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Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Science Communication Webinar Series: Engaging with Policymakers
<p>Blue-Action coordinated a series of webinars on climate science communication in 2020. These are aimed at early-career researchers, but anyone who wishes to engage people with their research at any stage will find them valuable.</p> <p>This webinar was delivered on 15th October 2020 by Jan-Stefan Fritz, Managing Director of the German Marine Research Consortium. Stefan has over fifteen years experience linking science with European policymakers in Brussels, and here shares his wealth of experience in how and why we should be engaging with policy. </p>
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
Pre-Processed Power Grid Frequency Time Series
<p><strong>Overview</strong><br> This repository contains ready-to-use frequency time series as well as the corresponding pre-processing scripts in python. The data covers three synchronous areas of the European power grid:</p> <ul> <li>Continental Europe</li> <li>Great Britain</li> <li>Nordic</li> </ul> <p>This work is part of the paper "Predictability of Power Grid Frequency"[1]. Please cite this paper, when using the data and the code. For a detailed documentation of the pre-processing procedure we refer to the supplementary material of the paper.</p> <p><strong>Data sources</strong><br> We downloaded the frequency recordings from publically available repositories of three different Transmission System Operators (TSOs).</p> <ul> <li><strong>Continental Europe </strong>[2]: We downloaded the data from the German TSO <em>TransnetBW GmbH,</em> which retains the Copyright on the data, but allows to re-publish it upon request [3].</li> <li><strong>Great Britain </strong>[4]: The download was supported by National Grid ESO Open Data, which belongs to the British TSO <em>National Grid</em>. They publish the frequency recordings under the NGESO Open License [5].</li> <li><strong>Nordic</strong> [6]: We obtained the data from the Finish TSO <em>Fingrid</em>, which provides the data under the open license CC-BY 4.0 [7].</li> </ul> <p><strong>Content of the repository</strong></p> <p><strong>A) Scripts</strong></p> <ol> <li>In the "Download_scripts" folder you will find three scripts to automatically download frequency data from the TSO's websites.</li> <li>In "convert_data_format.py" we save the data with corrected timestamp formats. Missing data is marked as NaN (processing step (1) in the supplementary material of [1]).</li> <li>In "clean_corrupted_data.py" we load the converted data and identify corrupted recordings. We mark them as NaN and clean some of the resulting data holes (processing step (2) in the supplementary material of [1]).</li> </ol> <p>The python scripts run with Python 3.7 and with the packages found in "requirements.txt".</p> <p><strong>B) Yearly converted and cleansed data</strong><br> The folders "<year>_converted" contain the output of "convert_data_format.py" and "<year>_cleansed" contain the output of "clean_corrupted_data.py".</p> <ul> <li><strong>File type</strong>: The files are zipped csv-files, where each file comprises one year.</li> <li><strong>Data format</strong>: The files contain two columns. The second column contains the frequency values in Hz. The first one represents the time stamps in the format <em>Year-Month-Day Hour-Minute-Second</em>, which is given as naive local time. The local time refers to the following time zones and includes Daylight Saving Times (python time zone in brackets): <ul> <li>TransnetBW: Continental European Time (<em>CE)</em></li> <li>Nationalgrid: Great Britain (<em>GB</em>)</li> <li>Fingrid: Finland (<em>Europe/Helsinki</em>)</li> </ul> </li> <li><strong>NaN representation</strong>: We mark corrupted and missing data as "NaN" in the csv-files.</li> </ul> <p><strong>Use cases</strong><br> We point out that this repository can be used in two different was:</p> <ul> <li><strong>Use pre-processed data</strong>: You can directly use the converted or the cleansed data. Note however, that both data sets include segments of NaN-values due to missing and corrupted recordings. Only a very small part of the NaN-values were eliminated in the cleansed data to not manipulate the data too much.</li> </ul> <ul> <li><strong>Produce your own cleansed data</strong>: Depending on your application, you might want to cleanse the data in a custom way. You can easily add your custom cleansing procedure in "clean_corrupted_data.py" and then produce cleansed data from the raw data in "<year>_converted".</li> </ul> <p><strong>License</strong></p> <p>This work is licensed under multiple licenses, which are located in the "LICENSES" folder.</p> <ul> <li>We release the code in the folder "Scripts" under the MIT license .</li> <li>The pre-processed data in the subfolders "**/Fingrid" and "**/Nationalgrid" are licensed under CC-BY 4.0.</li> <li>TransnetBW originally did not publish their data under an open license. We have explicitly received the permission to publish the pre-processed version from TransnetBW. However, we cannot publish our pre-processed version under an open license due to the missing license of the original TransnetBW data.</li> </ul> <p><strong>Changelog</strong><br> Version 2:</p> <ul> <li>Add time zone information to description</li> <li>Include new frequency data</li> <li>Update references</li> <li>Change folder structure to yearly folders</li> </ul> <p>Version 3:</p> <ul> <li> <p>Correct TransnetBW files for missing data in May 2016</p> </li> </ul>
Fig. 2. Polygala bringelii J.F.B in Two new endangered species of Polygala series Trichospermae (Polygalaceae), endemic to the Cerrado domain, Tocantins State, Brazil
Fig. 2. Polygala bringelii J.F.B.Pastore & Antar sp. nov. A. Habitat. B. Habit. C–D. Flowering branches. E. Inflorescence detail. Photos: G.M. Antar, in situ.
