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3,121 results for “Resources”
Wisconsin Forest County Potawatomi Communit Water Resources Program lake chemistry 2003-2010
Chemical water quality data that has been collected by the FCPC Water Resources Program since 2003. Monthly baseline monitoring was completed in September 2010. Quarterly monitoring has been completed since then. Parameters measured include Alkalinity, Chloride, Chlorophyll a, Pheophyton, Nitrate/Nitrite, Ammonia, TKN, Dissolved organic carbon, Ortho Phosphorus, Total phosphorus, Total suspended solids, Sulfates, Secchi, Turbidity.
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
<p>This dataset consists of 1,181,993 Tweet Ids, obtained as a result from the paper - A large-scale Twitter dataset for drug safety applications mined from publicly existing resources.</p> <p>The Tweet Ids can be hydrated using <strong>twarc</strong>, a command line tool and Python library for archiving Twitter JSON data. Please follow these instructions to install twarc - <a href="https://github.com/DocNow/twarc">https://github.com/DocNow/twarc</a></p> <p>It will take less than 4 hours to hydrate the tweet ids using twarc. </p> <p>If you intend to use the dataset, please cite the paper. </p>
Carbon fibres from renewable resources: the role of the lignin molecular structure in its blendability with biobased poly(ethylene terephthalate)
<p>Biobased poly(ethylene terephthalate) has been successfully blended with isopropyl alcohol fractioned hardwood organosolv lignin. The blend compatibility was analysed using Gibbs free energy calculations and confirmed by glass transition temperature measurements as well as morphological studies. The carbon fibres obtained from this blend displayed a turbostratic carbon phase and their morphology exhibited a one phase smooth surface. The carbon yield of the blend was found to be improved by fractionation, reaching values of ∼40%. The chemical structure of lignin, most notably the amount of available aromatic hydroxyl groups, was critical for the success of this work. The high molecular weight fraction is enriched with aromatic hydroxyl groups that can crosslink as ether type bonds such as β-O-4 and β-5 bonds. Upon aliphatic hydroxyl substitution in a modified lignin, the blend with BPET was found to be incompatible and produced a carbon fibre exhibiting two phases, low carbon yield and a low amount of the turbostratic phase.</p>
Fig. 1 in Intraspecific food resource partitioning in Brazilian silverside Atherinella brasiliensis (Atheriniformes: Atherinopsidae) in a tropical estuary, Brazil
Fig. 1. Study area. Map of the study area with locations of the sampling sites in the Mamanguape River Estuary, Brazil: Mud flat; Tidal Creek 1 and Tidal Creek 2.
Fig. 4 in Intraspecific food resource partitioning in Brazilian silverside Atherinella brasiliensis (Atheriniformes: Atherinopsidae) in a tropical estuary, Brazil
Fig. 4. Volumetric Percentage (%V) per habitat (a) of the most important food items in the diet each size classes (TL1= small juveniles; TL2= juveniles and TL3= adults) of the Mamanguape river estuary, northeastern Brazil. (b: Mudflat, c: Tidal Creek 1 and d: Tidal Creek 2).
Fig. 3 in Intraspecific food resource partitioning in Brazilian silverside Atherinella brasiliensis (Atheriniformes: Atherinopsidae) in a tropical estuary, Brazil
Fig. 3. Size distributions of Atherinella brasiliensis at sampling areas (Mudflat, Tidal Creek 1 and Tidal Creek 2) in de Mamanguape estuary river. Number of individuals (n) is listed. Age Class: White bar= small juveniles; Light Gray bar= juveniles, and Dark Gray bar= adults.
Fig. 6 in Intraspecific food resource partitioning in Brazilian silverside Atherinella brasiliensis (Atheriniformes: Atherinopsidae) in a tropical estuary, Brazil
Fig. 6. Ordination diagram from the first two principal components for the functional traits of Atherinella brasiliensis in Mamanguape river estuary. Symbols size classes: Small juveniles (Open Black Square); Juveniles (Gray Circle) and Adults (Black Circle).
Fig. 2 in A new and improved electric fish finder with resources for printed circuit board fabrication
Fig. 2. Electric fish finder printed circuit board layout in Autodesk Eagle format. For abbreviations see Fig. 1 legend. This layout matches the circuit schematic in Fig. 1.
Fig. 1 in A new and improved electric fish finder with resources for printed circuit board fabrication
Fig. 1. Electric fish finder circuit schematic in Autodesk Eagle format. BATT = battery, C = capacitor, CHGRND = chassis ground, GND = ground, JP = connector socket, R = resistor, SPK = speaker, VCC = voltage common collector. Integrated circuits are: INA = instrumentation amplifier INA128PA; NEB = operational amplifier NE5532; LM386 = audio amplifier LM386N-1; TLE = virtual ground TLE2425IP. This schematic matches the printed circuit board layout in Fig. 2.
Fig. 2 in Food resource partitioning among species of Astyanax (Characiformes: Characidae) in the Lower Iguaçu River and tributaries, Brazil
Fig. 2. Volumetric percentage of the origin of food items in the diet of Astyanax species in the Low Iguaçu River and tributaries in Brazil.
