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56 results for “Timeline”
45 Vulnerability Discoverability Timelines from the 2019 Collegiate Penetration Testing Competition
<p><em><strong>Description</strong></em></p> <p>This is a collection of manually curated timelines from the 2019 Collegiate Penetration Testing Competition (CPTC). Collection and annotation are described in detail in this publication:</p> <ul> <li>Benjamin S. Meyers, Sultan Fahad Almassari, Brandon N. Keller, and Andrew Meneely. Examining Penetration Tester Behavior in the Collegiate Penetration Testing Competition. Forthcoming at Transactions on Software Engineering and Methodology. https://dl.acm.org/doi/10.1145/3514040</li> </ul> <p><em><strong>Included Files</strong></em></p> <ul> <li><strong><em>2019_cptc_timelines.csv</em>:</strong> Completed timelines for ten teams from the 2019 CPTC nationals competition.</li> <li><strong><em>2019_cptc_timeline_columns.csv</em>:</strong> Descriptions of the columns in <strong><em>2019_cptc_</em></strong><em><strong>timelines.csv</strong></em>.</li> <li><strong><em>2019_cptc_vulnerabilities.csv</em>:</strong> Brief vulnerability descriptions and CWE mappings.</li> </ul> <p><em><strong>Other Resources</strong></em></p> <ul> <li>Complete Splunk log data dumps are available <a href="http://mirrors.rit.edu/cptc/2019/mirrors/nationals/">here</a>. These must be ingested and viewed with a Splunk instance.</li> <li>To request access to the CPTC team reports, please contact Brock Wagehoft (<a href="mailto:bew1127@rit.edu">email</a>).</li> </ul> <p><em><strong>Contact</strong></em></p> <p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p> <p><em><strong>Acknowledgments</strong></em></p> <p>Collection of this data has been sponsored in part by the National Science Foundation grant 1922169, and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
Tagged Twitter timelines for users reporting SARS-CoV-2 infections and related data
<p>Twitter data was collected through the Twitter API v2.0, specifically through the timeline endpoint. Details about the inference of SARS-CoV-2 self-reports and the tagging of the full timeline of each user can be found in the <a href="https://github.com/digitalepidemiologylab/content_changes_paper">GitHub repository</a>. The larger dataset ("preprocessed_data.csv") consists of a total of 8,534,171 tweets posted by 30,856 users from January 1, 2020 to October to September 30, 2021.</p> <p>The raw data from Twitter, including tweet and user IDs, has been removed or anonymized in order to comply with the EPFL guidelines for data sharing.</p> <p>In particular, the date of the tweets was removed, the text of the tweets, URLs and URL domains have been substituted with the "text", "<URL>" and "<URL_DOMAIN>" token respectively.</p> <p>User IDs have been substitued with new IDs in the [0, number of users] range (e.g. U0, U1, ...) .</p> <p>Tweet IDs have been substitued with new IDs in the [0, number of tweets] range (e.g. T0, T1, ...) .</p> <p>In addition to self-explanatory columns about topics, emotions, URL classification and symptoms we tagged, we also share the columns:</p> <ul> <li>pdate: date of the SARS-CoV-2 infection self-report for that user (adjusted with SUTime)</li> <li>effective_date: date of the tweet adjusted with SUTime, when the SUTime columns is available.</li> <li>rel_effective_day(week, month): days (weeks, months) computed with respect to the positivity date (i.e. "pdate" column). Negative numbers refer to tweets posted before the user reported a COVID-19 infection on Twitter.</li> </ul>
Timeline of government interventions and events regarding the COVID-19 pandemic in Sweden December 31, 2019, to May 5, 2023.
