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263 results for “forensics”
Fig. 1 in Forensic bioacoustics? The advertisement calls of two locally extinct frogs from Colombia
Fig. 1. Geographic location (A) and general view (B) of Reserva Natural La Planada (Department of Nariño, Colombia; (C) Paruwrobates andinus and (D) Gastrotheca guentheri from Reserva Natural La Planada, Colombia. Photos by I. De la Riva (B) and P.A. Burrowes (C–D).
Fig. 2 in Forensic bioacoustics? The advertisement calls of two locally extinct frogs from Colombia
Fig. 2. Full-scale oscillogram (top), and expanded oscillogram and its audiospectrogram (bottom) of the advertisement call of Paruwrobates andinus. Call groups (A, B, and C), inter-call group interval (ci), and background noise (bn) are represented in the full-scale oscillogram. The note duration (nd), inter note interval (ni), dominant frequency (df), and fundamental frequency (ff) are indicated in the expanded box.
DFPulse: The 2024 Digital Forensic Practitioner Survey (Response Dataset)
<h2><strong>Information given to survey respondents:</strong></h2> <p><strong>Introduction</strong></p> <p>The aim of this survey is to improve the relevance of academic work to practitioners, and to improve the flow of information between the digital forensic researcher and practitioner communities.</p> <p>This survey consists of three sections: </p> <p>* demographics (19 questions)</p> <p>* the challenges you face (20 questions), </p> <p>* engagement with academia (11 questions, optional). </p> <p>If you wish, you can stop and submit at the end of Section 2 the responses that you've given in Sections 1 and 2. We estimate that completing as far as the end of Section 2 will take approximately 20-25 mins, and the full survey approximately 30 minutes.</p> <p>We appreciate that your time is extremely valuable, but we hope to use this survey to direct academic research to be of maximum value to you. We would really appreciate you giving up some of your time to provide input to this survey to help make digital forensic research more relevant to you, and to help us communicate the results of that research more effectively to you.</p> <p><strong>Who is conducting this?</strong></p> <p>This survey is being conducted by Assoc. Prof. Mark Scanlon in the School of Computer Science, University College Dublin, Ireland. The title of this research is “Exploring the issues faced by digital forensic practitioners and identifying the relationship between academic research and digital forensic practice”. There are five collaborators on the project:</p> <p>Assoc. Prof. Frank Breitinger, School of Criminal Justice, University of Lausanne, Switzerland.<br>Dr. Liz Dowthwaite, School of Computer Science, University of Nottingham, United Kingdom.<br>Dr. Chris Hargreaves, Department of Computer Science, University of Oxford, United Kingdom.<br>Assoc. Prof. Mark Scanlon, School of Computer Science, University College Dublin, Ireland.<br>Dr. Helena Webb, School of Computer Science, University of Nottingham, United Kingdom.</p> <p><strong>What is this research about?</strong></p> <p>Exploring the status quo of (academic) research and digital forensic practitioners, i.e., has the research an impact on the practitioners. These also includes exploring if/how practitioners follow academic output/venues.</p> <p><strong>Why are we do</strong><strong>ing this research?</strong></p> <p>To better understand the needs of practitioners and to explore the interaction between digital forensic practitioners and researchers.</p> <p><strong>How will your data be used?</strong></p> <p>Responses will be discussed among the study authors. Most relevant findings will be summarised in a research article and the anonymous responses will be shared with the community.</p> <p><strong>What will happen if you decide to take part in this research study?</strong></p> <p>This anonymous online survey will take you approximately 30 minutes to complete.</p> <p><strong>How will we protect your privacy?</strong></p> <p>The responses given to the survey are anonymous, with no uniquely identifiable information recorded.</p> <p><strong>What are the benefits of taking part in this research study?</strong></p> <p>Taking part in this survey will enable you to inform future digital forensic research directions. The results of the survey will be disseminated with the digital forensic research community.</p> <p><strong>What are the risks of taking part in this research study?</strong></p> <p>The risks in taking part in the survey are negligible as all data collection is anonymous.</p> <p><strong>Can I change my mind at any stage and withdraw from the study?</strong></p> <p>During the completion of the survey, you can withdraw at any time. You can withdraw by closing the window and/or simply not submitting your responses on the last page of the survey. Partially completed surveys will not be retained. After your response is submitted, it cannot be subsequently withdrawn, as your response is anonymous.</p> <p><strong>How will I find out what happens with this project?</strong></p> <p>The intention is that the insights gained from this survey will be disseminated through common academic channels including any/all of the following: journal article or conference paper publication, poster publication, preprint publication, data sharing, conference presentation, classroom sharing, and/or informing future research directions.</p>
Forensic examination of Wuhan Institute of Virology COVID-19 patient specimens from December 2019 reveals gross contamination of the laboratory, including undisclosed research on a lethal BSL-4 pathogen
