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FIGURE 2. A in ZooNom: an online thesaurus for alleviating ambiguity in the terminology of zoological nomenclature
FIGURE 2. A screenshot of ZooNom taken on 10/11/2021. The concept shown is "doxisonym" (URL: https://www.loterre.fr/ skosmos/FM8/en/page/-PD692RFQ-4). The "note" field contains the etymology, and the "scope note" field presents the Code's equivalent (objective synonym) and definition.
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known. in Towards Target 1 of the Global Strategy for Plant Conservation: A working list of all known plant species - Progress and prospects
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known.
Online Supplement: Search-based Diverse Sampling from Real-world Software Product Lines
<p>This is the online supplement for the following paper: Yi Xiang, Han Huang, Yuren Zhou, Sizhe Li, Chuan Luo, Qingwei Lin, Miqing Li, and Xiaowei Yang. 2022. Search-based Diverse Sampling from Real-world Software Product Lines. In 44th International Conference on Software Engineering (ICSE’22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3510003.3510053</p>
Buy Light Pink Soft Woolen Scarfs Online
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Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"
<p>These are the raw plot files from the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history".</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>
Web scraping and API projects from "Online Data Collection and Management" (Spring 2022)
<p>As part of "Online Data Collection and Management" (taught at Tilburg University, Spring 2022), students collected publicly available datasets for use in academic research projects. With this repository, I am sharing (a) the documentation of these data sets, and (b) the associated source code that led to the collection of the data. The repository also contains the collected datasets.</p> <p>The data consists of the following projects:</p> <ul> <li>Autoscout (electric cars vs gasoline cars in the Dutch market)</li> <li>Mediamarkt (e-commerce)</li> <li>Steam API</li> <li>Twitch (chat capture)</li> <li>Zalando (e-commerce)</li> </ul> <p>Course website: https://odcm.hannesdatta.com. Archived at https://doi.org/10.5281/zenodo.6641811 (check for more recent versions if available).</p>
Extreme Poisson's Ratios Recorded In The Secondary Phloem Of Malvaceae (online data)
<p>This zip file contains two dataset (in xl format), namely the experimental results and the parametric study in relation to the published article</p>
Episodio inaugurale del 17 giugno 2022 del convegno online sulla testimonianza
<p>Episodio inaugurale del 17 giugno 2022 del convegno online sulla testimonianza, "Testimonianza e testimoni. Memoria, presenza e testimonianza tra passato e presente", in occasione del Premio Nazionale di Filosofia</p>
Episodio del 24 giugno 2022 del convegno online sulla testimonianza
<p>Episodio del 24 giugno 2022 del convegno online sulla testimonianza, "Testimonianza e testimoni. Memoria, presenza e testimonianza tra passato e presente", in occasione del Premio Nazionale di Filosofia</p>
Episodi 19 giugno 2022 del convegno online sulla testimonianza
<p>2 episodi del19 giugno 2022 in seno al convegno online sulla testimonianza, "Testimonianza e testimoni. Memoria, presenza e testimonianza tra passato e presente", in occasione del Premio Nazionale di Filosofia</p>
Episodi del 18 giugno 2022, convegno online sulla testimonianza
<p>2 Episodi del 18 giugno 2022, in seno al convegno online sulla testimonianza, "Testimonianza a testimoni. Memoria, presenza e testimonianza tra passato e presente", in occasione del Premio Nazionale di Filosofia, edizione 2022</p>
Episodi del 25 giugno 2022 del convegno online sulla testimonianza
<p>2 episodi della mattina del 25 giugno 2022 in seno al convegno online sulla testimonianza, "Testimonianza e testimoni. Memoria, presenza e testimonianza tra passato e presente", in occasione del Premio Nazionale di Filosofia</p>
A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109</p> <p><strong>Abstract</strong></p> <p>The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p><strong>Data Description</strong></p> <p>The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.</p> <p>The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development. </p> <ul> <li>Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)</li> <li>Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)</li> <li>Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)</li> <li>Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)</li> <li>Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)</li> <li>Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)</li> <li>Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)</li> <li>Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)</li> <li>Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)</li> </ul> <p>The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link to download</a> and a <a href="https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">step-by-step tutorial</a> on how to use Hydrator) may be used.</p> <p><strong>Table 1</strong>. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development</p> <table> <tbody> <tr> <td> <p>Terminology</p> </td> <td> <p>List of synonyms and terms</p> </td> </tr> <tr> <td> <p>COVID-19</p> </td> <td> <p>Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus</p> </td> </tr> <tr> <td> <p>online learning</p> </td> <td> <p>online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures</p> </td> </tr> </tbody> </table> <p> </p>
Ptolemy's adaptation of the Helikōn, diagram from Lynch, T.A.C. (forthcoming), 'Music', Oxford Classical Dictionary online.