<p>The Swedish approach to managing the COVID-19 pandemic has received significant attention in international scholarly work and the press. For this dataset, we have reviewed governmental and media archives to build a detailed timeline that chronicles significant policies, interventions, and events in the Swedish management of COVID-19. The dataset contains summary descriptions of what took place, when it happened, and who the principal actors involved were. Links to primary sources are provided for each entry. Because of the level of detail and saturation, the dataset offers a detailed account of Swedish pandemic governance and will benefit anyone working on Swedish pandemic management or doing comparative work between Sweden and other jurisdictions.</p> <p>The dataset contains details on the date an event took place (column 1), tags to facilitate navigation (column 2), details on the principal actors involved in the event (column 3), a summary description of what took place and who was involved (column 4), and links to primary materials (e.g., archival entries) (columns 4-12). Through a structured and detailed outline, the dataset provides a saturated account of policy interventions and events in Sweden during the COVID-19 pandemic for the period 2020-2023 until it was no longer considered a public health emergency of international concern by the WHO and complements existing and less detailed timelines published at earlier points in the period.</p>
SyRI Timeline
<p>Historical timeline of the SyRI case, from 2014-2020. It covers SyRI's predecessors (Waterproof and Black Box) as well.</p>
Dataset Timeline Covid-19
<p>O projeto Timeline Covid-19 tem como objetivo produzir, através da recuperação de informações online, notícias veiculadas pela grande mídia e pelas instituições federais a respeito de acontecimentos e fatos ocorridos em solo brasileiro ligados à Covid-19. Consiste em uma linha do tempo iniciada no dia 26 de fevereiro de 2020, data da confirmação do primeiro caso do novo coronavírus no Brasil, e não possui uma data final pré-definida, continuando a ser alimentada até o momento [24 mar 2022].</p> <p>A planilha é o <em>dataset</em> base para a Timeline (usando tecnologia <a href="http://timeline.knightlab.com/">Timeline JS</a> do<a href="https://knightlab.northwestern.edu/"> KnightLab</a> da <a href="https://www.northwestern.edu/">Northwestern University</a>, EUA) publicada no portal do Ministério da Ciência, Tecnologia e Inovações no combate à COVID-19 <<a href="http://covid19.mctic.gov.br/graf/">http://covid19.mctic.gov.br/graf/</a>>. Neste <em>dataset</em> está presente o conteúdo da linha do tempo completa.</p> <p>Na planilha, as colunas de A a I são relacionadas à data de início e encerramento de um evento. Como a proposta da linha do tempo é tratar de notícias diárias, apenas uma data foi mantida para marcar seu acontecimento e não denotar sua continuidade. As demais colunas consistem em:</p> <ul> <li> <p><strong>Headline</strong>: o título da notícia, sintetiza o conteúdo da reportagem e o exibe nas duas seções da linha do tempo;</p> </li> <li> <p><strong>Text</strong>: uma breve sinopse ou descrição do que trata a notícia para ser exibida no detalhamento da linha do tempo;</p> </li> <li> <p><strong>Media</strong>: link para a imagem exibida na descrição da notícia presente na segunda seção;</p> </li> <li> <p><strong>Media Credit</strong>: indicação do veículo de informação que publicou a notícia referendada;</p> </li> <li> <p><strong>Media Caption</strong>: link para a notícia publicada;</p> </li> <li> <p><strong>Background</strong>: código para a cor a ser utilizada no fundo da seção inferior da linha do tempo.</p> </li> </ul> <p>Disponibilizamos aqui atualizações da planilha onde cada versão é mais completa que sua antecessora. Além disso, também promovemos o trabalho de troca dos links que quebraram ao longo do tempo.</p>
