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1,045 results for “Generated Data”
Raw data for "Modular Pulse Program Generation for NMR Supersequences"
<p>Raw data for the paper <em>Modular Pulse Program Generation for NMR Supersequences</em>, which accompanies the GENESIS website for automatic generation of NOAH pulse programmes.</p> <p>Please note that this contains data only, not any of the accompanying figures. The same datasets may alternatively be downloaded from GitHub, if preferred: https://github.com/yongrenjie/genesis-paper/releases/tag/final-revision</p> <ul> <li>For the figures, and the scripts used to generate them, please see https://github.com/yongrenjie/genesis-paper (the repository readme contains detailed instructions on reproducing the figures)</li> <li>For the GENESIS source code and a LaTeX version of the paper itself, please see https://github.com/yongrenjie/genesis</li> <li>For the GENESIS website itself, please see https://nmr-genesis.co.uk</li> </ul>
Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)
<p><span>Data of</span> fitness effects of environmental stressors on <em>Daphnia magna</em> over multiple generations. Ages of first and second reproduction, and sizes of first and second brood were measured and used to calculate replacement rate. This data is part of a study on whole-genome bisulphate sequencing on individual <em>Daphnia magna</em> to assess whether environmentally-induced DNA methylation can persist for up to four generations.</p>
LiDAR metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands
<p>This data repository contains the LiDAR metrics generated from country-wide Airborne Laser Scanning (ALS) data from the Netherlands. The LiDAR metrics (10-meter resolution) are derived from AHN3 using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow. Raw point cloud data can be downloaded <a href="https://app.pdok.nl/ahn3-downloadpage/">here</a>. </p>
Generating FAIR Research Data in Experimental Tribology
<p><strong>Stream video at:</strong> <a href="http://youtu.be/xwCpRDnPFvs">https://youtu.be/xwCpRDnPFvs</a></p> <p>To assess the feasibility of producing FAIR data via the integration of a controlled vocabulary, an ontology, and an ELN, this dataset demonstrates the implementation of a tribological experiment while accounting for as many details as possible. The showcase experiment had a lubricated pin-on-disk arrangement, ran at 15 N normal load and a velocity range of 20 to 170 mm/s. With this dataset, we hope to provide a possible blueprint for FAIR data publication in experimental tribology.</p> <p><a href="http://www.nature.com/articles/s41597-022-01429-9">https://www.nature.com/articles/s41597-022-01429-9</a> - Garabedian, N.T., Schreiber, P.J., Brandt, N., Greiner, C., et al.</p> <p>Abstract: Generating FAIR research data in experimental tribology. Sci Data 9, 315 (2022). Digital solutions for the generation of FAIR (Findable, Accessible, Interoperable and Reusable) data and metadata in experimental tribology are currently lacking, despite the looming challenge of integrating cutting-edge data science techniques – a promising scientific route for any field that often relies on phenomenology and empiricism. Additionally, the broad interdisciplinarity of tribology is probably a main contributing factor for the lack of community-wide data and metadata standards, and the heavy reliance on custom workflows and equipment. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability and reusability of experimental tribological data beyond typical publication practices.</p> <p><a href="https://doi.org/10.5281/zenodo.5720626">https://doi.org/10.5281/zenodo.5720626</a> - FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment</p> <p><a href="https://doi.org/10.5281/zenodo.5720198">https://doi.org/10.5281/zenodo.5720198</a> or <a href="https://github.com/nick-garabedian/TriboDataFAIR-Ontology">https://github.com/nick-garabedian/TriboDataFAIR-Ontology</a> or <a href="https://fairsharing.org/3597">https://fairsharing.org/3597</a> - TriboDataFAIR Ontology</p> <p><a href="https://doi.org/10.5281/zenodo.5720218">https://doi.org/10.5281/zenodo.5720218</a> or <a href="https://github.com/nick-garabedian/SurfTheOWL">https://github.com/nick-garabedian/SurfTheOWL</a> - SurfTheOWL</p> <p><a href="https://kadi4mat.iam-cms.kit.edu/">https://kadi4mat.iam-cms.kit.edu/</a> - Kadi4Mat Virtual Research Environment and Electronic Lab Notebook </p>
Data generated by the model presented in the research article entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell"
<p>This repository provides all the data and scripts necessary to reproduce the line plots shown in the manuscript entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell".</p>
Textomics: A Dataset for Genomics Data Summary Generation
<p>This is the dataset of our ACL 2022 paper:</p> <p>Textomics: A Dataset for Genomics Data Summary Generation.</p> <p>Please read the "readme.md" in it for the format of the dataset.</p>
Data of "Efficient generation of entangled multi-photon graph states from a single atom"
<p>Data published in "<em>Efficient generation of entangled multi-photon graph states from a single atom</em>"</p>
