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12,662 results for “Linking”
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>
The datasets for "Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data" paper
<p>Paper title: Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data</p> <p>Conference: ICSME 2021</p>
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>
Dataset for Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries"</strong></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements as cyclic voltammetry using scanning electrochemical cell microscopy for three different Si crystallographic orientations (100, 110, 311) in 1 M LiPF6 in ethylene carbonate - ethyl methyl carbonate ("SECCM/")</li> <li>Scanning electron microscopy and transmission electron microscopy imaging of pristine and cycled samples ("Images/")</li> </ul>
Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".
<p>Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.</p>
Linked Open Data Management Services: A Comparison
<p>Thanks to a variety of software services, it has never been easier to produce, manage and publish Linked Open Data. But until now, there has been a lack of an accessible overview to help researchers make the right choice for their use case. This dataset release will be regularly updated to reflect the latest data published in a comparison table developed in Google Sheets [1]. The comparison table includes the most commonly used LOD management software tools from NFDI4Culture to illustrate what functionalities and features a service should offer for the long-term management of FAIR research data, including:</p> <ul> <li>ConedaKOR</li> <li>LinkedDataHub</li> <li>Metaphacts</li> <li>Omeka S</li> <li>ResearchSpace</li> <li>Vitro</li> <li>Wikibase</li> <li>WissKI</li> </ul> <p>The table presents two views based on a comparison system of categories developed iteratively during workshops with expert users and developers from the respective tool communities. First, a short overview with field values coming from controlled vocabularies and multiple-choice options; and a second sheet allowing for more descriptive free text additions. The table and corresponding dataset releases for each view mode are designed to provide a well-founded basis for evaluation when deciding on a LOD management service. The Google Sheet table will remain open to collaboration and community contribution, as well as updates with new data and potentially new tools, whereas the datasets released here are meant to provide stable reference points with version control.</p> <p>The research for the comparison table was first presented as a paper at DHd2023, Open Humanities – Open Culture,<strong> </strong>13-17.03.2023, Trier and Luxembourg [2].</p> <p>[1] Non-editing access is available here: <a href="http://docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link">docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link</a> To get editing access contact the authors.</p> <p>[2] Full paper will be made available open access in the conference proceedings.</p>
Venkataraman et al. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought
<p>VERSION 1: These supplementary files accompany the manuscript by Venkataraman et al. entitled "Rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought." This includes all raw data in the paper, supplementary data, and instructions for the blood puck feeder.</p> <p>VERSION 2: Supplemental Data Files 16-20 were added on 12/19/2022 to accompany a revision of the original bioRxiv pre-print after peer-review at eLife.</p> <p>VERSION 3: New versions of all files were added on 3/21/2023 to accompany the version of record published in eLife:</p> <p>Krithika Venkataraman , Nadav Shai, Priyanka Lakhiani, Sarah Zylka, Jieqing Zhao, Margaret Herre, Joshua Zeng, Lauren A Neal, Henrik Molina, Li Zhao, Leslie B Vosshall. Two novel, tightly linked, and rapidly evolving genes underlie Aedes aegypti mosquito reproductive resilience during drought. Elife. 2023 Feb 6;12:e80489. PMID: 36744865 DOI: 10.7554/eLife.80489</p>
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
Ports, Past and Present Blog (perma.cc link tabular data)
<p>A list of blog and calendar posts from the Ports, Past and Present project written on Wordpress. This tabular data is a modified output from the <a href="https://perma.cc/">perma.cc</a> folder containing a series of WARC records captured on the 26th of June, 2023.</p>
Ha-SingleMoleculeLab's data for publication: Linking folding dynamics and function of SAM/SAH riboswitches at the single molecule level
<p>This upload is the raw data that support our findings sent to review on Nucleic Acids Research, corresponding to each individual figure. The paper title is " Linking folding dynamics and function of SAM/SAH riboswitches at the single molecule level".</p>
Pseudonymised Dataset of the Characterising the IIIF and Linked Art communities survey
<p>This is the pseudonymised<em> </em>dataset of the survey titled "Characterising the IIIF and Linked Art communities" that was conducted between 24 March and 7 May 2023. The survey explored the socio-technical characteristics of two prevalent community-driven initiatives in the cultural heritage domain, namely the International Image Interoperability Framework (IIIF) as well as Linked Art. The survey was carried out as part of the PhD Thesis titled "Linked Open Usable Data for Cultural Heritage: Perspectives on Community Practices and Semantic Interoperability" (see <a href="https://phd.julsraemy.ch" target="_blank" rel="noopener">https://phd.julsraemy.ch</a>).</p> <p>The survey report is available at <a href="https://hal.science/hal-04162572">https://hal.science/hal-04162572</a></p> <p>The dedicated GitHub repository is available at: <a href="https://github.com/julsraemy/loud-socialfabrics/" target="_blank" rel="noopener">https://github.com/julsraemy/loud-socialfabrics/ </a></p>
Cothran, R. D., F. Radarian, and R. A. Relyea. 2011. Altering aquatic food webs with a global insecticide: Arthropod-amphibian links in mesocosms that simulate wetland communities. Journal of the North American Benthological Society 30:893-912.