Twitter Timelines of British MPs (Tweet IDs)
<p>TSV file containing Twitter tweet_ids from the Timelines of 584 members of British Parliament (collected between 4th and 6th of March 2022). The users were identified from the link below:</p> <p>https://www.ukinbound.org/resources/list-of-mp-twitter-accounts/</p> <p> </p> <p>If you use this dataset for an academic work, please reference the following paper:</p> <pre>@article{tacchi2022signed, title={Signed ego network model and its application to Twitter}, author={Tacchi, Jack and Boldrini, Chiara and Passarella, Andrea and Conti, Marco}, journal={arXiv preprint arXiv:2206.15228}, year={2022} }</pre>
Birmingham's Institute of Forest Research (BIFoR) Timeline of Events
<p><span>Figure depicting the timeline of significant events that took place at the Birmingham Institute of Forest Research (BIFoR) Free Air Carbon dioxide Enrichment (FACE) facility between 2016 and 2022.</span></p>
ESCALATOR Timeline
<p>The infographic was developed to show highlights of each of the five phases of the ESCALATOR programme between its inception in 2020 and the current phase in May 2024. The image is used in the close-out report that summarises activities and outputs for the period December 2020 - May 2024.</p> <p><a href="https://escalator.sadilar.org">ESCALATOR</a> is a programme developed by the <a href="https://sadilar.org">South African Centre for Digital Language Resources</a> (SADiLaR). ESCALATOR aims to support the development of an active and inclusive community of practice in Digital Humanities and Computational Social Sciences in South Africa.</p> <p>SADiLaR is supported by the Department of Science and Innovation as part of the South African Research Infrastructure Roadmap initiative.</p>
Fig. 1 in Timeline and geographical distribution of Helicoverpa armigera (Hübner) (Lepidoptera, Noctuidae: Heliothinae) in Brazil
Fig. 1. PCR-RFLP agarose gel (1.5%) for COI amplification products (511 b) using BstZ17I endonuclease. Lane 1: 100 bp MW; lanes 2 and 5: undigested product of Helicoverpa zea (Londrina, PR); lanes 3 and 4: digestion product of Helicoverpa armigera (Londrina, PR); lane 6: digestion product of H. armigera; lanes 7–10: undigested product of H. zea (Planaltina, DF); lanes 11–15: digestion product of H. armigera (Luiz Eduardo Magalhães, BA); lanes 16–20: digestion product of H. armigera (Carambeí, PR); lanes 21 and 22: undigested product of H. zea (Arapoti, PR); lanes 23 and 24: digestion product of H. armigera (Sengés, PR); lanes 25–28: digestion product of H. armigera (Taquarituba, SP); lane 29: undigested product of H. zea (Taquarituba, SP); lane 30: 100 bp MW.
FIGURE 5. Event timeline. 1 in Unveiling trampling history through trackway interferences and track preservational features: a case study from the Bletterbach gorge (Redagno, Western Dolomites, Italy)
FIGURE 5. Event timeline. 1, Ripple marks and Gl1 tracks formation; 2, Gl2 and Gl3 tracks formation; 3; Gl4 trackway formation; 4, Jb trackway formation; 5, Gl5 and Ct1 trackways formation; 6, Ct2 trackway formation; 7, Gl6 and Gl7 trackways formation; 8, Pd trackway formation; 9, Final interpretative drawing of the slab MPUR NS 34/28; 10, Trackmakers advancement directions. rip, ripple marks; Pd, Pachypes dolomiticus trackway; Ct1-Ct2, Chelichnus tazelwürmi trackways; Gl1-Gl7, Ganasauripus ladinus trackways; Jb, Janusichnus bifrons trackway; dcr,? desiccation cracks. Scale bar is 50 cm.
Timeline of generative models by type
<p>Timeline of generative models by type. Part of the study "What do we mean by GenAI?"</p>
The importance of evolutionary timelines when explaining the evolution of parental care strategies
Open the record for dataset details and reuse information.
