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67 results for “Life Sciences”
International E- Conference on "Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)
<p><strong>International E- Conference</strong> on “Recent Trends in Chemical Science, Physical Science, Life Science and Computer Technology (ICRTCPLCT–2022)” By Anjuman Islam Janjira Degree College of Science, Murud on <strong>29<sup>th</sup> March 2022.</strong></p>
Life Sciences in the Fast Lane: Dan Qunitana on Rapid Feedback, Tweeting, and Time Management
<p><strong>Episode Summary:</strong></p> <p>In the last episode of 2019 we talk to Professor Dan Quintana from the University of Oslo about the advantages of sharing preprints and ideas online, fears about getting scooped, and lessons he has learnt about Twitter and time management. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://www.dsquintana.com/publication/">Dan Quintana</a></p> <ul> <li><a href="https://twitter.com/dsquintana">Dan Quintana Twitter</a></li> </ul> <p><a href="https://twitter.com/hertzpodcast">Everything Hertz Podcast</a></p> <p><a href="https://twitter.com/pb_cast">Physiology & Behavior Podcast</a></p> <p><strong>Episode Quotes: </strong></p> <p>"The great thing about social media is it is a great way to organise a lot of people who are like-minded"</p>
The motivation for citizens' involvement in life sciences research is predicted by age and gender
<p>Data for publication <em><strong>The motivation for citizens’ involvement in life sciences research is predicted by age and gender</strong></em></p>
Metadata standards and tools practice at EPFL School of Life Sciences 2020 Survey
<p>In 2020, EPFL Library conducted a study about Tools and Metadata Standards practice in EPFL School of Life Sciences.</p> <p>By standard, we mean:<br> - terminological resources (vocabularies, terminologies, classifications, thesauri),<br> - formats and data models / schemas,<br> - structured knowledge bases (databases, reference databases, ontologies).<br> And by tools, we mean:<br> - bioinformatics software (i.e. for sequence or molecular structure analysis of proteins and genes)<br> - databases from the Life Sciences field (i.e. genome databases).</p> <p>Our goal was twofold: on the one hand, to gain new knowledge and insights, and on the other hand, to develop a reproducible survey methodology resolutely based on liaison librarian-data librarian collaboration.</p> <p>This dataset reflects the results collected during the second phase of the study: "Survey in EPFL Life Sciences Community".</p> <p> </p>
Process Not Product: How the Open Life Science Mentoring Program Creates Open Science Ambassadors
<p><strong>Episode Summary</strong></p> <p>In this episode we talk to Yo Yehudi and Malvika Sharan, two of the co-founders and organisers of the Open Life Science training and mentoring program. We discuss why mentorship and community are so important in encouraging open science, what makes the program unique, and what the future for Open Life Science. </p> <p><strong>Episode Links</strong></p> <ul> <li><a href="https://openlifesci.org/">Open Life Science</a></li> <li><a href="https://about.me/malvikasharan">Malvika Sharan</a> <ul> <li><a href="https://twitter.com/MalvikaSharan">Twitter</a></li> </ul> </li> <li><a href="https://yo-yehudi.com/">Yo Yehudi</a> <ul> <li><a href="https://twitter.com/yoyehudi">Twitter</a></li> </ul> </li> </ul>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki. in Deep learning brings speed, accuracy to the life sciences.
Kaisa Helminen is CEO of Fimmic Oy, a Finnish company that created the first commercial tool integrating deep learning and computer vision for pathology research. Photograph: Sebastian Mardones / Health Capital Helsinki.
Researchers trained a in Deep learning brings speed, accuracy to the life sciences.
