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13,499 results for “researcher”
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 7. The projects's Twitter page for educational and research micro-blogging
<p>The fourth stage was the use of social media to build a social learning network based on content and experience sharing and creation. The following web 2.0 services were used as a distributed platform able to support our experimental learning system: a) Panoramio (http://www.panoramio.com/user/7606828) (Figure 6) as a geo-referenced photo-sharing service over Google Maps and Google Earth for sharing project’s essential results; b) Twitter (https://twitter.com/maps_of_time) (Figure 7), as a social network and micro-blogging service for short announcements and comments; c) Google+ (https://plus.google.com/114705936110835992130?hl=en#114705936110835992130/posts?hl=en) (Figure 8), as a platform for sharing and tagging multiple content (photo, video), blogging and video chatting service with the recent Google Hangout, for sharing educational content; d) Google Drive (Levin, 2013) for cloud storage and collaborative document editing. We also created a YouTube channel for public distribution of video content (https://www.youtube.com/TimemapsNet), (Rusu et al., 2013) and a Facebook page of Vădastra School (https://www.facebook.com/scoalaVădastra). </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 1. Methodology used in the research by (Egmont-Petersen et al., 2002)
<p>Figure 1 shows the methodology used by Egmont-Petersen et al. (2002) to come up with an answer to their research question. Their study says that image recognition using neural networks goes through the stages shown in Figure 1. In our research we shall use this general proposed approach. </p>
Dataset for: Developing research data management services and support for researchers: a mixed methods study
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115.</p> <p>Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a></p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset: </p> <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> <p>Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a></p>
Preprints Servers as a Hub for Early-Stage Research Outputs
<p>Data for "Preprints as a Hub for Early-Stage Research Outputs"</p> <p>This data set contains three documents results from a survey focused on how <br> preprint servers relate to open science. See http://researchpreprints.com/2017/12/18/a-short-research-project-where-do-preprints-fit-in/<br> for the research plan.</p> <p> - submission systems and websites.xlsx</p> <p>A check of submission pages for preprint servers. Opportunities to link to other early-stage<br> research outputs were recorded. Information displayed on websites was also checked on an <br> ad hoc basis.</p> <p> - preprint abstracts.xlsx<br> <br> A check of 25 or 50 preprints for selected preprint servers, to see what information linking<br> to other early-stage research outputs was visible.</p> <p> - preprint operator survey<br> <br> Individual responses from a survey of those operating preprint servers. Names and email addresses<br> were collected but are not reported.</p>
Research data for: On the persistent shape and coherence of pulsating auroral patches
<p>Data for the research article: On the persistent shape and coherence of pulsating auroral patches in Journal of Geophysical Research: Space physics. For example all-sky imager movies of 557.7 nm pulsating/fluctuating aurora on 1 March 2012. </p>
Using the Socialise app to collect smartphone sensor data for mental health research: A feasibility study
<p>To investigate the feasibility of collecting smartphone sensor data for mental health research, we tested the Socialise app that was developed at the Black Dog Institute in a group of people with a lived experience of mental health challenges (n=32). Bluetooth, GPS and battery status data were collected at regular intervals (3, 4, 5 or 8 minutes) for 4 weeks. In addition, survey data was collected using the app to investigate the views of participants on user experience and the acceptability of passive data collection for mental health research. No mental health data was collected as part of the feasibility study.</p>
Research data supporting "Fractal-like hierarchical organisation of bone begins at the nanoscale"
<p>Raw research data supporting: N. Reznikov et al., Science 360, eaao2189 (2018). DOI: 10.1126/science.aao2189</p>
Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models
<p>Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models This repository contains the supplemental material for the <a href="https://pqdtopen.proquest.com/pubnum/10759956.html">thesis "Exploring Complexity Metrics for Artifact-Centric Business Process Models" by Marin, Mike A., Ph.D., University of South Africa (South Africa), 2017.</a></p>
Manipulation of netCDF data with R for climate change research: Multi-model analysis for CMIP5 models.