Rebel Media contributors timeline
<p>Data on the tenures of every person to have appeared on Rebel Media's masthead. Start dates for each contributor's tenure are cleaned up from the Wayback Machine's caches of the Rebel Media masthead list, formerly located at therebel.media/therebels before the website changed its top-level domain to rebelnews.com. The first cached copy is from February 15th 2015, which is the same day that the Rebel posted their first YouTube video. The most recent copy of the list that is mapped is a cached copy from May 17th 2019. End dates from this data have been adjusted based on news reports of hosts' departures where applicable.</p>
Data from: A phylogenomic framework, evolutionary timeline and genomic resources for comparative studies of decapod crustaceans
Comprising over 15 000 living species, decapods (crabs, shrimp and lobsters) are the most instantly recognizable crustaceans, representing a considerable global food source. Although decapod systematics have received much study, limitations of morphological and Sanger sequence data have yet to produce a consensus for higher-level relationships. Here, we introduce a new anchored hybrid enrichment kit for decapod phylogenetics designed from genomic and transcriptomic sequences that we used to capture new high-throughput sequence data from 94 species, including 58 of 179 extant decapod families, and 11 of 12 major lineages. The enrichment kit yields 410 loci (greater than 86 000 bp) conserved across all lineages of Decapoda, more clade-specific molecular data than any prior study. Phylogenomic analyses recover a robust decapod tree of life strongly supporting the monophyly of all infraorders, and monophyly of each of the reptant, 'lobster' and 'crab' groups, with some results supporting pleocyemate monophyly. We show that crown decapods diverged in the Late Ordovician and most crown lineages diverged in the Triassic–Jurassic, highlighting a cryptic Palaeozoic history, and post-extinction diversification. New insights into decapod relationships provide a phylogenomic window into morphology and behaviour, and a basis to rapidly and cheaply expand sampling in this economically and ecologically significant invertebrate clade.
ETimeline: An Extensive Timeline Generation Dataset based on Large Language Model
<div> <div>Timeline generation is of great significance for a comprehensive understanding of the development of events over time. Its goal is to organize news chronologically, which helps to identify patterns and trends that may be obscured when viewing news in isolation, making it easier to track the development of stories and understand the interrelationships between key events. Timelines have appeared in many commercial products, but there is a noticeable lack of research in this field in academia, and existing datasets need improvement in terms of effectiveness and scale. We propose the ETimeline dataset, which contains over 13,000 news articles, covering 600 bilingual timelines across 23 news domains. We collected more than 120,000 news articles as a candidate news pool and used the large language model (LLM) Pipeline to enhance performance, ultimately obtaining the ETimeline, and the news pool data will also be provided. This work contributes to the advancement of timeline generation research and supports a wide range of tasks, including topic generation and event relationships. We believe that this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the practical application of technology services.</div> </div>
Timeline Martian Climate Through Time
<p>Illustration of the surface and subsurface water cycle on Mars through the main geological periods.</p> <p>English and french avalaible, adapted for black or white background.</p>
Fagottino and Tenoroon Timeline
<p>Geographical depiction of fagottino and tenoroon builders in the 18th and 19th centuries.</p>
Timeline of drug development in inflammatory bowel disease
<p>Timeline of drug development in inflammatory bowel disease from the first trial published in 1955 until 2024.</p> <p>Detailed explanation of these trials can be found at www.ibd-eii.com</p>
Cocoa life cycle - timeline
<p>Schematic drawing of the growth cycle of a cocoa plant from seed to fruit</p>
Proof of Concept database with inputs and outputs of the Master thesis: Analyzing Software Delivery Performance behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics
<p>The software has become one of the main assets to deliver services today. Thus, software delivery has been dealing with a competitive and dynamic environment where the demand for faster and more assertive deliverables, called here Releases, only increases. Agile development methods emerged helping to accelerate software delivery, embracing industry and open source community. Since then, the software delivery frequency has expanded and improved bringing more adopters of rapid release cycles to reduce their time-to-market. However, using only rapid releases can not be enough as measuring software delivery can answer essential questions, like how software delivery is happening and how it should be. Some approaches for measuring software delivery appeared such as Software Delivery Performance (SDP) where software delivery is measured as a consequence of capabilities evolution. Popularity in Open Source Software Projects (OSSP) means that a project is mature enough in the community to fit the software demand and, therefore, is likely to be ready to be measured through a software delivery approach like SDP. In light of it, this work offers means to analyze SDP behavior in popular Open Source Software Projects on a Release timeline basis through delivery metrics. The results demonstrated that popularity is efficient filtering, as it improves the OSSP delivery, supporting the work's reliability and accuracy. The source code and methodology are published as a replication package to encourage reproducibility and future research.</p>
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