Researchers trained a neural network with random plant samples, shown here, from a large French herbaria data set. Next, the network was trained on a much smaller Costa Rican database. The French and Costa Rican species are different, but the model identified Costa Rican species with 80 percent accuracy. This training process, called transfer learning, can help taxonomists identify plants in countries that have small numbers of plant images in digital collections. Photograph: Reprinted from Carranza-Rojas J et al. 2017, Going deeper in the automated identification of herbarium specimens. BMC Evolutionary Biology.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Extended data for Manuscript: Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis
<p>This is the extended data for the manuscript submitted to F1000 Research titled:</p> <p><strong>Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis</strong></p> <p>The data provided includes redacted attendee data from a workshop, survey responses from an anonymous survey of participants, and a figure describing the overall workshop directory flow. </p>
Life Sciences dataset used in INFORE project, part 1
<p>Life Sciences dataset used in INFORE project, part 1</p> <p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different examples of parameters combinations of our use case that correspond to the different panels of Figure 4 in Documentation folder. This figure comes from the paper in the same folder.<br> You can find a broad discussion of our use case in the Biological Use Case Documentation file. Also,<br> The results of the cell simulations can be found in example_XXX/run0/outputs. The results of the microenvironment simulations can be found in example_XXX/run0/microutputs.<br> Details on how these files are built can be found in Biological Use Case output format file (which is a snippet of the broad documentation file that I detached for your convenience). Briefly: each time step defined, the software writes an output and microutput file. For instance, ecm_t00030.txt correspond to time step 30. Each line of these files corresponds to a cell or microenvironment entity (oxygen, TNF, etc). Columns are defined by the first row for output folder. For the microutputs, the first three columns correspond to spatial coordinates and the fourth to the value of the density.</p> <p>The examples are:<br> - example_spheroid_TNF_nopulse: corresponds to Figure 4 A.<br> - example_spheroid_TNF_onepulse: corresponds to Figure 4 C.<br> - example_spheroid_TNF_pulse150: corresponds to Figure 4 D left. This is the simulation outcome desired: proliferative cells die out with increasing number of pulses of TNF.<br> - example_spheroid_TNF_pulse600: corresponds to Figure 4 D right.<br> - example_spheroid_TNF_pulsecont: corresponds to Figure 4 B.<br> - example_cells_with_ECM_mutants: does NOT correspond to Figure 4. This is an example in which microutput folder is full of two entities: oxygen and ECM. Also, in this example you can find a folder (ECM_mut) with the kind of visualisation that we perform to showcase results.<br> - example_spheroid_TNF_pulsecont_oxy: 21 simulations with slightly different oxygen tolerance conditions using as a base the simulation with one continuous pulse (Figure 4 B from the presentation).<br> The only difference among parameters file is the "oxygen_necrotic" value, which controls the threshold above which cells commit to necrosis due to lack of oxygen. In the original simulation this value was zero and the maximum available oxygen is 40 fg/µm^3. Here, we have studied the parameter value from 0 to 40 in steps of 5.</p>
Life Sciences dataset used in INFORE project, part 2
<p>Life Sciences dataset used in INFORE project, part 2</p> <p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different examples of parameters combinations of our use case that correspond to the different panels of Figure 4 in Documentation folder. This figure comes from the paper in the same folder.<br> You can find a broad discussion of our use case in the Biological Use Case Documentation file. Also,<br> The results of the cell simulations can be found in example_XXX/run0/outputs. The results of the microenvironment simulations can be found in example_XXX/run0/microutputs.<br> Details on how these files are built can be found in Biological Use Case output format file (which is a snippet of the broad documentation file that I detached for your convenience). Briefly: each time step defined, the software writes an output and microutput file. For instance, ecm_t00030.txt correspond to time step 30. Each line of these files corresponds to a cell or microenvironment entity (oxygen, TNF, etc). Columns are defined by the first row for output folder. For the microutputs, the first three columns correspond to spatial coordinates and the fourth to the value of the density.