<p>Geoscientists now live in a world with an exponential growth in digital data and methods.<br> Climate change studies usually describe computational methods informally. Climate scientists seek to<br> share their information, the justification of reproducible research has received increasing attention in<br> geosciences. To have it in an open-source format makes it easier to interchange not only with fellow<br> scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source<br> computer language powerful and highly extensible that can promotes reproductive science techniques in a<br> easier way. R is highly accessible for non-computational scientists when coupled with packages like<br> ‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out<br> complex data analysis. In this paper we have assessed the power of R language for manipulating climate<br> data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we<br> have proposed an example of best practices to handle model ensembles. This is the first study to our<br> knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster<br> package capabilities provides efficient access to the multi-model, with crucial applications in climate<br> change research. In recent years more than 100 peer-reviewed scientific publications have used the<br> CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools<br> to author papers and disseminate information about the process and products of their research.</p>
Research data supporting "A robust liposomal platform for direct colorimetric detection of sphingomyelinase enzyme and inhibitors"
<p>Raw research data supporting the publication: Holme, M. N. et al., ACS Nano, 2018, DOI: 10.1021/acsnano.8b03308.</p>
Research data supporting "Fate of Liposomes in Presence of Phospholipase C and D: From Atomic to Supramolecular Lipid Arrangement"
<p>Raw research data for experimental work supporting the publication above.</p> <p>Raw data for MD simulation is available upon reasonable request from Irene Yarovsky (irene.yarovsky@rmit.edu.au).</p>
Research data supporting "Post-polymerisation functionalisation of conjugated polymer backbones and its application in multi-functional emissive nanoparticles"
<p>Research data supporting the publication:</p> <p>Creamer A. et al., "Post-polymerisation functionalisation of conjugated polymer backbones and its application in multi-functional emissive nanoparticles",<em> Nature Communications</em><strong>, 9</strong>:3237 (2018).</p>
Research data supporting "Duplex-Specific Nuclease-Amplified Detection of MicroRNA Using 2 Compact Quantum Dot−DNA Conjugates"
<p>Raw research data supporting the publication:</p> <p>Wang, Y. et al., 2018, ACS Applied Materials & Interfaces, "Duplex-Specific Nuclease-Amplified Detection of MicroRNA Using 2 Compact Quantum Dot−DNA Conjugates", DOI: 10.1021/acsami.8b07250.</p>
Research data supporting "Engineering anisotropic muscle tissue using acoustic cell patterning"
<p>Raw research data supporting the publication:</p> <p>Armstron, JPK et al., "Engineering anisotropic muscle tissue using acoustic cell paterning", Advanced Materials, DOI: 10.1002/adma.201802649 (2018)</p>
Original reconstructions for the research in "X-ray nanotomography of individual pulp fibre bonds reveals the effect of wall thickness on contact area"
<p>The following data set contains the original cropped and straightened reconstruction stacks of all the cellulose fibre bond samples imaged for the study outlined in the article<strong> “</strong>X-ray nanotomography of individual pulp fibre bonds reveals the effect of wall thickness on contact area” by T. Sormunen, A. Ketola, A. Miettinen, J. Parkkonen and E. Retulainen. The article is currently (23.11.2018) in decision phase.</p> <p>In addition, the algorithm for reconstruction stack processing conducted in ImageJ and the MATLAB function for contact area and pixelwise correlation calculations are included.</p>
Research data supporting "Single particle automated raman trapping analysis"