Data generated from: Functional connectivity of the world's protected areas
<p>Here, we provide the two primary global connectivity datasets generated in the study titled "Functional connectivity of the world's protected areas", including the protected area isolation (PAI) metric for all included protected areas (i.e., effective resistance), provided as a csv file, and the map of global mammal movement probability (i.e., electrical current density), provided as a tif. We also include the nationally aggregated PAI values in National_PAI.csv. National PAI represents the median PAI value for each country, after excluding values equal to -1.</p> <p>Generation of these datasets relied on the following three external data sources:</p> <p>- Observed mammal movement data (0.95 quantile displacement distances over 10-days), predictor variables and the linear mixed effects model presented in: M. A. Tucker <em>et al.</em>, <em>Science</em>. <strong>359</strong>, 466–469 (2018). </p> <p>- The 2009 Global Human Footprint map presented in: O. Venter <em>et al.</em>, <em>Nat. Communications.</em> <strong>7</strong>, 1–11 (2016). </p> <p>- The May 2020 and April 2018 versions of the World Database on Protected Areas, found at: UNEP-WCMC and IUCN, Protected Planet: the World Database on Protected Areas (WDPA), Cambridge, UK, (available at www.protectedplanet.com). </p> <p>Please read the Readme.txt for file details and cite the following paper if you use these data: Brennan, A., R. Naidoo, L. Greenstreet, Z. Mehrabi, N. Ramankutty and C. Kremen. Functional connectivity of the world's protected areas. Science (2022).</p> <p> </p>
Morphological and DNA sequence data generated by Sanger sequencing and target capture methods for moss plants in the genus Fissidens from herbarium specimens
<p><span>Morphological evolution in mosses has long been hypothesized to accompany shifts in microhabitats and can be tested using comparative phylogenetics. These lines of inquiry have developed substantially, in part, by target capture sequencing allowing for phylogenomic scale data generated from herbarium specimens. In the present study, we test the relationship between taxonomically important morphological characters in the moss genus <em>Fissidens</em>, using both a 400-locus dataset generated using a target-capture approach as well as a three-locus phylogeny generated using sanger sequencing. Phylogenetic trees were generated using ASTRAL and Bayesian Inference and used to test the monophyly of subgenera/sections and provided the basis for ancestral character reconstruction and phylogenetic correlation analyses among five morphological characters as well as habitat moisture scored from literature. The characters <em>axillary hyaline nodules</em>, <em>limbidium</em>, <em>costa</em>, and <em>peristome morphology</em> as well as <em>sexual system</em>, <em>minimum habitat moisture</em>, <em>average habitat moisture</em>, <em>maximum habitat moisture</em>, and <em>habitat moisture niche breadth</em> each exhibit statistically significant phylogenetic signal. Significant correlations were found between the limbidium (phyllid/leaf border) and habitat moisture niche breadth, which could be interpreted as a more extensive <em>limbidium</em> enabling species to survive across a wider variety of habitats. Correlations were also found between <em>costa anatomy</em> and the <em>limbidum</em> of the gametophyte and sporophyte <em>peristome</em> <em>morphology</em>, as well as <em>average habitat moisture</em> and <em>sexual system</em>. Continued exploration of the relationships between morphological evolution, life history, and habitat will enable us to expand our understanding of functional morphology in mosses.</span></p>
GouDa - Generation of universal Data Sets
<p>GouDa is a tool for the generation of universal data sets to evaluate and compare existing data preparation tools and new research approaches. It supports diverse error types and arbitrary error rates. Ground truth is provided as well. It thus permits better analysis and evaluation of data preparation pipelines and simplifies the reproducibility of results.</p> <p>Publication: <em>V. Restat, G. Boerner, A. Conrad, and U. Störl. GouDa - Generation of universal Data Sets. In Proceedings of Data Management for End-to-End Machine Learning (DEEM’22), Philadelphia, USA, 2022. https://doi.org/10.1145/3533028.3533311</em></p>
Raw Microscopy and Western Blot Data for "Distinct silencer states generate epigenetic states of heterochromatin"
<p>Raw microscopy and western blot images for "Distinct silencer states generate epigenetic states of heterochromatin"</p>
Code and data for "Global warming generates predictable extinctions of warm- and cold-water marine benthic invertebrates via thermal habitat loss"
