Find research datasets worth reusing

opencc-zeroApr 2022View details →

Google Scholar search record using GSscraper app: 2022-04-19

File generated: Search date, time, timezone: 2022-04-19 16:54:44 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish&hl=en&as_vis=0&as_sdt=2007

opencc-by-4.0May 2022View details →

"Do you want to know who you are?" The rise of genetic ancestry testing and the search for genealogies: an anonymized survey from Sweden

Full, anonymized survey data on genetic genealogy, ancestry and identity conducted by the Swedish Genealogical Society as part of a research project funded by the HERA joint research program "Uses of the Past"

A Multi-domain Benchmark for Personalized Search Evaluation

We provide large-scale multi-domain benchmark datasets for Personalized Search. Further information can be found <a href="https://github.com/AmenRa/a-multi-domain-benchmark-for-personalized-search-evaluation">here</a>.

Searching records on Oasisbr

Data generated in the context of research over recovering information in a specific field (Arts) on Oasisbr < https://oasisbr.ibict.br/ >.

Fermi-GBM Data Release Related to Searches for Neutrinos from Gamma-Ray Bursts using the IceCube Neutrino Observatory

This data release includes Fermi Gamma-ray Burst Monitor (GBM) localizations used in searches for neutrinos from gamma-ray bursts (GRB) by the IceCube Neutrino Observatory. These localizations are provided publicly to the community since they are generally useful for any analysis that needs the Fermi-GBM localization for a GRB. Full Details: The files contained herein are HEALPix representations of GRB localizations from the Fermi-GBM stored as FITS files and produced according to the automated method described in [1]. Each file represents the probability density (statistical + systematic) for the true source location. By definition, this excludes the Earth occulted region of the sky, which is set to 0 due to the fact that real sources are not visible through the Earth. These files cover a time range spanning the first detection of GRBs by GBM in July 2008 through July 2019 and should be considered preliminary.  The files are preliminary in the sense that they contain some key differences to the official files hosted at HEASARC FTP server through the Fermi Science Support Center (FSSC; <a href="https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/">https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/</a>).  We list the key differences here: <ul> <li>Fermi began production HEALPix FITS files in early 2018, and files prior to that have not been officially provided.  The files in this archive are currently the only version of HEALPix files pre-2018.  </li> <li>These files were not produced via the standard GBM operational pipeline; however they were produced with the same functional code that is used to make the files. The result of this is that the standard quality checks on the FITS headers by uploading to the FSSC were skipped.  The primary header is most affected, with some null values, but these null values do not affect the HEALPix data.  </li> <li>These localizations may have centroids that are slightly different than reported in the online catalog.  This is because an automated algorithm for localization (RoboBA) was used to localize the GRBs and produce these files as opposed to the manual Human-in-the-Loop localization performed for every GRB prior to 2016, and ~15% of GRBs thereafter [1].  </li> <li>These localizations contain an updated and improved systematic uncertainty model compared to the pre-July 2019 localizations at the FSSC. The new systematic uncertainty model is explained in [1], while the older localizations at the FSSC contain a systematic uncertainty model from [2].  </li> <li> In general, the official localizations hosted at the FSSC currently do not remove localization probability that overlaps the Earth, but these files do remove the probability that overlaps the Earth and renormalizes the remaining PDF.  This encodes the assertion that the localization is indeed of an astrophysical nature.</li> </ul> The FITS files are organized with two HDUs: <ul> <li> PRIMARY HDU with some basic metadata about the mission from which the data originated  </li> <li> HEALPIX HDU containing header information about the GBM detector pointings, as well as the Sun and Geocenter localizations with respect to Fermi. There are two data fields contained in the extension: <ul> <li> PROBABILITY: the differential localization probability per pixel (NSIDE=128)</li> <li> SIGNIFICANCE: integrated probability for estimating confidence intervals (NSIDE=128)</li> </ul> </li> </ul> Furthermore, we provide images of each localization.  The images are a Mollweide projection of the sky, with the 50% and 90% localization confidence regions marked in shaded purple.  The location of the Earth from Fermi's perspective is marked in shaded blue. The GBM trigger number associated with each FITS file and image is listed in the filename. References: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ab8bdb">[1] Goldstein, A. et al. 2020, ApJ, 895, 40</a> <a href="https://iopscience.iop.org/article/10.1088/0067-0049/216/2/32/meta">[2] Connaughton, V. et al. 2015, ApJS, 216, 32</a>

Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions

The archive "dataset.tar.gz" contains trained models (neural networks), training-, validation- and test-data and selected structures in POSCAR format, obtained in the neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions. See README for more information on the archive content.

opencc-zeroAug 2022View details →

dryad40/100

ATP binding facilitates target search of SWR1 chromatin remodeler by promoting one-dimensional diffusion on DNA

One-dimensional (1D) target search is a well-characterized phenomenon for many DNA-binding proteins but is poorly understood for chromatin remodelers. Herein, we characterize the 1D scanning properties of SWR1, a conserved yeast chromatin remodeler that performs histone exchange on +1 nucleosomes adjacent to a nucleosome-depleted region (NDR) at gene promoters. We demonstrate that SWR1 has a kinetic binding preference for DNA of NDR length as opposed to gene-body linker length DNA. Using single and dual color single-particle tracking on DNA stretched with optical tweezers, we directly observe SWR1 diffusion on DNA. We found that various factors impact SWR1 scanning, including ATP which promotes diffusion through nucleotide binding rather than ATP hydrolysis. A DNA-binding subunit, Swc2, plays an important role in the overall diffusive behavior of the complex, as the subunit in isolation retains similar, although faster, scanning properties as the whole remodeler. ATP-bound SWR1 slides until it encounters a protein roadblock, of which we tested dCas9 and nucleosomes. The median diffusion coefficient, 0.024 μm2/s, in the regime of helical sliding, would mediate rapid encounter of NDR-flanking nucleosomes at length scales found in cellular chromatin.

opencc-by-4.0Sep 2022View details →

Extensive literature search on organic arsenic in food

This record is a supplement to the external scientific report titled Extensive literature search on organic arsenic in food available at https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2022.EN-7565 Annex A Summary tables Annex B -  Evaluated references The archive contains references grouped in 2 folders: included references and excluded references. Annex C – Endnote library files The  archive contains: - the EndNoteTM  file  “EFSA_Arsen_complete”  with all  retrieved  references  after  duplicate  check organised  on  the  one  hand  by  individual  literature  databases  and  on  the  other  hand  by  areas  and substance group, as well as screening for relevance. - the EndNote file “EFSA_EndNote_summary tables” with all assigned relevant references for the summary tables which are organised by relevant area and substance group.

opencc-by-4.0Sep 2022View details →

Data release associated with ``Search for Coincident Gravitational Wave and Long Gamma-Ray Bursts from 4-OGC and the Fermi-GBM/Swift-BAT Catalog"

This is associated data release for the paper https://arxiv.org/abs/2208.03279. It contains the skymaps from potential gravitational-wave candidates from binary neutron star or neutron star-black hole merger. The notebook showcases how to use it. More information can be found in the github repository: https://github.com/gwastro/gw-longgrb <pre>  </pre> <pre>  </pre>

opencc-by-4.0Oct 2022View details →

Annexes to the Extensive literature search report on N-nitroso compounds in food

The overall purpose of this project was the identification and selection of relevant literature to gather information on chemical identification and characterisation, sources and occurrence in food as well as data on toxicokinetics and toxicity of N-nitrosocompounds in order to support the preparatory work for the human health risk assessment of this type of substances.

opencc-by-4.0Dec 2017View details →

Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study

This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.

opencc-by-4.0Dec 2017View details →

patteRNA: transcriptome-wide search for functional RNA elements via structural data signatures, Datasets.

Datasets, code and results supporting the manuscript: Ledda M. & Aviran S., patteRNA: transcriptome-wide search for functional RNA elements via structural data signatures

Literature Search from SECURE Deliverable 1.1: STATE-OF-THE-ART on Research Career Frameworks

SECURE Deliverable 1.1: STATE-OF-THE-ART on Research Career Frameworks (<a title="SECURE Deliverable 1.1: STATE-OF-THE-ART on Research Career Frameworks" href="../doi/10.5281/zenodo.10066374">https://zenodo.org/doi/10.5281/zenodo.10066374</a>) included a State-of-the-Art on existing literature and recommendations related to research career frameworks (RCFs) and focusing on recruitment and working conditions for researchers, career development and progression for researchers, and interinstitutional (between academic institutions), intersectoral (across sectors), and international (across countries) mobility. The literature review data for is available as: <ul> <li>an online library on Zotero - <a href="https://www.zotero.org/groups/5436703/secure_project_library/library">https://www.zotero.org/groups/5436703/secure_project_library/library</a></li> <li>Microsoft Excel files (xlsx) and</li> <li>CSV (comma-separated values) files.</li> </ul> The selected literature is separated into the following headings: <ul> <li>Career Development and Progression for Researchers</li> <li>Interinstitutional, Intersectoral, and International Mobility</li> <li>Recruitment and Employment Conditions for Researchers</li> <li>Research Career Frameworks</li> </ul>