<p>A Zenodo pre-print entitled, “CONTAMINATION OR VACCINE RESEARCH? RNA Sequencing data of early COVID-19 patient samples show abnormal presence of vectorized H7N9 hemagglutinin segment” has been published. In the paper, a forensic examination of the sequencing data from five COVID-19 bronchial lavage patient specimens reveals that the laboratory at the Wuhan Institute of Virology (WIV) was contaminated with a wide range of viruses, including the Nipah virus, a BSL-4 pathogen with a lethality of 50% to 92%. A video summary of the paper can be found here.</p> <p>The highlights of the paper are:</p> <ul> <li>Five patient specimens were sequenced by the WIV in December 2019 and were part of an early report on SARS-CoV-2 published by Dr. Zhengli Shi and colleagues (Nature 579, 270–273 (2020). This paper has been viewed over one million times, making it one of the most highly read paper on the pandemic virus.</li> <li>The most abundant contaminant is an undisclosed H7N9 influenza vaccine, which in one specimen is over six-times as abundant as SARS-CoV-2.</li> <li>The Nipah virus gene sequences were found in infectious cloning vectors of the type used for genetic manipulation.</li> <li>Nineteen other contaminants, including Japanese Encephalitis virus, HIV, human T-cell leukemia virus, and hepatitis delta virus were found.</li> </ul> <p>It is important to investigate why the Wuhan Institute of Virology was extensively contaminated in December 2019 as well as learn more about the undisclosed Influenza Vaccine research and Nipah virus cloning experiments. </p> <p>The Zenodo pre-print can be found here: https://zenodo.org/record/5067706#.YPtyyI4zbOg</p>
Network of reference tree-ring chronologies for forensic botanical (dendrochronological) examinations and dating of architectural structures in the Tyva Republic.
<p>The database consists of tables. First sheet - general description of tree-ring chronologies (general information: name of chronology, authors, data type, tree-ring parameter, notes, key words; description of sample collection site: site name, location, region, latitude, longitude, height; description of sample collection: collection code designation, number of series, year of first ring, year of last ring, maximum length of sample, average width of year ring; species affiliation - species; support - grant number). Second sheet, first column - years, second column - standardized growth value. The third sheet is a PDF document containing the results of independent testing in the program COFECA (the file is opened by the command: right-click/Acrobat Document object/open). The database is implemented in the OpenOffice.org Calc spreadsheet processor. The table file format is an internal OpenOffice.org Calc format, with the extension .ods. The data is accessed and structured using the standard tools "Sort", "Autofilter", etc. In the database, the integrity restriction control is not implemented, the user is invited to monitor the integrity of the database himself. Computer type: IBM PC. PC; OS: Windows 10.</p> <p>Type and version of the database management system: OpenOffice.org Calc.</p> <p>Database size: 5.6 MB</p>
Dataset Literature Review Digital Forensic and Image Processing
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>digital forensic</em>" dan "<em>image processing</em>"</p>
Dataset Literature Review Digital Forensic and Ghana
<p>Bahwa data ini digunakan untuk membuat penelitian sesuai dengan Tinjauan Literatur dengan kata kunci <em>"Digital Forensic </em>dan<em> Ghana"</em></p>
Dataset Literature Review Digital Forensic and Female
<p>Data ini digunakan untuk membuat penelitian sesuai dengan Tinjauan Literatur dengan kata kunci "digital forensic" dan "female"</p>
Dataset Literature Review Digital Forensic and Reproducibility of Result
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>digital forensic" dan</em> "<em>reproducibility of result"</em></p>
Dataset Literature Review Digital Forensic and Humans
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "D<em>igital Forensic dan Humans"</em></p>
Dataset Literature Review Digital Forensic and Digital Forensics
<p>Data ini digunakan untuk membuat penelitian sesuai dengan Tinjauan Literatur dengan kata kunci "<em>digital forensic</em>" dan "<em>digital forensics</em>"</p>
Dataset Literature Review Digital Forensic AND Humans
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensic" dan Humans</p>
Dataset Literature Review Digital Forensic AND Image Processing
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan "image processing" </p>
Dataset Literature Review Digital forensic AND Ghana
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensic" dan "Ghana"</p>
Dataset Literature Review Digital Forensic AND Reproducibility of Results
<p>data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensic" dan "Reproducibility of Results"</p>
Dataset Literature Review Digital Forensic AND Female
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan "Female"</p>
dataset literatur review digital forensic photogrammetry
<p>Bahwa data ini digunakan untuk membuat penelitian sesuai dengan literatur review dengan kata kunci "digital forensic AND photogrammetry"</p>
Dataset Literature Review Digital Forensic AND Digital Forensics
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "Digital "Forensic" dan "Digital Forensics"</p>
Dataset Literature Review Digital Forensic and Organization & Administration
<p>Bahwa data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci digital forensik dan Organization & Administration</p>
The use of Artificial Intelligence in Digital Forensics
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "Digital Forensik" dan "Artificial Intelligence"</p>
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.