<p>Research raw data supporting the publication:</p> <p>Penders J., et al., Nature Communications. (2018) 9:4256 | DOI: 10.1038/s41467-018-06397</p>
W2Share Case Study: Workflow Research Object (WRO)
<p>Case Study - Molecular Dynamics</p> <p>Our case study is based on a molecular dynamics simulation defined in the following article:</p> <p>Silveira, R.L. and Skaf, M. S. Molecular Dynamics Simulations of Family 7 Cellobiohydrolase Mutants Aimed at Reducing Product Inhibition. J. Phys. Chem. B 119, 9295-9303 (2015). DOI: <a href="https://doi.org/10.1021/jp509911m">https://doi.org/10.1021/jp509911m</a></p>
Research raw data supporting "Activatable cell-biomaterial interfacing with photo-caged peptides"
<p>Raw data supporting the publication;</p> <p>Lin Y. et al., Activatable cell-biomaterial interfacing with photo-caged peptides, 2018, Chemical Science, DOI: 10.1039/c8sc04725a.</p>
Market Research - What's become... of new entrants in research workflows and scholarly communication? ('long list')
<p>Market Research - What’s become... of new entrants in research workflows and scholarly communication?</p> <p>Full report to be published as preprint on OSF Preprints shortly.</p> <p>To systematically create a list of the new entrants in research workflows and scholarly communication that I’ve seen over the years, I used a range of resources, listed hereunder:</p> <ul> <li>400+ Tools and innovations in scholarly communication (charting the <a href="http://bit.ly/innoscholcomm-list">creation and availability</a> (supply side))<a href="#_edn1">[i]</a>. This database continues to build on the 2015-2016 survey by Bianca Kramer & Jeroen Bosman (both at Utrecht University Library), who are interested in the way information is created, shared, and processed in academia<a href="#_edn2">[ii]</a>. From this source I pulled 683 players. From the answers to the open and closed questions in the 2015-2016 survey I found 225 players.</li> <li>Presenters at STM events<a href="#_edn3">[iii]</a> from 2010-2017. This contributed 53 players.</li> <li>Outsell<a href="#_edn4">[iv]</a> ‘Companies to watch’, as per their annual Information Industry Outlook (2009-2017). This source provided 29 players.</li> <li>Pitches at APE (Academic Publishing in Europe) Conferences<a href="#_edn5">[v]</a> between 2011-2017. Here I found 44 players.</li> <li>Nominees for the ALPSP Award for Innovation in Publishing. This source contributed 10 players from 2014-2017.<a href="#_edn6">[vi]</a>.</li> </ul> <p>This file contains the 'long list' per 31 December 2018.</p> <p><a href="#_ednref1">[i]</a> https://docs.google.com/spreadsheets/d/1KUMSeq_Pzp4KveZ7pb5rddcssk1XBTiLHniD0d3nDqo/edit#gid=0</p> <p><a href="#_ednref2">[ii]</a> https://101innovations.wordpress.com/</p> <p><a href="#_ednref3">[iii]</a> https://www.stm-assoc.org/events/?previous</p> <p><a href="#_ednref4">[iv]</a> https://www.outsellinc.com/</p> <p><a href="#_ednref5">[v]</a> https://www.ape2019.eu/ape-literature</p> <p><a href="#_ednref6">[vi]</a> https://www.alpsp.org/Awards</p>
Market Research - What's become... of new entrants in research workflows and scholarly communication? ('sample V2')
<p>Market Research - What’s become... of new entrants in research workflows and scholarly communication?</p> <p>Full report to be published as preprint on OSF Preprints shortly.</p> <p>To systematically create a list of the new entrants, a range of approaches and sources were used. This resulted in a long list (see: <a href="https://zenodo.org/record/2530048#.XDnfnFxKjIV">https://zenodo.org/record/2530048#.XDnfnFxKjIV</a>).</p> <p>The ‘long list’ was shortened to a sample of 120 independent for-profit startups through various filtering exercises, described in the full report. For the sample, three questions were investigated: 1. Did they still exist (independently) in 2018? 2. If so, how were they funded and how were they doing? 3. If they were acquired by 2018, by whom and when were they taken over?</p> <p>This file contains the ‘sample’ and answers to the research questions per 31 December 2018.</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.