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24 results for “Link quality”
Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network
<p>This repository contains the long-term connectivity and link quality dataset collected on <a href="https://chirpbox.github.io/">ChirpBox</a> over 4 months (May -- September 2021) in the city of Shanghai, China. </p> <p>In addition to the dataset itself, we provide evaluation scripts for data analysis and visualization, in order to facilitate data exploration and re-use. To make it clear how to use the scripts, we provide a <em>Jupyter notebook -- </em> <strong>dataset.ipynb</strong> for dataset visualization.</p> <p><strong>List of files:</strong></p> <ol> <li><em>dataset_03052021_15092021.csv</em> <ul> <li>The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.</li> </ul> </li> <li><em>data_analysis.py</em> <ul> <li>The script for dataset analysis and visualization. One can use the functions in this script to derive network-level statistics (e.g., in terms of average number of correctly-exchanged packets), link-level statistics (e.g., in terms of SNR, RSS, and PRR), and node-level statistics(e.g., in terms of number of neighbours and temperature evolution over time).</li> </ul> </li> <li><em>metadata_processing.py</em> <ul> <li>The script for pre-processing metadata into CSV files. One can use the functions in this script to convert metadata for each measurement saved in TXT and JSON formats to CSV files that include attributes such as link quality, connectivity, and environmental information, an example of which is <strong>dataset_03052021_15092021.csv</strong>.</li> </ul> </li> <li><em>dataset.ipynb </em> <ul> <li>The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in <strong>data_analysis.py</strong> and <strong>metadata_processing.py</strong>.</li> </ul> </li> <li><em>topology_map.png</em> <ul> <li>The node deployment map used to create topology figures. A usage example is <strong>Figure 1</strong> shown in the notebook <strong>dataset.ipynb</strong>.</li> </ul> </li> <li><em>dataset_metadata.zip</em> <ul> <li>The dataset metadata is stored in TXT and JSON formats. Among them, link quality, connectivity and on-board sensor data are stored in TXT files and weather information are stored in JOSN files.</li> </ul> </li> <li><em>README.md</em> <ul> <li>The README.md explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.</li> </ul> </li> </ol>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
Wireless Link Quality Estimation on FlockLab - and Beyond
<p>This repository contains wireless link quality estimation data for the FlockLab testbed [1,2]. The rationale and description of this dataset is described in a the following abstract (pdf is included in this repository -- see below).</p> <blockquote> <p><strong>Dataset: Wireless Link Quality Estimationon FlockLab – and Beyond</strong><br> Romain Jacob, Reto Da Forno, Roman Trüb, Andreas Biri, Lothar Thiele<br> DATA '19 Proceedings of the 2nd Workshop on Data Acquisition To Analysis, 2019</p> </blockquote> <p><strong>Data collection scenario</strong></p> <p>The data collection scenario is simple. Each FlockLab node is assigned one dedicated time slot. In this slot, a node sends 100 packets, called strobes. All strobes have the same payload size and use a given radio frequency channel and transmit power. All other nodes listen for the strobes and log packet reception events (i.e., success or failed). </p> <p>The test scenario is ran every two hours on two different platforms: the TelosB [3] and DPP-cc430 [4] platforms. We used all nodes currently available at test time (between 27 and 29).</p> <p><strong>Final dataset status</strong></p> <ul> <li>3 months of data with about 12 tests per day per platform</li> <li>5 month of data with about 4 tests per day per platform</li> </ul> <p><strong>Data collection firmware</strong></p> <p>We are happy to share the link quality data we collected for the FlockLab testbed, but we also wanted to make it easier for others to collect similar datasets for other wireless networks. To achieve this, we include in this repository the data collection firmware we design. The entire data collection scheduling and control is done entirely in software, in order to make the firmware usable in a large variety on wireless networks. We implemented our data collection software using Baloo [5], a flexible network stack design framework based on Synchronous Transmission. Baloo efficiently handles network time synchronization and offers a flexible interface to schedule communication rounds. The firmware source code is available in the Baloo repository [6].