Fig. 1. Polygala bringelii J.F.B in Two new endangered species of Polygala series Trichospermae (Polygalaceae), endemic to the Cerrado domain, Tocantins State, Brazil
Fig. 1. Polygala bringelii J.F.B.Pastore & Antar sp. nov. A. Flower. B. Bracteoles. C. Bract. D. Two upper outer sepals. E. Lower outer sepal. F. One of the two inner sepals (wings). G. Gynoecium. H. Crest. I. Keel. J. Androecium and lateral petals. K. Fruit with persistent calyx. L. Capsule. M. Seed. All from G.M. Antar and L. Nascimento 380. Photos: J.F.B. Pastore.
Fig. 5. Polygala tocantinensis J.F.B in Two new endangered species of Polygala series Trichospermae (Polygalaceae), endemic to the Cerrado domain, Tocantins State, Brazil
Fig. 5. Polygala tocantinensis J.F.B.Pastore & Antar sp. nov. A. Inflorescence detail. B. Habit. Photos: G.M. Antar, in situ.
Fig. 8 in Two new endangered species of Polygala series Trichospermae (Polygalaceae), endemic to the Cerrado domain, Tocantins State, Brazil
Fig. 8. Comparative plate of floral parts of P. bringelii J.F.B.Pastore & Antar sp. nov. (G.M. Antar & L. Nascimento 380), P. tocantinensis J.F.B.Pastore & Antar sp. nov. (J.M. Silva et al. 6769), and P. trichosperma Jacq. (J.R. Abbott 19728). A. Bracts and bracteoles. B. Flowers. C. Inner sepals (wings). D. Lower outer sepal. E. Two upper outer sepals. F. Androecium and lateral petals. G. Crest. H. Keel. I. Gynoecium. J. Seed. Photos: J.F.B. Pastore.
Fig. 7. Polygala trichosperma Jacq. A. Flower. B. Bracteoles. C. Bracts. D. Two upper outer sepals. E. Lower outer sepal. F in Two new endangered species of Polygala series Trichospermae (Polygalaceae), endemic to the Cerrado domain, Tocantins State, Brazil
Fig. 7. Polygala trichosperma Jacq. A. Flower. B. Bracteoles. C. Bracts. D. Two upper outer sepals. E. Lower outer sepal. F. One of the two inner sepals (wings). G. Gynoecium. H. Crest. I. Keel. J. Androecium and lateral petals. K. Capsule. L. Seed. All from J.R. Abbott 19728. Photos: J.F.B. Pastore.
Fig. 4. Polygala tocantinensis J.F.B in Two new endangered species of Polygala series Trichospermae (Polygalaceae), endemic to the Cerrado domain, Tocantins State, Brazil
Fig. 4. Polygala tocantinensis J.F.B.Pastore & Antar sp. nov. A. Flower. B. Bracteoles. C. Bracts. D. Two upper outer sepals. E. Lower outer sepal. F. One of the two inner sepals (wings). G. Gynoecium. H. Crest. I. Keel. J. Androecium and lateral petals. K. Fruit with persistent calyx. L. Capsule. M. Seed. All from J.M. Silva, J. Cordeiro and J. Vaz 6769. Photos: J.F.B. Pastore.