</p> <p>The examples are:<br> - example_spheroid_TNF_nopulse: corresponds to Figure 4 A.<br> - example_spheroid_TNF_onepulse: corresponds to Figure 4 C.<br> - example_spheroid_TNF_pulse150: corresponds to Figure 4 D left. This is the simulation outcome desired: proliferative cells die out with increasing number of pulses of TNF.<br> - example_spheroid_TNF_pulse600: corresponds to Figure 4 D right.<br> - example_spheroid_TNF_pulsecont: corresponds to Figure 4 B.<br> - example_cells_with_ECM_mutants: does NOT correspond to Figure 4. This is an example in which microutput folder is full of two entities: oxygen and ECM. Also, in this example you can find a folder (ECM_mut) with the kind of visualisation that we perform to showcase results.<br> - example_spheroid_TNF_pulsecont_oxy: 21 simulations with slightly different oxygen tolerance conditions using as a base the simulation with one continuous pulse (Figure 4 B from the presentation).<br> The only difference among parameters file is the "oxygen_necrotic" value, which controls the threshold above which cells commit to necrosis due to lack of oxygen. In the original simulation this value was zero and the maximum available oxygen is 40 fg/µm^3. Here, we have studied the parameter value from 0 to 40 in steps of 5.</p>
Data from: Prolific observer bias in the life sciences: why we need blind data recording
Observer bias and other "experimenter effects" occur when researchers' expectations influence study outcome. These biases are strongest when researchers expect a particular result, are measuring subjective variables, and have an incentive to produce data that confirm predictions. To minimize bias, it is good practice to work "blind," meaning that experimenters are unaware of the identity or treatment group of their subjects while conducting research. Here, using text mining and a literature review, we find evidence that blind protocols are uncommon in the life sciences and that nonblind studies tend to report higher effect sizes and more significant p-values. We discuss methods to minimize bias and urge researchers, editors, and peer reviewers to keep blind protocols in mind.
Dataset: Anavex Life Sciences Corp. (AVXL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 3 in Equity and career-life balance in marine mammal science?
Figure 3. Survey responses from women (yellow) and men (green) in the Society of Marine Mammalogy (including students) showing reactions to the listed statements (from 1 = strongly disagree to 5 = strongly agree). Box plots are shown for median (heaviest color shading and stronger line) and interquartile range with whiskers for minimum and maximum values.
Linked collectors and determiners for: Brigham Young University Life Science Museum (BYU) Mammal Collection (Arctos).
Natural history specimen data linked to collectors and determiners held within, "Brigham Young University Life Science Museum (BYU) Mammal Collection (Arctos)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1da75725-1eb5-4e17-b586-ffad7b783987">https://bionomia.net/dataset/1da75725-1eb5-4e17-b586-ffad7b783987</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1da75725-1eb5-4e17-b586-ffad7b783987">https://gbif.org/dataset/1da75725-1eb5-4e17-b586-ffad7b783987</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Vascular Plant Herbarium.
Natural history specimen data linked to collectors and determiners held within, "Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Vascular Plant Herbarium". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de">https://bionomia.net/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de">https://gbif.org/dataset/6b5e4c2d-127f-492e-bf1c-2026d515e0de</a>. Formatted as a Frictionless Data package.
Figure 3 from: Thessen A, Patterson D (2011) Data issues in the life sciences. ZooKeys 150: 15-51. https://doi.org/10.3897/zookeys.150.1766
Figure 3 - Technical infrastructure needed for Big New Biology to fully emerge (based on Sinha et al. 2010).
Figure 2 from: Thessen A, Patterson D (2011) Data issues in the life sciences. ZooKeys 150: 15-51. https://doi.org/10.3897/zookeys.150.1766
Figure 2 - A Big New Biology can only emerge with a framework that optimizes reuse. Ideally, data should be in forms that can flow from source into a common pool and can flow back out to consumers, be subject to quality control, or be enhanced through analysis to rejoin the pool as processed data.
Figure 1 from: Thessen A, Patterson D (2011) Data issues in the life sciences. ZooKeys 150: 15-51. https://doi.org/10.3897/zookeys.150.1766
Figure 1 - Rogers adoption curve describes the acceptance of a new technology. Life Sciences is still in the Early Adopters phase for accepting principles of data readiness.
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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.