Fig. 3 in Food resource partitioning among species of Astyanax (Characiformes: Characidae) in the Lower Iguaçu River and tributaries, Brazil
Fig. 3. Variation in diet breadth based on 40 food items consumed by five Astyanax species in the Low Iguaçu River and tributaries in Brazil. Diet breadth was evaluated by dispersion of the diet of species in the multivariate space by using PERMDISP (large spatial distance from the centroid indicates wide dispersion, i.e., wide trophic breadth). Lower and upper bars of the box represent 25 and 75 quartiles, respectively. The horizontal bar within each box represents the mean diet breadth.
Fig. 4 in A new and improved electric fish finder with resources for printed circuit board fabrication
Fig. 4. External view of electric fish finder (above, bottom left) and electrode cable/connector assembly (bottom right).
Strategies for Culture Change and Resources for Best Practice in Open Science: Slides https://osf.io/fqypv
<p>Audio recording of a talk given by David Mellor at the Reward/Equator conference organised by the Bf3R seminar series. The audio can be combined with the original slides found here: <a href="https://osf.io/fqypv">https://osf.io/fqypv</a></p> <p>The BfR is the German Centre for the Protection of Laboratory Animals at the German Federal Institute for Risk Assessment. The Bf3R organized the focus track “Improving biomedical research through preregistration” at the REWARD/EQUATOR conference in Berlin, where David Mellor was an invited expert speaker. The aim of this conference is: “sharing strategies for research improvement”.</p>
DSing ASR task: Resources and Baseline for an unaccompanied singing ASR.
<p>DSing ASR task: Resources and Baseline for an unaccompanied singing ASR.</p> <p>In this repository, you will find the scripts used to construct the DSing ASR-oriented dataset and the baseline system constructed on Kaldi.</p> <p>Cite:</p> <pre><code>@inproceedings{Roa_Dabike-Barker_2019, author = {Roa Dabike, Gerardo and Barker, Jon} title = {{Automatic Lyric Transcription from Karaoke Vocal Tracks: Resources and a Baseline System}}, year = 2019, booktitle = {Proceedings of the 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019)} } </code></pre> <p>1- DSing dataset</p> <p>DSing is an ASR-oriented dataset constructed from the <a href="https://ccrma.stanford.edu/damp/">Smule Sing!300x30x2</a> dataset (<strong>Sing!</strong>). This repository provides the scripts to transform <strong>Sing!</strong> to the DSing ASR task.</p> <p>2- Initial steps</p> <p>The first step before running any of the scripts is to obtain access to <strong>Sing!</strong> dataset. For more details, go to <a href="https://ccrma.stanford.edu/damp/">DAMP repository</a>.</p> <p>3- Transform Sing! to DSing dataset</p> <p>The scripts to transform the <strong>Sing!</strong> dataset to DSing ASR task dataset is located in the <strong>[DSing Construction](DSing Construction/)</strong> directory. The process is based on a series of python tools that are summarised in the runme_sing2dsing.sh bash script.</p> <ol> <li>Define the variable <em>version</em> with the name of the DSing version you want to construct (DSing1, DSing3 or DSing30). Any other option will raise an error.</li> <li>Set the variable <em>DSing_dest</em> with the path where the DSing version will be saved.</li> <li>Set the variable <em>SmuleSing_path</em> with the path to your copy of Smule Sing!300x30x2.</li> <li>Run code until step <strong>K</strong></li> <li>.....</li> </ol> <p>4- Extract DSing dataset using pre-segmented data.</p> <p>If you want to do some analysis in the segmentation results or to use DSing for different porpoise than ASR. In directory <strong>[DSing preconstructed](DSing preconstructed)</strong> you can find a small script that allows recovering the transcriptions and utterance wav files. Just need to to set the output directory and the path of your version of <strong>Sing!</strong></p>
YAMP Resources with Human Genome indexed bbmap 38.76
<p>Resource dataset for <strong>YAMP</strong> (https://github.com/alesssia/YAMP) with Human Genome indexed using <strong> bbmap 38.76</strong></p> <p><em>Original dataset:</em></p> <p>Alessia. (2017). Data for YAMP (https://github.com/alesssia/YAMP) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1068229</p> <p><em>From the original dataset :</em></p> <p>This dataset includes all the databases/datasets queried by YAMP (https://github.com/alesssia/YAMP). This dataset has been created to help the users to get started with YAMP, and to save them from the hassle of collecting and downloading the data from different sources.</p> <p>More in details, this dataset contains:</p> <ul> <li>a FASTA file listing the adapter sequences to remove in the trimming step. This file is usually provided within the BBmap installation (https://sourceforge.net/projects/bbmap, version 37.68). </li> <li>two FASTA files describing synthetic contaminants (sequencing_artifacts.fa.gz and phix174_ill.ref.fa.gz). These files are usually provided within the BBmap installation (https://sourceforge.net/projects/bbmap, version 37.68). </li> <li>a FASTA file provided by Brian Bushnell for removing human contamination (described here: http://seqanswers.com/forums/showthread.php?t=42552). Please note that this file should be indexed beforehand. This can be done using BBMap, using the following command: `bbmap.sh -Xmx24G ref=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz`. </li> <li>the BowTie2 database file for MetaPhlAn2. This file is usually provided within the MetaPhlAn2 installation (version 2.6.0)</li> <li>the ChocoPhlAn and UniRef (Uniref50, Uniref90) databases, downloaded directly by HUMAnN2 (version 0.9.9), as explained here: https://bitbucket.org/biobakery/humann2/wiki/Home#markdown-header-5-download-the-databases</li> </ul>
A chromosome-scale genome assembly resource for Myriosclerotinia sulcatula infecting sedge grass (Carex sp.)
