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3,758 results for “facilitation”
Isometric exercise facilitates attention to salient events in women via the noradrenergic system
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Microbial narrow-escape is facilitated by wall interactions: Simulation Supplementary material
<p>Simulation codes and simulation results for the paper "Microbial narrow-escape is facilitated by wall interactions".</p>
Retrieval practice facilitates memory updating by enhancing and differentiating medial prefrontal cortex representations
Open the record for dataset details and reuse information.
ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics
<p>ExcapeDB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics</p> <p>Supplementary file (full dataset download)</p> <p>- v2 with SMILES errors fixed (19.01.2019)</p>
Accompanying material to the Inventory of opportunities and bottlenecks in policy to facilitate the adoption of soil-improving techniques
<p>Inventory of policies at EU and country level for the inventory and analysis of bottlenecks and opportunities in sectoral and environmental policies to facilitate the adoption of Soil-Improving Cropping Systems (SICS).</p>
Dataset from: "Reward expectation facilitates context learning and attentional guidance in visual search"
<p>Dataset for Bergmann N, Koch D, Schubö A (2019). Reward expectation facilitates context learning and attentional guidance in visual search, <em>Journal of Vision</em>, 19(3). <a href="https://doi.org/10.1167/19.3.10">https://doi.org/10.1167/19.3.10</a></p>
The Updated Investment Facilitation Index
<p>The Investment Facilitation Index (IFI) provides information on the current adoption of investment facilitation measures at country level for 142 World Trade Organisation (WTO) Members. It was developed by the German Institute of Development and Sustainability (IDOS), previously known as the Deutsches Institut für Entwicklungspolitik / German Development Institute (DIE), in cooperation with the WTO. The IFI is a composite index measuring the adoption of investment facilitation measures in 2021 and applying a multiple binary scoring scheme. Departing from an earlier version of the index (<a href="https://doi.org/10.23661/dp23.2021">Berger et al., 2021</a>), it has been conceptually revised and extended regarding its country coverage. It now consists of 101 measures composing six regulatory dimensions and corresponds closely to the main policy areas and developments within current policy debates, including the newly negotiated <a href="https://www.wto.org/english/news_e/news23_e/infac_06jul23_e.htm">Investment Facilitation for Development (IFD) Agreement</a> among the WTO Members.</p> <p>The data set provides the foundation for analysing specific facilitation hurdles in investment frameworks of a large number of economies. The fine grained data of the IFI can be used for investigating economic benefits and challenges of investment facilitation reforms, support the assessment of implementation gaps, as well as prioritisation of technical assistance and capacity development. It can also be used by investors seeking information on a country’s investment regime.</p> <p>For a detailed description of the methodology and coding of the IFI, please have a look at the uploaded data documentation, contained in the file <strong>ifi_documentation.pdf</strong>. It provides information on the conceptual composition of the index, its evolution from the first version, as well as the coding, data generation and validation processes. In the annex, it also features a detailed overview of each measure contained in the index.</p> <p>The file <strong>ifi_codebook.csv</strong> contains the codebook for the 101 investment facilitation measures included in the IFI. The file features six variables (columns):</p> <ul> <li>Measure: The code of a measure under observation;</li> <li>Area: Specification of the policy area a given measure belongs to;</li> <li>Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;</li> <li>Weight: Specification of the individual weight of a measure, the product of the allocated score (0, 1 or 2) and this weight denotes the contribution to the total score of a given measure;</li> <li>Unit: The measurement unit for the answer of a given measure, it can take values "Score", meaning that the answer is directly measured by score from the multiple binary scoring scheme, or specify another measurement unit, e.g. number of documents, days, US Dollars, etc.;</li> <li>Coding_0: Specifies the answer coding which allocates a score of 0 to this measure;</li> <li>Coding_1: Specifies the answer coding which allocates a score of 1 to this measure;</li> <li>Coding_2: Specifies the answer coding which allocates a score of 2 to this measure.