<pre>This repository contains the following information: Datasets S1 to S4 can all be loaded, manipulated, and analysed in R using script provided in Data S5 to obtain the results of the paper, Reddin et al. 2022, "Global warming generates predictable extinctions of warm and cold-water marine benthic invertebrates via thermal habitat loss". Data S1. (separate file) The original downloaded PaleoDB dataset. Data S2. (separate file) The pre-prepared dataset of occurrences. Data S3. (separate file) The finished environmental dataset. Data S4. (separate file) Additional environmental dataset. Data S5. (separate file) The R-code for the main analysis. Data S6. (compressed directory) Output data and code from the simulations. Table S7 (separate file). List of data source publications for PaleoDB data used in our study. Listed are the data source author list (ref_author), year (ref_pubyr), and reference number as appears in the PaleoDB (reference_no). </pre>
Self-generated LBNP data
<p>Raw and analyzed data from self-generated LBNP testing. Further details in the README. </p>
Data set on soil physicochemical parameters, biomass accumulation and carbon credit generation in different management systems in Rio Verde, GO, Brazil
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on soil organic carbon and its role in mitigating climate change through carbon sequestration on agricultural lands in Rio Verde, GO, Brazil. With the global imperative to reduce anthropogenic CO2 emissions, our data highlights the effectiveness of no-till agricultural practices in both improving soil quality and enhancing carbon storage. This collection represents extensive soil and biomass sampling from five distinct areas within the Cerrado region, utilizing three priority management systems:</p> <p>No-till with soybean and maize in sequence under rainfed conditions. No-till with soybean and maize in sequence with central pivot irrigation. First and second cuts of sugarcane. The samples were meticulously collected post-harvest and used to estimate both soil biomass accumulation and carbon stock indices. A thorough analysis of the soil's physicochemical parameters was conducted for the 0-20 cm soil profile in each area. This dataset not only provides a valuable resource for studying the impact of different no-till practices on carbon sequestration but also serves as a critical input for modeling future contributions of conservation management systems to carbon trading markets.</p> <div> <div> </div> <div> <h2>Data Contents</h2> </div> <p>Soil organic carbon measurements for various no-till systems. Biomass accumulation data post-harvest. Carbon stock indices derived from biomass samples. Detailed physicochemical profiles of soil samples.</p> <div> <h2>Significance</h2> </div> <p>This dataset is pivotal for researchers and policymakers focusing on the potentials of agricultural carbon sequestration and its implications for carbon trading schemes. It offers insights into the current contributions of no-till conservation management systems and aids in the development of future strategies to enhance carbon</p> <h1>Metadata Description and Script</h1> </div> <p>This repository contains two key data files that encapsulate diverse aspects of soil physicochemical parameters, biomass accumulation, and carbon credit generation across different management systems in Rio Verde, GO, Brazil. Below are descriptions of each file's contents and structure.</p> <div> <h2>all.txt</h2> </div> <p>This text file presents aggregated data from various sites under different agricultural management systems. Each row in the dataset represents measurements from distinct sample plots, with the following fields:</p> <ul> <li><code>Sites</code> - Identifier for the plot location.</li> <li><code>SB</code> - Soil bulk density (g/cm³).</li> <li><code>SOC</code> - Soil organic carbon (%).</li> <li><code>Stock</code> - Carbon stock (ton/ha).</li> <li><code>Biomass</code> - Biomass accumulation (ton/ha).</li> <li><code>Credits</code> - Estimated carbon credits (ton CO2 equivalent/ha).</li> </ul> <div> <h2>Quimica.xlsx</h2> </div> <p>This Excel file provides detailed physicochemical analyses of soil samples from different management zones in the study area. The data is structured to support in-depth analysis of soil characteristics influencing carbon sequestration capabilities. Each sheet in the workbook corresponds to a specific area, with columns typically representing:</p> <ul> <li><code>pH</code> - Soil pH, indicating the acidity or alkalinity.</li> <li><code>EC</code> - Electrical conductivity (dS/m).</li> <li><code>Cation Exchange Capacity (CEC):</code> - (meq/100g).</li> <li><code>Organipont c Matter:</code> - (%).</li> <li><code>NPK levels</code> - Concentrations of Nitrogen (N), Phosphorus (P), and Potassium (K).</li> </ul>
Data from: Crows 'count' the number of self-generated vocalizations
<p>Producing a specific number of vocalizations with purpose requires a sophisticated combination of numerical abilities and vocal control. Whether any animal possesses such a capacity to voluntarily control the number of self-generated vocalizations is yet unknown. We demonstrate that crows can flexibly produce a variable number of one to four vocalizations in response to arbitrary cues associated with numerical values. The acoustic features of the first vocalization of a sequence were predictive of the total number of vocalizations, indicating a planning process. Moreover, the acoustic features of vocal units were foretelling of their order in the sequence and could be used to read-out counting errors during vocal production. Together, the crows' vocal enumeration capability could be an evolutionary precursor to symbolic counting found uniquely in humans.</p>