<p>Ptolemy’s adaptation of the <em>Helikōn, </em>diagram from Lynch, T.A.C. (forthcoming), ‘Music’, Oxford Classical Dictionary online.</p>
[Supplementary material] Online Testing of RESTful APIs: Promises and Challenges
<p>This is the supplementary material of the paper entitled <em>Online Testing of RESTful APIs: Promises and Challenges</em>.</p> <p><strong>NOTE: An online easier-to-navigate version of this supplementary material is available at <a href="https://anonymous.4open.science/r/fse2022-supplementary-material-1172">https://anonymous.4open.science/r/fse2022-supplementary-material-1172</a>.</strong></p>
Effects of topicality in the Interpretation of implicit consequentiality: Evidence from offline and online referential processing in Korean
<p>This supplementary material contains data files and R scripts that accompany the article to be published in Linguistics (volume 61, 2023).</p>
Online Appendix of "Crowd-based Requirements Elicitation via Pull Feedback: Method and Case Studies"
<p>The online appendix contains the data sets used in the paper Crowd-based Requirements Elicitation via Pull Feedback: Method and Case Studies, by Jelle Wouters, Abel Menkveld, Sjaak Brinkkemper and Fabiano Dalpiaz. It consists of three data sets, a process-deliverable diagram, a file that contains charts, and two Jupyter (Python) notebook scripts. In this readme, we briefly explain how files should be read and interpreted.</p> <p><strong>Dataset-Tournify.xlsx</strong></p> <p>This file contains all data of the Tournify case. The following tabs are present:</p> <ul> <li> <p>Raw data: contains the raw data collected from the Tournify CrowdRE platform.</p> </li> <li> <p>Automatically translated data: The readability and vagueness measures were calculated using English text. Most of the ideas collected were in Dutch, so the raw data was translated into English using a Google Translator API.</p> </li> <li> <p>Readability scores: Consists of the Flesch and ARI readability scores, calculated using the Python scripts (see below). </p> </li> <li> <p>Vague hits: Consists of all the vague words found in the Tournify ideas. We identified those using a Python script. Using numbers we identified whether the hit was a True Positive (TP) or was a false positive, and in which category the false positive lied. </p> </li> <li> <p>Tagging-50-FD & Tagging-50-JW: These tabs contain the tagging of the ideas on qualities of the QUS-framework and the ISO/IEC 25010. Two researchers did this independently from another.</p> </li> <li> <p>Compare-50: This sheet consists of all ideas for which a disparity exists between the two fields. When a disagreement exists, the field is colored green. By text in the field, we indicated what the final decision was. </p> </li> <li> <p>Result-50: This tab combines the results and shows the final decision after deliberation between the two authors.</p> </li> <li> <p>Tagging-195-FB, Tagging-195-JW, Compare-195, Result-195: Same as above, but for the other 195 ideas in the case (we split this data set in two to try out the modus operandi first).</p> </li> <li> <p>Result-total: Combines the Result-50 and the Result-195 sheet. Colored cells indicate that we marked the idea to be considered to present verbatim in the paper. </p> </li> <li> <p>Kappa-scores: Calculates the Kappa-scores that are presented in the paper.</p> </li> </ul> <p><strong>Dataset-SSys.xlsx and Dataset-VSys.xlsx</strong></p> <p><strong>This file contains all data for respectively the S-Sys and V-Sys cases. The following tabs are present:</strong></p> <ul> <li> <p>Raw data: consists of the raw data collected in the CrowdRE platform of the case. As can be seen, some data is ‘not published’ (but was analyzed in the study and read by both researchers and therefore just redacted in the online appendix), and some data is ‘classified’ (and therefore not analyzed by both researchers as one researcher was not allowed to review the data). The classified ideas should be considered as non-existent in the rest of the data set. </p> </li> <li> <p>Automatically translated data, readability scores: These files are compiled in the same way as in the Tournify case.</p> </li> <li> <p>Vagueness: This file is compiled in the same way as in the Tournify case, although we do provide a small explanation that describes a part of the idea to show why a true or false positive was indicated.