</li> </ul> <p>The file <strong>ifi_table.csv</strong> or <strong>ifi_table.xlsx </strong>(please choose your preferred file format) contains all 14484 data points resulting from the 101 measures coded for 142 economies. Moreover, it also contains the total score for each country calculated by applying the expert weighting scheme. The file contains the following variables (columns):</p> <ul> <li>CountryCode: <a href="https://unstats.un.org/unsd/methodology/m49/">ISO-alpha3</a> code of a country for which a given measure is coded;</li> <li>Country: Name of a country for which a given measure is coded;</li> <li>Measure: Code of a measure that is coded in a given row;</li> <li>Area: Specification of the policy area a given measure belongs to;</li> <li>Measure_Description: A short description of what investment facilitation feature is evaluated by a given measure;</li> <li>Answer: The answer coded for a given measure and country;</li> <li>Score: The allocated score based on the answer, according to the definition of the measure (see codebook);</li> <li>Unit: The measurement unit for the answer of a given measure;</li> <li>Coding: The answer option coded for a given measure and country (corresponds to either Coding_0, Coding_1 or Coding_2 in the codebook);</li> <li>Source: Source statement for the provided answer.</li> </ul> <p>For further inquiries please contact the authors.</p>
Data from: Heterogeneity in habitat and nutrient availability facilitate the co-occurrence of N2 fixation and denitrification across wetland - stream - lake ecotones of Lakes Superior and Huron
Great Lakes coastlines are mosaics of wetland, stream, and lake habitats, characterized by a high degree of spatial heterogeneity that may facilitate the co-occurrence of seemingly incompatible biogeochemical processes due to variation in environmental factors that favor each process. We measured nutrient limitation and rates of N2 fixation and denitrification along transects in 5 wetland - stream - lake ecotones with different nutrient loading in Lakes Superior and Huron and hypothesized that rates of both processes would be related to nutrient limitation status, habitat type, and environmental characteristics including temperature, nutrient concentrations, and organic matter quality. This data package includes information on sampling sites, dates and locations; rates of N fixation and denitrification measured at each site, date and transect location; and biomass information from nutrient diffusing substrates deployed on the study transects.
Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion
<p>Raw data of "Patient-derived and artificial ascites have minor effects on MeT-5A mesothelial cells and do not facilitate ovarian cancer cell adhesion".</p>
DELANA - An eyetracking dataset from facilitating a series of laptop-based lessons
<p>This dataset contains eye-tracking data from a two subjects (an expert and a novice teachers), facilitating three collaborative learning lessons (2 for the expert, 1 for the novice) in a classroom with laptops and a projector, with real master-level students. These sessions were recorded during a course on the topic of digital education and learning analytics at [EPFL](http://epfl.ch).</p> <p>This dataset has been used in several scientific works, such as the [CSCL 2015](http://isls.org/cscl2015/) conference paper "The Burden of Facilitating Collaboration: Towards Estimation of Teacher Orchestration Load using Eye-tracking Measures", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg. The analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/cscl2015-eyetracking-orchestration</p>
JDC2014 - An eyetracking dataset from facilitating a semi-authentic multi-tabletop lesson
<p>This dataset contains eye-tracking data from a single subject (a researcher), facilitating three collaborative learning lessons in a multi-tabletop classroom, with real 10-12 year old students. These sessions were recorded during an "open doors day" at the [CHILI Lab](http://chili.epfl.ch).</p> <p>This dataset has been used in several scientific works, such as the [CSCL 2015](http://isls.org/cscl2015/) conference paper "The Burden of Facilitating Collaboration: Towards Estimation of Teacher Orchestration Load using Eye-tracking Measures", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg. The analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/cscl2015-eyetracking-orchestration</p>
JDC2015 - A multimodal dataset from facilitating multi-tabletop lessons in an open-doors day
<p><strong>IMPORTANT NOTE: Two of the files in this dataset are incorrect, see this dataset's errata at https://zenodo.org/record/204063 and https://zenodo.org/record/204819</strong></p> <p>This dataset contains eye-tracking, EEG, accelerometer, indoor location and video coding data from a single subject (a researcher with limited teacher experience), facilitating four maths lessons in a simulated multi-tabletop classroom, with four cohorts of 10-12 year old students, using tangible paper tabletops and a projector. These sessions were recorded in the frame of the MIOCTI project (http://chili.epfl.ch/miocti).</p> <p>This dataset has been used in several scientific works, such a submitted journal paper "Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking", by Luis P. Prieto, Kshitij Sharma, Lukasz Kidzinski & Pierre Dillenbourg (the analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/paper-IEEETLT-orchestrationload)</p>
