate alignment with Sentinel-2 imagery from specific timestamps. </p> <p>The dataset includes three mask variants:</p> <ul> <li>Masks exclusively from Maus et al. (n=1,090)</li> <li>Masks exclusively from Tang et al. (n=817)</li> <li>A preferred mask selected from either Maus or Tang based on alignment quality determined during manual review (n=1,210).</li> </ul> <p>Each tile corresponds to a 2048x2048 pixel Sentinel-2 image, with metadata on mine type (surface, placer, underground, brine & evaporation) and scale (artisanal, industrial). For convenience, the preferred mask dataset is already split into training (75%), validation (15%), and test (10%) sets. </p> <p>Furthermore, dataset quality was validated by re-validating test set tiles manually and correcting any mismatches between mining polygons and visually observed true mining area in the images, resulting in the following estimated quality metrics: </p> <table> <tbody> <tr> <td> </td> <td>Combined</td> <td>Maus</td> <td>Tang</td> </tr> <tr> <td>Accuracy</td> <td>99.78</td> <td>99.74</td> <td>99.83</td> </tr> <tr> <td>Precision</td> <td>99.22</td> <td>99.20</td> <td>99.24</td> </tr> <tr> <td>Recall</td> <td>95.71</td> <td>96.34</td> <td>95.10</td> </tr> </tbody> </table> <p>Note that the dataset does not contain the Sentinel-2 images themselves but contains a reference to specific Sentinel-2 images. Thus, for any ML applications, the images must be persisted first. For example, Sentinel-2 imagery is available from Microsoft's Planetary Computer and filterable via STAC API: <a href="https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a" target="_blank" rel="noopener">https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a</a>. Additionally, the temporal specificity of the data allows integration with other imagery sources from the indicated timestamp, such as Landsat or other high-resolution imagery.</p> <p>Source code used to generate this dataset and to use it for ML model training is available at <a href="https://github.com/SimonJasansky/mine-segmentation" target="_blank" rel="noopener">https://github.com/SimonJasansky/mine-segmentation</a>. It includes useful Python scripts, e.g. to <a href="https://github.com/SimonJasansky/mine-segmentation/blob/main/src/data/05_persist_pixels_masks.py">download Sentinel-2 images via STAC API</a>, or to <a href="https://github.com/SimonJasansky/mine-segmentation/blob/main/src/data/06_make_chips.py">divide tile images (2048x2048px) into smaller chips (e.g. 512x512px)</a>. </p> <p>A database schema, a schematic depiction of the dataset generation process, and a map of the global distribution of tiles are provided in the accompanying images. </p>
Strategic Roadmap Toward I4.0: A Model Based on Maturity and Readiness Analysis
<p><span>Modern distributed manufacturing is having an essential role in the world economy. Agility, flexibility, and responsiveness distinguishs an innovation-driven business ecosystem, mainly anchored in cloud-based environments. Therefore, manufacturing firms demand an structured roadmap to lead their way toward Industry 4.0, impacting their supply chains, operations, and business models. Naturally, dificulties will raise strong dpending how far they are from a modern ditributed model adherent to I4.0 and their readiness to start a digital transformation roadmap. Therefore, maturity and readiness analysis is essential to evaluate the impact of a sound design process toward I4.0 and to anticipate obstacles and possible return. This work proposes a design framework based on a web-based distributed cloud manufacturing approach to suport a roadmap to I4.0, relying on maturity and readiness analysis, focusing on specific building blocks as rationales.</span> </p>
Machine Learning Ready Induced Seismicity Data
<p>This dataset contains previously published data on induced seismicity that has been processed to be machine learning ready.<br> <br> The data contains time series of the cumulative number of seismic events in certain areas and the corresponding pressures induced from injecting fluids into the ground. The natural task is to forecast future seismicity given past seismicity and pressures. These datasets aim to require as little seismology experience as necessary to prepare the data for forecasting algorithms. <br> <br> Data is provided for different locations. For Decatur Illinois, the seismic data was taken from Williams-Stroud et al., 2018 and the pressure data originated from Luu et al., 2022. Data aggregated over the whole region lies in the temporal_datasets/decatur_illinois/ folder. The region was further subdivided into subregions and the corresponding data stored separately (e.g. in loc1). </p> <p>The Kansas data originated from Cochran et al., 2018 and is further divided into subregions. </p> <p>The Cushing, Oklahoma is adapted from Skoumal et al., 2020. </p> <p>Each seismic file contains the following columns: epoch latitude longitude depth easting northing magnitude. The epoch corresponds to the number of seconds since a certain date (e.g. November 17, 2011 for Decatur). Each seismic event corresponds to one row in the file. <br> <br> Each pressure file contains the following columns: epoch pressure dpdt. dpdt is the derivative of pressure. </p>
Deliverable 2.4: Biodiversity impact database including characterization factors and documentation ready for use in Module C
<p><span>We quantify the impacts of agriculture and livestock, on biodiversity (including intensity of use), and translate the results of these modeling efforts which indicate current patterns of biodiversity indicators in both South America and Africa (as well as potential biodiversity loss) to data inputs to be further used in CLEVER. </span></p>
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E).
Future-ready Nordic homes: Responding to the Changing Climate with Free-running Buildings; Digital Repository of Obtained Results
<p>The figures provided here show complete results of the parametric building performance simulation for each studied location, which was a part of the Master thesis in Energy-efficient and Environmental Building Design (Faculty of Engineering, Lund University, Sweden) by Marko Ljubas.</p> <p>The simulations were performed using software IDA ICE 4.8 by EQUA.</p> <p>The weather files were generated using software Meteonorm 8.2 by Meteotest.</p>
Figure 2 in A workshop to advance invasive species early detection capacity of The Rapid Environmental DNA Assessment and Deployment Initiative & Network (READI-Net)
Figure 2. The Rapid Environmental (e)DNA Assessment and Deployment Initiative & Network (READI-Net) project components being developed to support molecular detection of invasive species. Molecular tools, like eDNA sampling, are sensitive and costeffective for early detection of invasive species and are one component of the National Early Detection Rapid Response framework.
ScienceDex guides
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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.