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2,129 results for “scores”
Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation
<p><strong>Background</strong>: Prostate-related quality of life can be assessed with a variety of different questionnaires. The 50-item Expanded Prostate Cancer Index Composite (EPIC) and the International Prostate Symptom Score (IPSS) are two widely used options. The goal of this study was, therefore, to develop and validate a model that is able to convert between the EPIC and the IPSS to enable comparisons across different studies. </p> <p><strong>Methods</strong>: Three hundred forty-seven consecutive patients who had previously received radiotherapy and surgery for prostate cancer at two institutions in Switzerland and Germany were contacted via mail and instructed to complete both questionnaires. The Swiss cohort was used to train and internally validate different machine learning models using fourfold cross-validation. The German cohort was used for external validation.</p> <p><strong>Results</strong>: Converting between the EPIC Urinary Irritative/Obstructive subscale and the IPSS using linear regressions resulted in mean absolute errors (MAEs) of 3.88 and 6.12, which is below the respective previously published minimal important differences (MIDs) of 5.2 and 10 points. Converting between the EPIC Urinary Summary and the IPSS was less accurate with MAEs of 5.13 and 10.45, similar to the MIDs. More complex model architectures did not result in improved performance in this study. The study was limited to the German versions of the respective questionnaires.</p> <p><strong>Conclusions</strong>: Linear regressions can be used to convert between the IPSS and the EPIC Urinary subscales. While the equations obtained in this study can be used to compare results across clinical trials, they should not be used to inform clinical decision-making in individual patients. Trial registration This study was retrospectively registered on clinicaltrials.gov on January 14th, 2022, under the registration number NCT05192876.</p>
Рис. 3. Оценка окраски меΛанином первостепенных маховых P6–P10 чайки КумΛиена (баΛΛы по критерию ИнгоΛфссона) Fig. 3. Primary Pattern Score of Kumlien's gull assessed using the Ingolfsson criteria (1970) in The first documented record of the Kumlien's gull Larus glaucoides kumlieni Brewster, 1883 in Russia
Рис. 3. Оценка окраски меΛанином первостепенных маховых P6–P10 чайки КумΛиена (баΛΛы по критерию ИнгоΛфссона) Fig. 3. Primary Pattern Score of Kumlien's gull assessed using the Ingolfsson criteria (1970)
Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period<br> of storage (days) for which the processed cheese has been in the shelf can be determined based on<br> sensory score (Fig. 5).</p>
Figure 4.1. Distribution of scores for the Nelson test-The Effects of CALL on Vocabulary Learning: A Case of Iranian Intermediate EFL Learners
<p>In the past, vocabulary teaching and learning were often given little priority in second<br> language programs but recently there has been a renewed interest in the nature of vocabulary and its<br> role in learning and teaching. Although most teachers might be aware of the importance of<br> technology, say, computer, rarely teachers use it for teaching vocabulary. Thus, the current study<br> aims at exploring the effects of CALL on vocabulary learning of Iranian EFL Learners. In this<br> study, 40 intermediate EFL learners, both male and female aged from 16 to 18 studying New<br> Interchange, book III, were chosen randomly from a language institute in Tehran. They were divided<br> into two twenty-member groups. The experimental group was given the VTS.S (a computer<br> program for teaching vocabularies), a computerized dictionary and provided with teacher efeedback.<br> The control group received no special software and vocabularies were taught using the<br> conventional ways with the help of a paper dictionary. A vocabulary pre-test based on the tests<br> available in their teacher's guide was given to both groups. The aim of this test was to make sure<br> that the students were not familiar with the words in advance. By pre-test/post-test comparison<br> researchers found learners exposed to VTS.S teacher e-feedback plus the computerized dictionary<br> scored higher than the control group. Both high-stake and low-stake holders can avail from the<br> findings of the study.</p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 5. Hierarchical clustering by scores across the EPQ–R scales for data about all the participants