<p>The fungus <em>Myriosclerotinia sulcatula</em> is a close relative of the notorious polyphagous plant pathogens <em>Botrytis cinerea</em> and <em>Sclerotinia sclerotiorum</em> but exhibits a host range restricted to plants from the <em>Carex</em>genus (<em>Cyperaceae</em> family). To date, there are no genomic resources available for fungi in the <em>Myriosclerotinia</em>genus. Here, we present a chromosome-scale reference genome assembly for <em>M. sulcatula</em>. The assembly contains 24 contigs with a total length of 43.53 Mbp, with scaffold N<sub>50</sub> of 2,649.7 kbp and N<sub>90</sub> of 1,133.1 kbp. BRAKER-predicted gene models were manually curated using WebApollo, resulting in 11,275 protein-coding genes that we functionally annotated. We provide a high-quality reference genome assembly and annotation for <em>M. sulcatula</em> as a resource for studying evolution and pathogenicity in fungi from the <em>Sclerotiniaceae</em> family. </p>
Data of paper "Predicting Transverse Mixing Efficiency Downstream of a River Confluence by Pouchoulin et al. (Water Resources Research)"
<p>Here is the dataset of velocity (ADCP) & conductivity (CTD) masured over the three surveys and at each cross-section of the Soane Rhone confluence at Lyon (France), as presented by "Pouchoulin et al. Predicting Transverse Mixing Efficiency Downstream of a River Confluence, Water Ressources Research"</p>
Arbitration of I/O Forwarding Resources
<p>This repository contains the datasets, source-codes, and parsing scripts from our paper.</p>
The political and institutional challenges facing the crisis of water resources and water and sanitation services in Chile (in Spanish)
<p>Talk (in Spanish) with Alex Caldera, University of Guanajuato, Mexico, and Jose Esteban Castro, National Council of Scientific and Technical Research (CONICET), Argentina and Newcastle University, UK. In this issue we feature Hugo Maturana Aguilar, President of the Workers Union of Valparaiso’s Company of Sanitary Works (ESVAL S.A.), and President of the Executive Directorship, National Federation of Workers of Sanitary Works (FENATRAOS), Chile, who talks about the challenges and opportunities facing water resources and water and sanitation services in Chile. The talk also focuses on the crisis triggered in the city of Osorno in July 2019, when the city was left without water supply for over two weeks by failures of the privatized Los Lagos Company of Sanitary Services (ESSAL), owned by the French multinational Suez.</p> <p>This podcast is part of the Voices for Water Series: http://waterlat.org/public-engagement/podcasts/.</p> <p>The podcast is part of the activities taking place within the Cooperation Agreement between the WATERLAT-GOBACIT Network and the Public Services International (PSI) (http://waterlat.org/projects/cooperation-agreement-with-the-psi/ ), and the collaboration with the Confederation of Workers of Water, Sanitation, and Environment Services of the Americas (CONTAGUAS) (http://www.contaguas.org/).</p> <p> </p> <p> </p>
Landscape composition and local floral resources influence foraging behavior but not the size of Bombus impatiens Cresson (Hymenoptera: Apidae) workers
<p>Here, we assessed how the morphology, weight and foraging behavior of individual workers are affected by their surrounding landscape. We hypothesized that colonies established in landscapes showing high cover of intensive crops and low cover of flowering crops, as well as low amounts of local floral resources, would produce smaller workers, which would perform fewer foraging trips and collect pollen loads less constant in species composition. We tested these predictions with 80 colonies of commercially reared <i>Bombus impatiens</i> Cresson placed in 20 landscapes spanning a gradient of agricultural intensification in southern Québec, Canada. We estimated weekly rate at which workers entered and exited colonies and captured eight workers per colony over a period of 14 weeks during the spring and summer of 2016. Captured workers had their wing, thorax, head, tibia, and dry weight measured, as well as their pollen load extracted and identified to the lowest possible taxonomic level. We did not detect any effect of landscape habitat composition on worker morphology or body weight, but found that foraging activity decreased with intensive crops. Moreover, higher diversity of local floral resources led to lower pollen constancy in intensively cultivated landscapes. Finally, we found a negative correlation between the size of workers and the diversity of their pollen load. Our results provide additional evidence that conservation actions regarding pollinators in arable landscapes should be made at the landscape rather than at the farm level.</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.