Pesticides play a critical role in maximizing yields of economically important crops and minimizing the human health threats of disease-carrying pests, but they often have collateral effects on nontarget species. We used a mesocosm study to address how the most commonly used insecticide in the USA, malathion, applied at low, ecologically relevant concentrations (20 and 110 mg/L) affects species interactions in aquatic communities. Unlike many community ecotoxicology studies, our study assessed how malathion affects both consumptive and nonconsumptive effects of predators. We also considered how the vertical distribution of predator cues and malathion (caused by potential stratification) affects species interactions. We found no evidence for vertical stratification of malathion, a result suggesting that exposure to the pesticide was uniform throughout the water column. Malathion was lethal to some primary consumers (cladocerans) at both concentrations and to top predators (dragonflies) at the highest concentration (110 mg/L). These lethal effects initiated density-mediated indirect effects in both cases. Malathion also may have decreased dragonfly foraging efficiency, resulting in increased tadpole survival (trait-mediated indirect effect), which decreased the resources used by tadpoles (periphyton). Collectively, our results show that malathion alters species interactions. However, we suggest that the degree to which pesticides affect aquatic communities will depend strongly on the species composition of communities. Therefore, the community-level consequences of pesticide exposure are likely to vary across the ecological landscape.
Wikimedia Links to UK Repositories (snapshot)
<p>This is a snapshot (08/12/2019) of links from Wikipedia to UK repositories using the "insource" parameter on this special page to search across Wikipedia <a href="https://en.wikipedia.org/w/index.php?sort=relevance&search=">https://en.wikipedia.org/w/index.php?sort=relevance&search=</a></p> <p>It covers institutional open access and data repositories. It also includes non-institutional and subject specific data repositories including figshare, zenodo, datadryad</p> <p>N.B. There are over 150 HEIs in the UK and for practical reasons it is only complete across the Russell Group. Other repositories were partially crowdsourced via the UKCoRR mailing list which can be updated via this Google sheet for future iterations of the dataset <a href="https://docs.google.com/spreadsheets/d/1rJ3Se0y6dJJW43SLeFxaKlSsdrQN5p4lQLK_rMhxAZQ/edit#gid=0">https://docs.google.com/spreadsheets/d/1rJ3Se0y6dJJW43SLeFxaKlSsdrQN5p4lQLK_rMhxAZQ/edit#gid=0</a></p>
Online supplementary data linked to the publication "Aubenas-les-Alpes (S-E France). Part III – Last and final part of the mammalian assemblage with some comments on the palaeoenvironment and palaeobiogeography" doi:10.1016/j.annpal.2019.03.001
<p>Online supplementary appendix including the list of Oligocene localities and associated faunal lists compared to Aubenas-les-Alpes, and the size estimation of the non-predatory species for the construction of Fig.10.</p>
DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats
<p>Inferences of the interactions between species’ ecological niches and spatial distribution have been historically based on simple metrics such as low-resolution dietary breadth and range size, which might have impeded the identification of meaningful links between niche features and spatial patterns. We analysed the relationship between dietary niche breadth and spatial distribution features of European bats, by combining continent-wide DNA metabarcoding of faecal samples with species distribution modelling. Our results show that while range size is not correlated with dietary features of bats, the homogeneity of the spatial distribution of species exhibits a strong correlation with dietary breadth. We also found that dietary breadth is correlated with bats’ hunting flexibility. However, these two patterns only stand when the phylogenetic relations between prey are accounted for when measuring dietary breadth. Our results suggest that the capacity to exploit different prey types enables species to thrive in more distinct environments and therefore exhibit more homogeneous distributions within their ranges.</p>
Fig. 2 in Missing geographic link: minute lady beetles (Coleoptera: Coccinellidae: Microweiseinae) from Mount Wilhelm, New Guinea
Fig. 2. Morphology of Scymnomorphus species. A–H – S. bimaculatus sp. nov.: A – abdomen, female; B – antenna; C – ovipositor; D – spermatheca; E – tegmen; inner view; F – tegmen, lateral view; G – penis, lateral view; H – abdomen, male. I–P – S. kausi sp. nov.: I – abdomen, female; J – antenna; K – ovipositor; L – spermatheca; M – tegmen, inner view; N – tegmen, lateral view; O – penis, lateral view; P – abdomen, male (arrows indicate glandular opening pores).
Repository: Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment
<p>This repository contains the input data, codes and results of the model developed in the paper "Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade-linked multilevel life cycle assessment" published in the Journal of Industrial Ecology (2020) by Alexandre Milovanoff, I. Daniel Posen, Heather L. MacLean.</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>
Figure 2. D in Neotypification of Drawida hattamimizu Hatai, 1930 (Annelida, Oligochaeta, Megadrili, Moniligastridae) as a model linking mtDNA (COI) sequences to an earthworm type, with a response to the 'Can of Worms' theory of cryptic species
Figure 2. D. hattamimizu unscaled habitus (from Watanabe, 2005, fig. 1 after Hatai's 1931 original).
10 Women of Digital Humanities: An Analysis of Linked Open Data
<p>This is a spreadsheet of linked open data, including tweets of the 10 female scholars randomly chosen for this project. This is an experimental study, and was prepared for my own learning. Presentation prepared for a Masters seminar in Information Science at uOttawa in Winter 2020. </p> <p>Linked Open Data in the Humanities was taught by Prof. Constance Crompton.</p> <p>10 Women of Digital Humanities: An Analysis of Linked Open Data</p> <p>tags: dh, digital humanities, feminist dh, computational analysis, Voyant, word clouds, vizualization, digital identifiers, open scholarship</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.