Fig. 3 in Two new endangered species of Polygala series Trichospermae (Polygalaceae), endemic to the Cerrado domain, Tocantins State, Brazil
Fig. 3. Geographical distribution of Polygala bringelii J.F.B.Pastore & Antar sp. nov. (circles). PEJ = Parque Estadual do Jalapão, EESGT = Estação Ecológica Serra Geral do Tocantins. The cream-colored portion of the insert map shows the extent of the Cerrado domain in Brazil.
Text-fig. 2. Synchrotron radiation X-ray tomographic microscopy volume renderings (a, b) and orthoslices (c–e) of Mugideiriflora portugallica gen. et sp. nov. from the Early Cretaceous Catefica locality, Portugal (holotype, S174254, Catefica sample 150). Yellow dots – stamens, red dots – carpels. a) Flower in lateral view showing the broad bases of the laminar tepals; b) Flower in longitudinal section showing the flat to slightly concave floral receptacle with a central conical gynoecial region (cut between orthoslices yz0800 and 1220); c) Flower in transverse section showing the numerous laminar tepals in several series and the stamens cut in the region of the poorly differentiated anthers; note cellular differences between outer (op) and inner (in) perianth parts, as well as and transverse sections of anthers, apparently with laterally to slightly dorsally placed pollen sacs (arrow heads) (cut at orthoslice xy0770); d) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0820); e) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0920); f) Flower in longitudinal section showing the shallowly concave floral receptacle with laminar tepals, stamens, and a central conical gynoecial region bearing poorly differentiated carpels (cut at orthoslice yz0900); g) Flower in longitudinal section perpendicular to that in (f) showing stamens and poorly differentiated carpels (cut at orthoslice xz1630). Scale bars = 1 mm (a, b), 500 µm (c–g). in Multiparted, Apocarpous Flowers From The Early Cretaceous Of Eastern North America And Portugal
Text-fig. 2. Synchrotron radiation X-ray tomographic microscopy volume renderings (a, b) and orthoslices (c–e) of Mugideiriflora portugallica gen. et sp. nov. from the Early Cretaceous Catefica locality, Portugal (holotype, S174254, Catefica sample 150). Yellow dots – stamens, red dots – carpels. a) Flower in lateral view showing the broad bases of the laminar tepals; b) Flower in longitudinal section showing the flat to slightly concave floral receptacle with a central conical gynoecial region (cut between orthoslices yz0800 and 1220); c) Flower in transverse section showing the numerous laminar tepals in several series and the stamens cut in the region of the poorly differentiated anthers; note cellular differences between outer (op) and inner (in) perianth parts, as well as and transverse sections of anthers, apparently with laterally to slightly dorsally placed pollen sacs (arrow heads) (cut at orthoslice xy0770); d) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0820); e) Flower in transverse section showing the numerous laminar tepals in several series, flattened rhomboidal stamen bases in several series, and poorly differentiated carpels (cut at orthoslice xy0920); f) Flower in longitudinal section showing the shallowly concave floral receptacle with laminar tepals, stamens, and a central conical gynoecial region bearing poorly differentiated carpels (cut at orthoslice yz0900); g) Flower in longitudinal section perpendicular to that in (f) showing stamens and poorly differentiated carpels (cut at orthoslice xz1630). Scale bars = 1 mm (a, b), 500 µm (c–g).