</p> <p>A set of experiment parameters can be patched directly in the firmware, which let the user tune the data collection without having to recompile the source code. This improves usability and facilitates automation. An example patching script is included in this repository. Currently, the following parameters can be patched:</p> <ul> <li>rf_channel,</li> <li>payload,</li> <li>host_id, and</li> <li>rand_seed</li> </ul> <p><em>Current supported platforms</em></p> <ul> <li>TelosB [3]</li> <li>DPP-cc430 [4]</li> </ul> <p><strong>Repository versions</strong></p> <ul> <li><strong>v1.4.1</strong><br> Updated visualizations in the notebook</li> <li><strong>v1.4.0</strong><br> Addition of data from November 2019 to March 2020.<br> Data collection is discontinued (the new FlockLab testbed is being setup).</li> <li><strong>v1.3.1</strong><br> Update abstract and notebook</li> <li><strong>v1.3.0</strong><br> Addition of October 2019 data.<br> The frequency of tests has been reduced to 4 per day, executing at (approximately) 1:00, 7:00, 13:00, and 19:00.<br> From October 28 onward, time shifted by one hour (2:00, 8:00, 14:00, 20:00).</li> <li><strong>v1.2.0</strong><br> Addition of September 2019 data.<br> Many missing tests on the 12, 13, 19, and 20 of September (due to construction works in the building).</li> <li><strong>v1.1.4</strong><br> Update of the abstract to have hyperlinks to the plots. Corrected typos.</li> </ul> <ul> <li><strong>v1.1.0</strong><br> Initial version.<br> Add the data collected in August 2019.<br> Data collected was disturbed at the beginning of the month and resumed normally on the August 13. Data from previous days are incomplete.</li> <li><strong>v1.0.0</strong><br> Initial version.<br> Contain collected data in July 2019, from the 10th to 30th of July.<br> No data were collected on the 31st of July (technical issue).</li> </ul> <p><strong>List of files</strong></p> <ul> <li><strong>yyyy-mm_raw_platform.zip</strong><br> Archive containing all FlockLab test result files (one .zip file per month and per platform).</li> <li><strong>yyyy-mm_preprocessed_all.zip</strong><br> Archive containing preprocessed csv files, one per month and per platform.</li> <li><strong>firmware.zip</strong><br> Archive containing the firmware for all supported platform.</li> <li><strong>firmware_patch.sh</strong><br> Example bash script illustrating the firmware patching.</li> <li><strong>parse_flocklab_results.ipynb </strong>[<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3731498/files/parse_flocklab_results.ipynb">open in nbviewer</a>]<br> Jupyter notebook used to create the pre-process data files. Also includes some example of data visualization.</li> <li><strong>parse_flocklab_results.html</strong><br> HTML rendering of the notebook (static).</li> <li><strong>plots.zip</strong><br> Archive containing high resolution visualization of the dataset, generated by the <strong>parse_flocklab_results </strong>notebook, and presented in the <strong>abstract</strong>.</li> <li><strong>abstract.pdf</strong><br> A 3 page abstract presenting the dataset.</li> <li><strong>CRediT.pdf</strong><br> The list of contributions from the authors.</li> </ul> <p><strong>References</strong></p> <p>[1] R. Lim, F. Ferrari, M. Zimmerling, C. Walser, P. Sommer, and J. Beutel, “FlockLab: A Testbed for Distributed, Synchronized Tracing and Profiling of Wireless Embedded Systems,” in <em>Proceedings of the 12th International Conference on Information Processing in Sensor Networks</em>, New York, NY, USA, 2013, pp. 153–166.</p> <p>[2] “FlockLab,” <em>GitLab</em>. [Online]. Available: <a href="https://gitlab.ethz.ch/tec/public/flocklab/wikis/home">https://gitlab.ethz.ch/tec/public/flocklab/wikis/home</a>. [Accessed: 24-Jul-2019].</p> <p>[3] Advanticsys, “MTM-CM5000-MSP 802.15.4 TelosB mote Module.” [Online]. Available: <a href="https://www.advanticsys.com/shop/mtmcm5000msp-p-14.html">https://www.advanticsys.com/shop/mtmcm5000msp-p-14.html</a>. [Accessed: 21-Sep-2018].</p> <p>[4] Texas Instruments, “CC430F6137 16-Bit Ultra-Low-Power MCU.” [Online]. Available: <a href="http://www.ti.com/product/CC430F6137">http://www.ti.com/product/CC430F6137</a>. [Accessed: 21-Sep-2018].</p> <p>[5] R. Jacob, J. Bächli, R. Da Forno, and L. Thiele, “Synchronous Transmissions Made Easy: Design Your Network Stack with Baloo,” in <em>Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks</em>, 2019.</p> <p>[6] “Baloo,” Dec-2018. [Online]. Available: <a href="http://www.romainjacob.net/research/baloo/">http://www.romainjacob.net/research/baloo/</a>.</p> <p> </p>
Linked collectors and determiners for: Freshwater samples in MZNA-INV-FRW: Macroinvertebrate samples from the water quality monitoring network along the Ebro Basin.