Literature Search from SECURE Deliverable 1.2: STATE-OF-THE-ART on Tenure Track-Like Models

SECURE Deliverable 1.2: STATE-OF-THE-ART on Tenure Track-Like Models (<a title="SECURE Deliverable 1.2: STATE-OF-THE-ART on Tenure Track-Like Models" href="https://doi.org/10.5281/zenodo.10066388">https://doi.org/10.5281/zenodo.10066388</a>) included a State-of-the-Art on Tenure Track-Like Models. The literature review data is available as: <ul> <li>an online library on Zotero - <a href="https://www.zotero.org/groups/5436703/secure_project_library/library">https://www.zotero.org/groups/5436703/secure_project_library/library</a></li> <li>Microsoft Excel files (xlsx) and</li> <li>CSV (comma-separated values) files.</li> </ul> The selected literature is separated into the following headings: <ul> <li>Career development and assessment for tenure track-like models</li> <li>Overall Literature for Tenure Track-Like Model</li> <li>Review of Funding Schemes for Tenure Track-Like Models</li> <li>Review of recruitment and employment conditions for tenure track-like models</li> </ul>

opencc-by-4.0May 2024View details →

Literature Search from OPUS Deliverable 1.2: Initial State of the Art on Open Science Literature

OPUS Deliverable 1.2: Initial State of the Art on Open Science Literature (<a title="OPUS Deliverable 1.2: Initial State of the Art on Open Science Literature" href="../doi/10.5281/zenodo.8410049" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.8410049</a>) conducted an analysis of initiatives and literature to reform research(er) assessment and incentivise and reward Open Science. Within WP1, a State-of-the-Art on Open Science Literature was conducted. This state-of-the-art on existing literature and recommendations to reform research(er) assessment and incentivise and reward Open Science was designed to support the development of interventions in WP2, of indicators and metrics in WP3 and of pilot action plans in WP4. Within this overall review, specific focus was placed on a review of: <ul> <li>Research(er) assessment and Open Science and incentives and rewards and Open Science</li> <li>Precarity of research careers and Open Science</li> <li>Gender equality and Open Science</li> <li>Industry practices and Open Science</li> <li>Trust and Open Science</li> </ul> This literature review data is available as: <ul> <li>an online library on Zotero - <a title="OPUS Zotero Library" href="https://www.zotero.org/groups/4932671/opus_project_library/collections/H4KBRTUF" target="_blank" rel="noopener">https://www.zotero.org/groups/4932671/opus_project_library/collections/H4KBRTUF</a></li> <li>a single Microsoft Excel file (xlsx) with multiple tabs and</li> <li>CSV (comma-separated values) files.</li> </ul>

Disentangling Web Search on Debated Topics - User Study Data

Data of an exploratory, open-ended user study (N = 255) to advance knowledge and uncover relations between the different facets of web search on debated topics. We explored the relations between factors inherent to the searcher and search system (user characteristics, exposure bias), search intercations (confirmation bias, position bias, search effort), and post-search epistemic states (attitude change, knowledge gain). This data set contains the following variables for each of the 255 participants: SERP ranking bias, prior knowledge, attitude strength, receptiveness to opposing views, attitude-confirming clicks, click rank deviation, number of clicks, time on SERP, hover depth, attitude change, knowledge gain.

Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"

This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data: A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0. In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing. Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components: <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> Number of samples: <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> An example of few lines of python code to read each file is: <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3].  This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan. [1] https://gwpy.github.io [2] https://pycbc.org [3] https://lscsoft.docs.ligo.org/lalsuite

opencc-by-4.0Nov 2015View details →

Figure 4. Extracted bands diagramClassification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Based on that coefficient, different bands have to be extracted. The bands are alpha, beta, theta, gamma, and delta. Figure 4 shown in below which is represent the different extract band diagrams.

opencc-by-4.0Aug 2015View details →

Figure 3. Electrode placement diagram-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Reference electrode placed AFz placed in between AF1 and AF2 electrode and ground electrode Oz is placed between O1 and O2 electrodes. The impedance of the electrode range is 5KΩ. The sampling rate was fixed range between 256 samples per second for all the channels. Figure 3 shown in below which is represent the electrode placement diagram. The recorded EEG signal is used to recognize the different level of emotions.