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18,621 results for “development;”
Fig. 1 in Growth Processes In The Postembryonic Development In Altricial Birds On The Example Of Song Thrush, Turdus Philomelos (Passeriformers, Turdidae): A Multivariate Approach
Fig. 1. Distribution of centroids for the age-based song thrush samples in the factor space of PC1 and PC2 (figs 1–13 denote nestling's age in days).
Fig. 3 in Growth Processes In The Postembryonic Development In Altricial Birds On The Example Of Song Thrush, Turdus Philomelos (Passeriformers, Turdidae): A Multivariate Approach
Fig. 3. Results of comparison of 20 morphometric traits (1–20) in nestlings of song thrush according to their DRPI and dynamics of growth processes. Numbers of traits as given in table 1.
Fig. 1 in The Effect Of Temperature On The Development Of Adult Generations Of Entomopathogenic Nematode Steinernema Arenarium Isolate Ch
Fig. 1. Scatterplots of canonical scores for females of both generations. Legend: dots — 18 °С; squares — 22 °С; rhombs — 28 °С. Ranges of groups are ellipsed with coefficient 0.95.
Fig. 2 in The Effect Of Temperature On The Development Of Adult Generations Of Entomopathogenic Nematode Steinernema Arenarium Isolate Ch
Fig. 2. Scatterplots of canonical scores for males of both generations. Legend: dots — 18 °С; squares — 22 °С; rhombs — 28 °С. Ranges of groups are ellipsed with coefficient 0.95.
Development of a machine learning model to predict non- durable response to anti-TNF therapy in Crohn's disease using transcriptome imputed from genotypes
<p>This is the expression value predicted using PrediXcan version 7 to find a gene feature that can distinguish between patients with and without effect on infliximab.</p> <p>Among the various tissue models provided by PrediXcan v7, three models were selected and used: whole blood, Colon transverse, and terminal ileum of small intestine, and the predicted gene counts of each model were 6,294, 5,612 and 3,107.</p> <p>For each of the three models, predicted gene expression values and phenotype information per sample were submitted.</p>
Developing Notification and Enforcement Systems to Communicate and Administer Bridge Load Postings
<p>State and local law enforcement agencies in the US strive to enforce weight restrictions on trucks and heavy vehicles travelling on public roads as a matter of public safety and as a way of safeguarding vital transportation infrastructure. This research aimed to identify plausible notification systems that can effectively communicate bridge load postings to dispatchers and drivers, investigate and suggest possible approaches to communicate potential detour routes, and identify corresponding enforcement methods required to successfully administer bridge load postings. This report discusses the current practices of bridge load posting notification and enforcement systems. Also, it presents an overview of the existing conditions of intelligent Transportation systems (ITS) in Louisiana that included: Traffic Management Centers (TMCs), Motorist Assistance Patrol (MAP) service, ITS devices, and technologies. A national survey was also conduct targeting US Departments of Transportation (DOTs) professionals and law enforcement agencies in USA to obtain their feedback and insights regarding the current bridge load posting notification and enforcement procedures/systems, its limitations and required modifications at their States. The results of this research suggest that improving the notification methods alone is not enough to ensure public compliance with posted weight limit on bridges, therefore, the enforcement methods need to be improved as well to enhance drivers’ compliance and to prolong the lifespan of bridges. The report concluded by providing recommendations that can improve the notification and enforcement systems to effectively communicate and administer bridge load postings</p>
Enzymatic digestion method development for long-term stored chitinaceous planktonic samples - Data
<table> <tbody> <tr> <td>Data for Carrillo-Barragán, Priscilla, Heather Sugden, Catherine Scott, and Clare Fitzsimmons. 2022.<br> “Enzymatic Digestion Method Development for Long-Term Stored Chitinaceous Planktonic Samples.”<br> Marine Pollution Bulletin. </td> </tr> </tbody> </table>
Development and implementation of a tool for measuring the training effectiveness of the patient-centered consultation model
<p><strong>Background:</strong> The patient-centered consultation model comprises four elements: exploring health, illness, and disease experiences, understanding the whole person, finding common ground, and enhancing the patient-doctor relationship. This method is taught at the course in general practice at Copenhagen University. The aim of the study was to develop a simple tool consisting of a questionnaire about the patient-centered elements and a test video consultation. The outcome is the change in the students’ ability to identify these elements. Used as a pre-course and post-course test it can inform the teachers which elements of the patient-centered consultation need intensifying in the teaching.<br> <strong>Methods:</strong> The students from a course in general practice volunteered to participate in all steps of the development. They took part in individual interviews to select items from an already existing questionnaire (DanSCORE). The preliminary questionnaire was tested for face and content validity, pilot-tested and tested for test-retest reliability. All video consultations were transcribed and assessed for patient-centered elements through a conversation analysis. The videos showed medical students seeing real patients.