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2,863 results for “Challenge”
Data from: Identifying and overcoming breastfeeding challenges using two-way short message service, a randomized controlled trial
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Data from: Physician champions’ perspectives and practices on electronic health records implementation: challenges and strategies
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Data from: Joint histology in Alligator mississippiensis challenges the identification of synovial joints in fossil archosaurs and inferences of cranial kinesis
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Expert-based assessment of rewilding indicates progress at site-level, yet challenges for upscaling
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Empirical and methodological challenges to the model-based inference of diversification rates in extinct clades
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Data from: Migrating songbirds on stopover prepare for, and recover from, oxidative challenges posed by long-distance flight
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Data from: Revaccination of cattle with Bacille Calmette-Guérin two years after first vaccination when immunity has waned, boosted protection against challenge with Mycobacterium bovis
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Data from: Taxonomic delimitation of species complexes: a challenge for conservation, first steps with Abarema cochliacarpos complex
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Telomere length correlates with physiological and behavioural responses of a long-lived seabird to an ecologically-relevant challenge
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Temperature-mediated plasticity in incubation schedules is unlikely to evolve to buffer embryos from climatic challenges in a seasonal songbird
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Supplement 1: Challenges Faced by Neurology Trainees in a Neuro-Intervention Career Track
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Tools for assessing hydrologic challenges and restoration potential of reservoir deltas: case studies from Southern California
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Raw ITC and NOESY data for the SAMPL7 Cyclodextrin Derivatives Challenge
<p>This provides the raw ITC and NOESY datasets associated with the SAMPL7 modified cyclodextrin binding challenge (focusing on host-guest binding), as described at: <a href="https://github.com/samplchallenges/SAMPL7/tree/master/host_guest/cyclodextrin_derivatives">https://github.com/samplchallenges/SAMPL7/tree/master/host_guest/cyclodextrin_derivatives</a> and links therein. </p>
FIGURE 5 in Three challenges to contemporaneous taxonomy from a licheno-mycological perspective
FIGURE 5. Proportional distribution of major topics among 3,403 publications on Latin American lichens from 1750 to present (analysed from the Recent Literature on Lichens database, RLL: http://nhm2.uio.no/botanisk/lav/RLL/RLL. HTM). Only 7% of these are dealing with potential revenue generation; yet, most of the other 93% are burdened with the same obligations regarding the Nagoya Protocol, as long as they include molecular phylogenetic or chemotaxonomic approaches.
FIGURE 3 in Three challenges to contemporaneous taxonomy from a licheno-mycological perspective
FIGURE 3. Global distribution of the 200 most prolific fungal taxonomists, responsable for nearly half of all approximately 360,000 species names described (Index Fungorum 2020; cut-off point: December 2017). Orange: historic and recent authors that have passed away (75%); violet: authors still alive and active (25%). The area size of the circles is proportional to the species named by these authors. Note the overall heavily unbalanced distribution of both described species and authors towards Europe and North America but the more balanced situation when only taking into account active taxonomists. For detailed information, see Table 1). The base map was modified from Wikipedia [https://upload.wikimedia.org/wikipedia/commons/e/e8/World_blank_ map_countries.PNG].
FIGURE 2 in Three challenges to contemporaneous taxonomy from a licheno-mycological perspective
FIGURE 2. Historic, current and future proposed accumulation of catalogued fungal species assuming a much increase annual rate by a dedicated workforce of 300 full-time taxonomists, envisioning a completion of an additional 3 million species by 2060.
FIGURE 1 in Three challenges to contemporaneous taxonomy from a licheno-mycological perspective
FIGURE 1. Estimated time to complete the inventory of fungal species, given a current maximum rate of 2,000 new species per year and based on different global richness predictions: (1) May (2000); (2) Mora et al. (2011); (3) Schmit & Mueller (2006); (4) Hammond (1992); (5) Rossman (1994); (6) Mora et al. 2011 (adjusted according to Hawksworth & Lücking 2018); (7) Hawksworth (1991, 2001); (8) Pascoe (1990); (9) Hawksworth (2012) and Hawksworth & Lücking (2018); (10) Hawksworth & Rossman (1997) (in Hawksworth 2001); (11) O'Brien et al. (2005); (12) Cannon (1997); (13) Wu et al. (2019).