Open-source quality control routine and multi-year power generation data of 175 PV systems
<p><strong>Description</strong></p> <p>The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution.</p> <p><strong>PV power measurements</strong></p> <p>Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in <em>unfiltered_pv_power_measurements.csv</em>. Filtered power measurements are included as <em>filtered_pv_power_measurements_sc.csv </em>and<em> filtered_pv_power_measurements_ac.csv</em>. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in<em> metadata.csv</em>. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary).</p> <p><strong>Quality control routine</strong></p> <p>An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (<em>qcpv.py</em>). Sample code to call and run the functions in the qcpv package is available as <em>example.py.</em></p> <p><strong>Objective</strong></p> <p>By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid.</p> <p>By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine. </p> <p><strong>Data usage</strong></p> <p>If you use the data and/or python package in a published work please cite: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Units</strong></p> <p>Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00).</p> <p>Power measurements are in Watt.</p> <p>Installed capacities (DC and AC) are in Watt-peak.</p> <p><em><strong>Additional information</strong></em></p> <p>A detailed discussion of the data and qcpv package is presented in: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. </em><em>Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Acknowledgements </strong></p> <p>This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign. </p>
Data from: Asynchronous life histories generate uneven arms races and impact the maintenance of mutualisms
<p>Mutualisms constitute a diverse class of ecologically important interactions, yet their ecological and evolutionary stability remain topics of debate in coevolutionary theory. Recent theoretical and empirical work has suggested that coevolutionary arms races may be involved in the maintenance of mutualistic interactions, sustaining mutually beneficial outcomes for interacting species while producing exaggerated traits. Here we present an individual-based model that evaluates how asynchronous life histories – i.e., partners with different average lifespans – change the dynamics of trait coevolution, the expected fitness outcomes for species involved, and the dynamics of selection differentials across time for each species. Results indicate that a longer-lived mutualist will consistently 'lose' an otherwise balanced coevolutionary arms race, being outpaced in both the mean trait value and fitness outcome compared to a shorter-lived partner. Furthermore, linear selection differentials on mutualistic traits become increasingly divergent as life histories become increasingly asynchronous, with the longer-lived species experiencing persistent directional selection and the shorter-lived species experiencing weaker, more inconsistent selection. These results suggest that asynchronous life histories can complicate the maintenance of mutualistic interactions via coevolutionary arms-races and that detecting coevolution via selection differentials may be difficult when life histories are sufficiently divergent.</p>
Supporting material for: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data
<p>Supplementary material for the paper called: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data. The data without "indexes" in the name are map-projected M3 cubes, they can be used in the python library <i><strong>MoonIndex </strong></i>to obtain the spectral indexes stored in the files with "indexes" in the name.</p><p>This research was done on the framework of the EXPLORE project, that has received funding from the European Union's 2020 research and innovation program under grant agreement No 101004214. </p>
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
<p>Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. <em>In vitro</em> human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing <em>in vitro</em> and <em>in vivo</em> models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted <em>in vitro</em> to <em>in vivo</em> therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (<em>i.e.</em>, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (<em>i.e., </em>quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable <em>in vitro</em> proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.</p>
Assessment of Fair Trade education programs in France: data 2022 from the control group and those from the two experimental fields (the Fair Generation scheme and the Fair Trade Universities Label)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></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.