Natural history specimen data linked to collectors and determiners held within, "Freshwater samples in MZNA-INV-FRW: Macroinvertebrate samples from the water quality monitoring network along the Ebro Basin". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9">https://bionomia.net/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9">https://gbif.org/dataset/dfddad59-5bc5-4e35-8b35-334eed43bba9</a>. Formatted as a Frictionless Data package.
Female and male plumage colour is linked to parental quality, pairing and extra-pair mating in a tropical passerine
<p>Sexual selection has been proposed to drive the evolution of elaborate phenotypic traits in males, which often confer success in competition or mating. However, in many species both males and females display such traits, although studies investigating selection acting in both sexes are scarce. In this study, we investigated whether plumage ornamentation is sexually selected in female and male lovely fairy-wrens <em>Malurus amabilis</em>, a cooperatively breeding songbird. We found that female and male plumage colour was correlated with parental quality but not with individual quality and survival. We also found evidence of positive assortative mating based on plumage colour. Microsatellite analyses of paternity indicated that the lovely fairy-wren has high levels of extra-pair paternity, with 53% of offspring (in 58% of broods, of 57% of females) resulting from extra-pair mating. Female and male plumage colour did not predict reproductive success or the proportion of extra-pair offspring in their own nest, but less colourful males obtained higher extra-pair paternity when paired with more colourful females, and gained overall higher total paternity (own nest and other nests). We argue that plumage colour may be under sex-specific selection, highlighting the importance of looking at both sexes in studies of sexual selection and ornament evolution. The current findings together with previous study, suggest that plumage colour in female and male lovely fairy-wrens appears to be an honest signal relevant in both intra and inter-sexual competition contexts.</p>
Linked Data Quality Assessment for Datasets on the LOD Cloud
<p><strong>For more up to date quality metadata, please visit https://w3id.org/lodquator</strong></p> <p>This dataset is a collection of TRiG files with quality metadata for different datasets on the LOD cloud. Each dataset was assessed for</p> <ol> <li>The length of URIs</li> <li>Usage of RDF primitives</li> <li>Re-use of existing terms</li> <li>Usage of undefined terms</li> <li>Usage of blank nodes</li> <li>Indication for different serialisation formats</li> <li>Usage of multiple languages</li> </ol> <p>This data dump is part of the empirical study conducted for the paper "Are LOD Cloud Datasets Well Represented? A Data Representation Quality Survey."</p> <p>For more information visit http://jerdeb.github.io/lodqa</p>
Linking 23 physical activity intensity levels to health-related quality of life in 10-year-old children
<p>This anonymized data is a subset of the ASK study: <br>Resaland, G.K., Moe, V.F., Aadland, E., et al. Active Smarter Kids (ASK): Rationale and design of a cluster-randomized controlled trial investigating the effects of daily physical activity on children’s academic performance and risk factors for non-communicable diseases.BMC Public Health 15, 709 (2015). https://doi.org/10.1186/s12889-015-2049-y</p> <p>The ASK study was approved by the Regional Committees for Medical and Health Research Ethics in Norway (reference number: 2013/1893) and registered at www.Clinicaltrials.gov with ID NCT02132494. </p> <p>The selected cross-sectional baseline data in this file are used in the following unpublished paper: <br>Stai, M., Aadland, E., Andersen, J.R. (Year). *Linking 23 physical activity intensity levels to health-related quality of life in 10-year-old children.</p> <p>The data is stored in Excel. <br><br>Explanations: <br>- #NULL! = missing data<br>- Sex (0 = Girl, 1= Boy) <br>- Overweight or obesity (0 = No, 1 = Yes)<br>- All other data has labeling that is self-explanatory. </p> <p>Corresponding Author: <br>John Roger Andersen, johnra@hvl.no </p> <p> </p>
Linking leaf economic traits with forage quality across temperate grasslands under ambient and drought conditions
Open the record for dataset details and reuse information.
Female and male plumage colour is linked to parental quality, pairing and extra-pair mating in a tropical passerine
Open the record for dataset details and reuse information.