</p> </li> <li> <p>Tagging: The tagging of the QUS-framework and ISO/IEC 25010 was done here. As we did this in person, the small discussion held when discrepancies occurred is not presented in the file. The colored cells indicate ideas we considered for verbatim publication in the paper. The colors indicate why we want to publish a certain idea (for example, because it has all QUS-violations).</p> </li> <li> <p>Kappa-scores: This shows the kappa scores and the discrepancies between the individual tagging. This is indicated in colors. </p> </li> </ul> <p><strong>Graphs_readability.aspx</strong></p> <p>This file was used to construct the graphs as presented in figures six and seven in the paper. For this, the readability scores of the three data sets were combined in one tab (one for Flesch and one for ARI) and used in a boxplot.</p> <p><strong>CREUS-pdd.drawio</strong></p> <p>This is the source file for the PDD presented in the paper.</p> <p><strong>Python scripts</strong></p> <p>Two Jupyter notebooks that "S-Sys V-Sys.ipynb" and "Tournify.ipynb" that we used to calculate the readability scores and to identify the vague words. As the data sets were structured a bit differently, the scripts are a bit different between the three cases. In the Tournify case, the script and the output are both present in the Jupyter notebook. For the S-Sys and V-Sys cases, we do include the script but omit the output due to confidentiality.</p> <p><strong>Note:</strong></p> <p>The data sets are included in our paper to allow readers to explore the data themselves. Please contact the corresponding author if you wish to use the data set for other reasons to obtain an explicit permission, since these user stories should be analyzed with proper domain knowledge in order to draw meaningful conclusions.</p>
Online supporting material: †Estelestes ensis (Mammalia, Metatheria) from the Early Eocene of Baja California, Mexico, as a generalized polydolopimorphian
<p>In Table 1, we provide the ratio between the length of m1-4 and the height of the dentary below m1 in several extinct and extant metatherians from the Americas. We give the taxonomic assignment (Family, Order) for each of the measured species and indicate the source of the figure where the ratio was obtained. In Figure 1, we show details of the anterior portion of the left lower jaw of <em> Bobbschaefferia</em> <em>fluminensis</em> (MNRJ 1350-V, cast of the holotype) in occluso-lateral and occlusal views.</p>
Supplementary material 2 from: Talamas EJ, Thompson J, Cutler A, Fitzsimmons Schoenberger S, Cuminale A, Jung T, Johnson NF, Valerio AA, Smith AB, Haltermann V, Alvarez E, Schwantes C, Blewer C, Bodenreider C, Salzberg A, Luo P, Meislin D, Buffington ML (2017) An online photographic catalog of primary types of Platygastroidea (Hymenoptera) in the National Museum of Natural History, Smithsonian Institution. In: Talamas EJ, Buffington ML (Eds) Advances in the Systematics of Platygastroidea. Journal of Hymenoptera Research 56: 187–224 https://doi.org/10.3897/jhr.56.10774
Non-primary type specimens of Platygastroidea in USNM : Explanation note: This table contains a list of species and specimens in USNM that are determined to species, but not represented by primary types, and for which images are publicly available.
Supplementary material 1 from: Talamas EJ, Thompson J, Cutler A, Fitzsimmons Schoenberger S, Cuminale A, Jung T, Johnson NF, Valerio AA, Smith AB, Haltermann V, Alvarez E, Schwantes C, Blewer C, Bodenreider C, Salzberg A, Luo P, Meislin D, Buffington ML (2017) An online photographic catalog of primary types of Platygastroidea (Hymenoptera) in the National Museum of Natural History, Smithsonian Institution. In: Talamas EJ, Buffington ML (Eds) Advances in the Systematics of Platygastroidea. Journal of Hymenoptera Research 56: 187–224 https://doi.org/10.3897/jhr.56.10774
Primary type specimens of Platygastroidea in USNM : Explanation note: This table contains a list of all primary types of Platygastroidea in USNM. This list includes paratypes and paralectotypes that were photographed to supplement images of primary types that are incomplete or damaged.
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.