ISL2015NOVEL (ERRATUM) - An eyetracking dataset from facilitating secondary multi-tabletop classrooms
<p>This dataset is a complement (a correction, actually) to the "ISL2015NOVEL - An eyetracking dataset from facilitating secondary multi-tabletop classrooms" dataset, also published in Zenodo (see https://zenodo.org/record/198681 for further info on the dataset). This erratum contains a CSV file with the manual videocoding of episodes, that substitutes the (erroneous) original one provided there (in the ISL2015NOVEL-CodingData.zip file).</p>
Data from: Saltmarsh vegetation and secured woody debris facilitate mangrove re-colonization
<p>Does the presence of saltmarsh vegetation affect the long-term regeneration of the pioneer mangrove species <em>Avicennia germinans</em> in a degraded dwarf forest? Does immobilized coarse woody debris (CWD) affect regeneration similarly? Do larger trees suppress or facilitate intraspecific saplings? The study was conducted in a dwarf mangrove forest in the high intertidal zone on Bragança peninsula in northern Brazil. The spatial patterns of <em>A. germinans</em>, the herbaceous halophyte <em>Sesuvium portulacastrum</em>, and CWD were mapped in three sample plots (each 400 m<sup>2</sup>) during two consecutive vegetation surveys, conducted in 2011 and 2014. Inhomogeneous Poisson and Thomas point-process models were used to assess the distribution of <em>A. germinans</em> life-history stages (seedlings, saplings, and adult dwarf trees), conditioned on the presence of <em>S. portulacastrum</em> and CWD. In addition, intraspecific interactions between trees and regeneration were assessed based on crown projection mapping. Bivariate point pattern analyses were used to assess the dependence of advance regeneration on dwarf <em>A. germinans</em> trees and <em>S. portulacastrum</em>. <em>A. germinans</em> saplings and trees were positively associated with <em>S. portulacastrum</em> and CWD, whereas seedlings were located around tree crowns. The density of fruit-bearing trees was positively associated with sapling density, indicating that regeneration relied on locally dispersed propagules. Herbaceous vegetation and CWD have an important ecological function in degraded mangroves by retaining tidally dispersed propagules. Here, we show that herbaceous vegetation does not suppress the growth of seedlings but facilitates mangrove recolonization. Due to limited tidal dispersal, regeneration relies on local propagule supply. In addition to hydrological restoration, the observed vegetation patterns suggest that, in the absence of propagule-retaining vegetation, restoration of high-intertidal mangroves can be facilitated by establishing nuclei of planted trees and installing secured logs.</p>
WaveFake: A data set to facilitate audio DeepFake detection
<p>The main purpose of this data set is to facilitate research into audio DeepFakes. We hope that this work helps in finding new detection methods to prevent such attempts. These generated media files have been increasingly used to commit <a href="https://www.vice.com/en/article/pkyqvb/deepfake-audio-impersonating-ceo-fraud-attempt">impersonation attempts</a> or <a href="https://www.wired.com/story/telegram-still-hasnt-removed-an-ai-bot-thats-abusing-women/">online harassment</a>. You can find the accompanying code repository on <a href="https://github.com/RUB-SysSec/WaveFake">GitHub</a>.</p> <p>The data set consists of 104,885 generated audio clips (16-bit PCM wav). We examine multiple networks trained on two reference data sets. First, the <a href="https://keithito.com/LJ-Speech-Dataset/">LJSpeech</a> data set consisting of 13,100 short audio clips (on average 6 seconds each; roughly 24 hours total) read by a female speaker. It features passages from 7 non-fiction books and the audio was recorded on a MacBook Pro microphone. Second, we include samples based on the <a href="https://sites.google.com/site/shinnosuketakamichi/publication/jsut">JSUT</a> data set, specifically, basic5000 corpus. This corpus consists of 5,000 sentences covering all basic kanji of the Japanese language (4.8 seconds on average; roughly 6.7 hours total). The recordings were performed by a female native Japanese speaker in an anechoic room. Finally, we include samples from a full text-to-speech pipeline (16,283 phrases; 3.8s on average; roughly 17.5 hours total). Thus, our data set consists of approximately 175 hours of generated audio files in total. Note that we do not redistribute the reference data.