Relating Wikipedia Article Quality to Edit Behavior and Link Structure
<p>This dataset was analyzed and produced during the study described in the paper "Relating Wikipedia Article Quality to Edit Behavior and Link Structure" (under review, doi and link follows - see references). Its creation process and use cases are described in the dedicated paper.</p> <p>For directions and code to process and evaluate this data, please see the corresponding GitHub repository: https://github.com/ruptho/editlinkquality-wikipedia.</p> <p>We provide three files for 4941 Wikipedia articles (in .pkl format):<br> The "article_revisions_labeled.pkl" file provides the final, semantically labeled revisions for each analyzed article per quality category. The "article_revision_features.zip" file contains processed per-article features, divided into folders for the specific quality categories they belong to. In "article_revision_features_raw.zip", we provide the raw features as retrieved via RevScoring API (https://pythonhosted.org/revscoring/).</p>
Data from: Noninvasive physiological markers demonstrate link between habitat quality, adult sex ratio and poor population growth rate in a vulnerable species, the Cape mountain zebra
Effective conservation and species management requires an understanding of the causes of poor population growth. Conservation physiology uses biomarkers to identify factors that contribute to low individual fitness and population declines. Building on this, macrophysiology can use the same markers to assess how individual physiology varies with different ecological or demographic factors over large temporal and spatial scales. Here, we use a macrophysiological approach to identify the ecological and demographic correlates of poor population growth rates in the Cape mountain zebra metapopulation. We use two non-invasive biomarkers: faecal glucocorticoids as a measure of chronic stress, and faecal androgens as an indicator of male physiological status. We found that faecal glucocorticoid concentrations were highest in the spring prior to summer rainfall, and were elevated in individuals from populations associated with low quality habitat (lower grass abundance). In addition, faecal androgen concentrations were higher in populations with a high proportion of non-breeding stallions (where male:female adult sex ratios exceed 2:1) suggesting sex ratio imbalances may intensify male competition. Finally, population growth rate was negatively associated with faecal glucocorticoid concentrations and female fecundity was negatively associated with faecal androgens, indicating a relationship between hormone profiles and fitness. Together, our results provide cross population evidence for how poor population growth rates in Cape mountain zebra can be linked to individual physiological biomarkers. More broadly, we advocate physiological biomarkers as indicators of population viability, and as a way to evaluate the impact of variable ecological and demographic factors. In addition, conservation physiology can be used to assess the efficacy of management interventions for this subspecies, and this approach could inform models of species' responses to future environmental change.
ALFAsleep: Exploring Cognitive and Biological Correlates of Sleep Quality and Their Potential Links With Alzheimer's Disease
ClinicalTrials.gov study NCT04932473. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Links Between Depression, Anxiety, Coping and Quality of Life After a Stroke
ClinicalTrials.gov study NCT02778334. IPD Sharing: Not stated. Countries: 1. Publications: 1.
SMART-ALD - A New Lifestyle Intervention to Improve Quality of Life in Women With X-linked Adrenoleukodystrophy (X-ALD)
ClinicalTrials.gov study NCT04687007. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Impact of Changes in the Use of Care and Reorganization of the Healthcare System Linked to the Covid-19 Outbreak on the Quality of Care Pathways of Patients Suffering From Acute Myocardial Infarction
ClinicalTrials.gov study NCT04979208. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Noninvasive physiological markers demonstrate link between habitat quality, adult sex ratio and poor population growth rate in a vulnerable species, the Cape mountain zebra
Open the record for dataset details and reuse information.
Online machine learning algorithms to predict link quality in community wireless mesh networks
<p>FunkFeuer raw topology data from 15-01-2016 to 28-01-2016 retrieved on 17-02-2016 from http://opendata.confine-project.eu/dataset/funkfeuer-topology-data. This dataset was employed in "Miguel L. Bote-Lorenzo, Eduardo Gómez-Sánchez, Carlos Mediavilla-Pastor, Juan I. Asensio-Pérez, Online machine learning algorithms to predict link quality in community wireless mesh networks, Computer Networks, Volume 132, 2018, Pages 68-80, https://doi.org/10.1016/j.comnet.2018.01.005."</p>
Impact of Keratoconus, Cross-linking and Cross-linking Combined With Topo-guided Photorefractive Keratectomy on Self-reported Quality of Life. A Three-year Update.
ClinicalTrials.gov study NCT01845714. IPD Sharing: Not stated. Countries: 0. Publications: 1.
The sperm quality in DIO male mice is linked to the NF-κB signaling and Ppp2ca expression in the hypothalamus
GEO Series GSE284408. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Pathways Linking Reduced Sleep Duration and Quality to Obesity Risk
ClinicalTrials.gov study NCT00915707. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.