<p>The clusters were generated using an implementation of a hierarchical clustering algorithm available in the R environment (R, n.d.). The top three clusters were extracted from a hierarchical cluster tree shown in Figure 5, while the color of data points in the visualization shown in figure 4 was determined based on cluster labels. Hierarchical clusters could be used when investigating which students in the analyzed sample share similar personality traits. This could be especially useful for smaller student groups as the teacher may manually inspect the cluster tree and its leaves, which designate individual students. For instance, there are three students in cluster 3, who are represented within the tree in Figure 5 by identifiers 14, 22, and 24. The students with identifiers 14 and 22 are more closely linked and more similar to each other than to the student with identifier 24. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 2. Comparison of mean scores on the EPQ–R scales
<p>The data for the workshop participants were loaded from the data warehouse, while the summary data from the original EPQ–R study were loaded from a CSV file. The bar chart featured in Figure 2 shows mean scores on the EPQ–R scales for the selected workshop participants (denoted by blue bars) and the selected participants of the original EPQ–R study (denoted by yellow bars). The mean scores on the P scale agree between the two samples, but the overall scores for the other scales vary. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 3. Radial visualization of scores across the EPQ–R scales for the male and female participants
<p>The radial visualization in Figure 3 depicts each participating student as a dot whose color indicates the gender of the student, blue for male students (M) and red for female students (F). The position of a dot in the visualization is determined by the scores of the associated student on the four EPQ–R scales. The radial overview may provide a much clearer outline of clustering within the analyzed group. Although there are only five female students, they are concentrated in a relatively narrow area within the radial coordinate system</p>
Turkish Makam Music Audio-Score Alignment Dataset
<p>This release contains the annotations and the scores to test the audio-score alignment methodology explained in:</p> <blockquote> <p><em>Şentürk, S., Gulati, S., and Serra, X. (2014). <strong>Towards alignment of score and audio recordings of Ottoman-Turkish makam music.</strong> In Proceedings of 4th International Workshop on Folk Music Analysis, pages 57–60, Istanbul, Turkey.</em></p> </blockquote> <p>The dataset in this release is derived from the transcription test dataset used in the paper:</p> <blockquote> <p><em>Benetos, E. & Holzapfel, A. (2013). <strong>Automatic transcription of Turkish makam music.</strong> In Proceedings of 14th International Society for Music Information Retrieval Conference, 4 - 8 Nov 2013, Curitiba, PR, Brazil.</em></p> </blockquote> <p>The scores for each composition are obtained from the SymbTr collection explained in:</p> <blockquote> <p><em>Karaosmanoğlu, K. (2012). <strong>A Turkish makam music symbolic database for music information retrieval: SymbTr.</strong> In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), pages 223–228.</em></p> </blockquote> <p>From the annotated score onsets for some of the above recordings only the main singing voice segments have been selected. Further separately only a subset of vocal onsets crresponding to phoneme transitions rules have been explicitly annotated as annotationOnsets.txt</p> <blockquote> <p><a href="http://mtg.upf.edu/biblio/author/810">Dzhambazov, G.</a>, <a href="http://mtg.upf.edu/biblio/author/644">Srinivasamurthy A.</a>, <a href="http://mtg.upf.edu/biblio/author/494">Şentürk S.</a>, & <a href="http://mtg.upf.edu/biblio/author/1012">Serra X.</a> (2016). <a href="http://mtg.upf.edu/node/3492">On the Use of Note Onsets for Improved Lyrics-to-audio Alignment in Turkish Makam Music</a>. 17th International Society for Music Information Retrieval Conference (ISMIR 2016</p> </blockquote> <p><strong>Using this dataset</strong></p> <p>Please cite the above publications if you use this dataset in a publication.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="http://compmusic.upf.edu/node/233">http://compmusic.upf.edu/node/233 </a></p>
Dataset Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study
<p>This dataset is related to "Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study".</p> <p>Vanessa Kraege*, Janko Fabecic*, Pedro Marques Vidal, Gérard Waeber and Marie Méan</p> <p>*Contributed equally; co-first authors</p>
Text-fig. 6b. Z-score profile for Moča skull. Comparison of Moča skull indices with LUP and recent sample, –1.96 and +1.96: 95% tolerance interval (95% of the LUP and recent variability), 0: LUP and recent sample mean, grey band: 95% confidence interval of mean z-scores. Only those samples having more than n = 5 in the particular group are included. in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 6b. Z-score profile for Moča skull. Comparison of Moča skull indices with LUP and recent sample, –1.96 and +1.96: 95% tolerance interval (95% of the LUP and recent variability), 0: LUP and recent sample mean, grey band: 95% confidence interval of mean z-scores. Only those samples having more than n = 5 in the particular group are included.