LifeCLEF 2020 Plant Identification Challenge
<p>Despite recent progress in automated plant identification, a vast majority of the 300K+ plant species on earth can still not be recognized easily because of the lack of training data for that species. On the other side, for several centuries, botanists have collected, catalogued and systematically stored plant specimens in herbaria. These physical specimens are used to study the variability of species, their phylogenetic relationship, their evolution, or phenological trends. Millions of such specimens are now digitized and publicly available. Using them for training deep learning models is thus a very promising approach to help identifying data deficient species. However, their visual appearance is very different from field pictures which makes it a challenging cross-domain classification task.</p> <p>The goal of the challenge is to identify plants in field pictures based on a training set of digitized herbarium specimens. Concretely, this will consist in a cross-domain classification task with a training set composed of digitized herbarium sheets and a test set composed of field pictures. To enable learning a mapping between the herbarium sheets domain and the field pictures domain, we will provide both herbarium sheets and field pictures for a subset of species.</p> <p>This dataset is used in the context of LifeCLEF Plant Identification:</p> <p><a href="https://www.aicrowd.com/challenges/lifeclef-2020-plant">https://www.aicrowd.com/challenges/lifeclef-2020-plant</a></p> <p> </p>
MERLIN H2020 Project: Video of the Marshmallow challenge time lapse at SAAEI 2018 Conference
<p>Video of the Marshmallow challenge time lapse at SAAEI Conference, July 2018</p>
The SAMPL7 Host-Guest Challenge Virtual Workshop
<p>This SAMPL7 virtual workshop goes over preliminary results and analysis of the SAMPL7 host-guest challenge (https://github.com/samplchallenges/sampl7). The authors list provided here is the list of presenters; if you would like to cite one of the individual presentations given here, please contact the presenter for a full authors list. Here's the speakers list and schedule we followed:</p> <p> </p> <ul> <li> <p>8-8:30: Martin Amezcua/David Mobley: Overview of results and analysis</p> </li> <li> <p>8:30-8:55: Jay Ponder, Washington University in St. Louis, “AMOEBA Force Field Results for the SAMPL7 Host-Guest Challenge”</p> </li> <li> <p>8:55-9:10: Yuriy Khalak, Max Planck Institute for Biophysical Chemistry (Göttingen), “Non-Equilibrium Absolute Free Energy Calculations for Cyclodextrin Host-Guest Systems”</p> </li> <li> <p>9:10-9:35: Yigitkan Eken, Michigan State University, ”Accuracy of MM/PBSA and MM/GBSA methods on SAMPL7 Host-Guest Binding Prediction”</p> </li> <li> <p>9:35-9:55: Nuno Almeida, Michigan State University, “Binding energy calculations of SAMPL7 host-guest systems using density functional theory and a range of basis sets”</p> </li> <li> <p>10:00-10:20: Lyle Isaacs, Maryland, “SAMPL7-TrimerTrip Overview”/Q&A</p> </li> <li> <p>10:20-10:40: Bruce Gibb, Tulane, OctaAcid highlights/Q&A</p> </li> <li> <p>10:40-10:55: Katy Kellett, formerly UCSD, CD derivatives highlights/Q&A</p> </li> <li> <p>10:55-11:10: Dylan Serillon, University of Barcelona, “SAMPL7, an overview of data based and xtb-GNF2B semi-empirical approach to predict free binding energy in host-guest complexes”</p> </li> <li> <p>11:10-11:40 open discussion/follow-ups/lessons learned</p> </li> <li> <p>11:40-11:45: Set timeframe for paper submissions</p> </li> </ul>
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.