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747 results for “Open Data”
Supplementary Data and Scripts for the Paper "Synchronous Development in Open-Source Projects: A Higher-Level Perspective"
<p>Anonymized data and scripts used to produce the results of the paper "Synchronous Development in Open-Source Projects: A Higher-Level Perspective".</p>
Open data for "High-performance lasers for fully integrated silicon nitride photonics"
<p>Available data for the results presented in "High-performance lasers for fully integrated silicon nitride photonics".</p>
Data from: Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community
<p>It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on > 100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 years are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human-environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community's use of NEON data, and opportunities for the next 10 years of NEON operations in emergent science themes, open science best practices, education and training, and community building.</p>
Figure 2 from: Kress W, Knapp S, Stoev P, Penev L (2012) On the front line of modern data-management and Open Access publishing: Two years of PhytoKeys – the fastest growing journal in plant systematics. PhytoKeys 19: 1-8. https://doi.org/10.3897/phytokeys.19.4501
Figure 2 - Taxonomic distribution by family of the published nomenclatural novelties in PhytoKeys.
Full Data Set for the Paper: An Evaluation of Open Source Trajectory Planners for Robotic Manipulators with Focus on Human-Robot Collaboration
<p>This data sets contain the full evaluation data for the paper "An Evaluation of Open Source Trajectory Planners for Robotic Manipulators with Focus on Human-Robot Collaboration".</p>
Wu et al. Polysulfamide paper data - open access
<p>Data used in the main manuscript of "Investigating hydrogen bond-induced self-assembly of polysulfamides using molecular simulations and experiments" by Wu et al., published on Macromolecules in 2023</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Research data for "Stability and decomposition pathways of the NiOOH OER active phase of NiOx electrocatalysts at open circuit potential traced by ex-situ and in-situ spectroscopies"
<p>This dataset supports the paper " Stability and decomposition pathways of the NiOOH OER active phase of NiOx electrocatalysts at open circuit potential traced by ex-situ and in-situ spectroscopies" in Catalysis Science and Technology. The paper is online here: <a href="https://doi.org/10.1039/D3CY00674C">https://doi.org/10.1039/D3CY00674C</a></p>
Open Label Study to Collect Clinical Data to Document Clinical Performance and Safety in Total Knee Arthroplasty
ClinicalTrials.gov study NCT04727060. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Open-label, Rollover Study Providing Continued Dosing of Gevokizumab in Order to Assess Long-term Gevokizumab Safety Data
ClinicalTrials.gov study NCT02258854. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Open Label Data Collection Study of My Connect Post-Op, a Post-Operative Communication Tool
ClinicalTrials.gov study NCT07120191. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Project Open - Use of Administrative Health Data to Increase Diabetic Retinopathy Screening
ClinicalTrials.gov study NCT05074342. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Open Label,Observational, Real Time Data Capturing of Usage & Outcome of Coseal
ClinicalTrials.gov study NCT01706640. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Video education versus face-to-face education on inhaler technique for patients with well-controlled or partly-controlled asthma: a phase IV, open-label, non-inferiority, multicenter, randomized, controlled trial
Open the record for dataset details and reuse information.
Data from: Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community
Open the record for dataset details and reuse information.
Data from: Genome-wide evidence supports mitochondrial relationships and pervasive parallel phenotypic evolution in open-habitat chats
Open the record for dataset details and reuse information.
Data from: Gigapixel big data movies provide cost‐effective seascape scale direct measurements of open‐access coastal human use such as recreational fisheries
Open the record for dataset details and reuse information.
Global Roads Open Access Data Set, Version 1 (gROADSv1)
The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.
Integrated Analysis of Whole-Genome ChIP-Seq and RNA-Seq Data of Primary Head and Neck Tumor Samples Associates HPV Integration Sites with Open Chromatin Marks (ChIP-Seq)
GEO Series GSE112021. Homo sapiens. 26 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A genome-wide analysis of open chromatin in human epididymis epithelial cells reveals novel candidate regulatory elements for male infertility [Transcriptome data]
GEO Series GSE50605. Homo sapiens. 3 samples. Type: Expression profiling by array.
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.