<br> <strong>Results:</strong> The preliminary version of the questionnaire (called DanOBS) <a href="#_msocom_1">[CM1]</a> <a href="#_msocom_2">[MJ2]</a> had 23 items. In the subsequent interviews, items were reduced to 17, each with three response options. After the pilot test, the questionnaire was further reduced to 13 items, all strictly relevant to the model and with two response options. The final questionnaire had acceptable test-retest reliability. The number of test consultation videos underwent a reduction from six videos to one.<br> <strong>Conclusions:</strong> The DanOBS combined with a test video consultation, used as a pre-and post-course test demonstrates for teachers which elements in the patient-centered consultation need to be intensified in the teaching.</p> <p> </p> <p> </p>
DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 192 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>this version (3):</strong><br> * fixes issues with annotations from HB set</p> <p> </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>## Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 174<br> Total duration | 21 hours<br> Total classes | 47<br> Total events | 14229<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes | 11<br> Total events | 9026<br> Ratio event/duration | 0.04<br> Sampling rate | 24000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 5 hours<br> Total classes | 5<br> Total events | 611<br> Ratio event/duration | 0.05<br> Sampling rate | 6000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes | 1<br> Total events | 357<br> Ratio event/duration | 0.06<br> Sampling rate | 22050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes | 4<br> Total events | 1294<br> Ratio event/duration | 0.04<br> Sampling rate | 8000 Hz<br> -----------------------------------------------------<br> TRAINING SET/WMW<br> -----------------------------------------------------<br> Number of audio recordings | 161<br> Total duration | 4 hours and 40 mins<br> Total classes | 26<br> Total events | 2941<br> Ratio event/duration | 0.24<br> Sampling rate | various sampling rates<br> -----------------------------------------------------</p> <p>-----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 18<br> Total duration | 5 hours and 57 minutes<br> Total classes | 5<br> Total events | 1077<br> -----------------------------------------------------<br> VALIDATION SET/HB<br> -----------------------------------------------------<br> Number of audio recordings | 10<br> Total duration | 2 hours and 38 minutes<br> Total classes | 1<br> Total events | 712<br> Ratio event/duration | 0.7<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes | 2<br> Total events | 292<br> Ratio event/duration | 0.003<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/ME<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 20 minutes<br> Total classes | 2<br> Total events | 73<br> Ratio event/duration | 0.01<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2022_task5_training_set_classes.csv and DCASE2022_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set.</p> <p>DCASE2022_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2022_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1st of June 2022</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p>
Development of standardized definitions for Combined and Composite High Voltage Tests
<p>Driven by the increasing demands on traceable measurement systems and calibration services, being able to fully cover the required needs, a large number of NMIs have put a great effort on the development of standardized wave shapes definitions, low voltage calibrators, measuring instruments and software for combined and composite high voltage tests. The European Metrology Programme for Innovation and Research (EMPIR) supports this work through the normative Joint Research Project 19NRM07 HV-com², which started in 2020 and has received funding from the EMPIR co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program. </p>
Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation - source data
<p>Full data files for the paper:</p> <p>Duvillier, C., Eckert, N., Evin, G., and Deschâtres, M.: Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation, Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, 2023.</p> <p>Can be used to reproduce all the results of the paper and for further benchmarking of snow avalanche potential release area detection methods.</p>
(10)-Strobl2022A-DS0008 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0008 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0004 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0001 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0001 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0003 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0003 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0002 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0002 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0007 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0007 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0009 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0009 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0006 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0006 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0005 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
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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.