Text-fig. 4. Ratio diagrams of total length of p3–m3. Parasorex depereti from BRS 25 (data from Fanfani 1999), Parasorex depereti (data from Crochet 1986), Parasorex ibericus, type locality Otura-1, Spain (Mein and Martín-Suárez 1993), Parasorex pristinus (Ziegler 2003), Apulogalerix pusillus, Gargano (fissure filling F32), Italy (Masini and Fanfani 2013). On the horizontal axis are reported the element of the series, in ordinates the ratio of the average lengths on the standard Parasorex socialis from La Grive, France (data from Masini and Fanfani 2013). in New Light On Parasorex Depereti (Erinaceomorpha: Erinaceidae: Galericini) From The Late Messinian (Mn 13) Of The Monticino Quarry (Brisighella, Faenza, Italy)
Text-fig. 4. Ratio diagrams of total length of p3–m3. Parasorex depereti from BRS 25 (data from Fanfani 1999), Parasorex depereti (data from Crochet 1986), Parasorex ibericus, type locality Otura-1, Spain (Mein and Martín-Suárez 1993), Parasorex pristinus (Ziegler 2003), Apulogalerix pusillus, Gargano (fissure filling F32), Italy (Masini and Fanfani 2013). On the horizontal axis are reported the element of the series, in ordinates the ratio of the average lengths on the standard Parasorex socialis from La Grive, France (data from Masini and Fanfani 2013).
Figure 14. Atractides spinipes, neotype series. A–E in Water mites of the genus Atractides Koch, 1837 (Acari: Parasitengona: Hygrobatidae) in the western Palaearctic region: a revision
Figure 14. Atractides spinipes, neotype series. A–E, neotype ♂, F–I paraneotype ♀. A, ♂ idiosoma, ventral view; B, ♂, I-L-5 and -6; C, ♂ palp, medial view; D, ♂ palp, lateral view; E, ♂ gnathosoma and chelicera, lateral view; F, ♀, genital field; G, ♀, I-L-5 and -6; H, ♀ palp, medial view I ♀ palp, lateral view. Scale bars = 100 µm.
Series AC Arc Fault Detection Method Based on High-Frequency Coupling Sensor and Convolution Neural Network
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>Test for to produce an arcing fault : Open contact electrodes and Carbonized path wires</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p>
Daily streamflow time series in the Tirgua River Basin (Paso Viboral), Venezuela
<p>Daily streamflow time series derived from the raw data provided by the National Institute of Meteorology and Hydrology (INAMEH) for the period from January 1963 to December 1996 (34 years). Site: Paso Viboral, San Carlos, Cojedes [-68,605964° W; 9,719648° N]. Data frame with year, month, day and flow in m<sup>3</sup>/s. </p>
The dataset for the submitted paper " Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".
<p>These files are the data of the result in the submitted paper, titled "Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".</p> <ul> <li>File Description</li> </ul> <p>pv13.data : Exp. 1<br> pv17.data : Exp. 2</p> <p>The raw potential vorticity (PV) data for the Exp.1 and Exp.2, respectively, used in drawing the Fig.1, 2, and the supplemental movie 1 and 2.<br> These are the grid point value files, 72 levels for the zonal direction, 30 levels for meridional direction. More details are described in the next ctl files.</p> <p> </p> <p>pv13.ctl<br> pv17.ctl</p> <p>Description files for pv13.data and pv17.data. This will be called from grads_pv13.gs and grads_pv17.data, respectively.</p> <p>grads_pv13.gs<br> grads_pv17.gs</p> <p>GrADS script for mapping the PV.</p> <p> </p> <p>energy17.txt : Exp.2</p> <p>The time series table of energy values for exp.2.<br> One raw is identified by combination of the TIME in the experiment and zonal wave number N.</p>
Sentinel-2 Time Series for Pheno-VAE
<p>This repository contains a SQLite file with Sentinel-2 B4 (red) and B8 (near infra-red) bands time series, used to compute NDVI time series for pheno-VAE : https://gitlab.cesbio.omp.eu/zerahy/pheno-VAE</p> <p> </p>
Multilingualism (TRIPLE Video Tutorial Series)
<p>In this tutorial series, researcher Agnieszka Szulińska and Research Infrastructure Coordinator Edward Gray discuss gotriple.eu. This episode explores the opportunities GoTriple offers for multilingual research. </p> <p>Filmed and edited by Fabian Fess.</p>
Open Chat Series: How-to-Advocacy "Why advocate for open scholarly communication in the social sciences and humanities?"
<p>On the 21.10.2022 Alíz Horváth, researcher, expert in East Asian studies, creator of “Humanista” podcast - and member of OPERAS's Special Interest Group "Advocacy" moderated an Open Chat with our guest speaker Per Pippin Aspaas, Head of Library Research and Publishing Support at the UiT in Tromsø, discussing the question "Why advocate for scholarly communication in the social sciences and humanities?"</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.