</p> <p>We included a range of architectures in our data set:</p> <ul> <li><a href="https://arxiv.org/abs/1910.06711">MelGAN</a></li> <li><a href="https://arxiv.org/abs/1910.11480">Parallel WaveGAN</a></li> <li><a href="https://arxiv.org/abs/2005.05106">Multi-Band MelGAN</a></li> <li><a href="http://arxiv.org/abs/2005.05106">Full-Band MelGAN</a></li> <li><a href="https://arxiv.org/abs/2010.05646">HiFi-GAN</a></li> <li><a href="https://arxiv.org/abs/1811.00002">WaveGlow</a></li> </ul> <p>Additionally, we examined a bigger version of MelGAN and include samples from a full TTS-pipeline consisting of a conformer and parallel WaveGAN model.</p> <p><strong>Collection Process</strong></p> <p>For WaveGlow, we utilize the <a href="https://github.com/NVIDIA/waveglow">official implementation</a> (commit 8afb643) in conjunction with the official pre-trained network on <a href="https://pytorch.org/hub/nvidia_deeplearningexamples_waveglow/">PyTorch Hub</a>. We use a popular implementation available on <a href="https://github.com/kan-bayashi/ParallelWaveGAN">GitHub</a> (commit 12c677e) for the remaining networks. The repository also offers pre-trained models. We used the pre-trained networks to generate samples that are similar to their respective training distributions, <a href="https://keithito.com/LJ-Speech-Dataset/">LJ Speech</a> and <a href="https://sites.google.com/site/shinnosuketakamichi/publication/jsut">JSUT</a>. When sampling the data set, we first extract Mel spectrograms from the original audio files, using the pre-processing scripts of the corresponding repositories. We then feed these Mel spectrograms to the respective models to obtain the data set. For sampling the full TTS results, we use the <a href="https://github.com/espnet/espnet">ESPnet</a> project. To make sure the generated phrases do not overlap with the training set, we downloaded the <a href="https://commonvoice.mozilla.org/en/datasets">common voices data set</a> and extracted 16.285 phrases from it.</p> <p>This data set is licensed with a CC-BY-SA 4.0 license.</p> <p>This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- EXC-2092 CaSa -- 390781972.</p>
Mixed methods systematic review and metasummary about barriers and facilitators for the implementation of cotrimoxazole and isoniazid - preventive therapies for people living with HIV.
<p>This is the minimal data set underlying the findings of our systematic review and metasummary:</p> <p>We uploaded the following data extracted from the studies included in our review:</p> <p>- Systematic Review protocol, also published in PROSPERO (CRD42019137778).</p> <p>- detailed description of studies included in our review.</p> <p>- barriers identified in the review (metasummary).</p> <p>- facilitators identified in the review.</p>
Major facilitator superfamily domain-containing protein 10 (MFSD10) A Target Enabling Package (TEP)
<p>MFSD10 (also known as TETRAN in humans) has been proposed to function as an organic anion efflux pump and as a transporter for some NSAIDs. We have produced milligram quantities of purified recombinant protein and solved its structure in an outward-facing state at 2.6 Å resolution by X-ray crystallography. The structure - the first example for a human atypical SLC - provides the initial clues to understanding the broad specificity of its putative substrate-binding site.</p>
A data directory to facilitate investigations on worldwide wildlife trafficking
<p>We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer-reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data directory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data-driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network interdiction problems.</p>
Molecular dynamics trajectories for "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations
<p>The molecular dynamics-generated ensemble dataset for cyclo-(cGHHQKLV), used in the manuscript "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations". The dataset consists of 14 + 6 =20 .dcd files, and one .pdb file for rendering.</p>
Does land-use history facilitate non-native plant invasion? A field experiment with Celastrus orbiculatus in the Bent Creek Experimental Forest in the southern Appalachians from 2008 to 2009
Although historic land use is often implicated in non-native plant invasion of forests, little is known about how land-use legacies might actually facilitate invasion. The researchers conducted a 2-year field seeding experiment in western North Carolina, USA, to compare germination and first-year seedling survival of Celastrus orbiculatus Thunb. in stands that had been cultivated and abandoned a century earlier and were dominated by tulip poplar (Liriodendron tulipifera L.), and in paired stands that had never been cultivated and were dominated by oaks (Quercus spp.). Experiments were conducted at five sites with paired tulip poplar and oak stands by varying litter mass (none, low, or high) and litter type (tulip poplar or oak).
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.