Text-fig. 6a. Z-score profile for Moča skull. Comparison of Moča skull measurements with LUP and recent sample, –1.96 and +1.96: 95% tolerance interval (95% of the LUP and recent variability), 0: LUP and recent sample mean, gray band: 95% confidence interval of mean z-scores. Only those samples having more than n = 5 in the particular group are included. in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 6a. Z-score profile for Moča skull. Comparison of Moča skull measurements with LUP and recent sample, –1.96 and +1.96: 95% tolerance interval (95% of the LUP and recent variability), 0: LUP and recent sample mean, gray band: 95% confidence interval of mean z-scores. Only those samples having more than n = 5 in the particular group are included.
Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry
<p>These are a collection of EWAS summary statistics for the following publication:</p> <p>Deng Wei Q, Cawte Nathan, Campbell Natalie, Azab Sandi M, de Souza Russell J, Lamri Amel, Morrison Katherine M, Atkinson Stephanie A, Subbarao Padmaja, Turvey Stuart E, Moraes Theo J, Teo Koon K, Mandhane Piush, Azad Meghan B, Simons Elinor, Pare Guillaume, Anand Sonia S (2024) Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry eLife 13:RP93260, https://doi.org/10.7554/eLife.93260.3</p> <p>1. CHILD_450K_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in CHILD (HM450K array).</p> <p>2. CHILD_450K_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in CHILD (HM450K array).</p> <p>3. CHILD_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in CHILD (HM450K array).</p> <p>4. FAMILY_EPIC_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in FAMILY (customized EPIC array).</p> <p>5. FAMILY_EPIC_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in FAMILY (customized EPIC array).</p> <p>6. FAMILY_EPIC_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in FAMILY (customized EPIC array).</p> <p>7. START_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in START (HM450K array).</p> <p>8. mateversmk_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using ever definition.</p> <p>9. matsmoke_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using current smoking definition.</p> <p>10. mblsmkexp_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking exposure (hours per week).</p>
EWAS associations between DNAm and general cognitive score, perceptive performance score, and verbal score
<p><strong><span>Evaluating the association between placenta DNA methylation and cognitive functions in the offspring </span></strong></p> <p><a name="_Hlk141095040"></a><span>Placenta plays a crucial role protecting the foetus from environmental harm and supports the development of its brain. In fact, compromised placental function could predispose an individual to neurodevelopmental disorders</span><span><span>. Placental epigenetic modifications, including DNA methylation, could be considered a proxy of placental function and thus plausible mediators of the association between intrauterine environmental exposures and genetics, and childhood and adult mental health. Although neurodevelopmental disorders such as autism spectrum disorder have been investigated in relation to placenta DNA methylation, no studies have addressed the association between placenta DNA methylation and child’s cognitive functions. </span><span>Thus, our goal here was to investigate whether placental DNA methylation profile measured using the Illumina EPIC array is associated with three different cognitive domains (namely verbal score, perceptive performance score, and general cognitive score) assessed by the McCarthy Scales of Children’s functions in childhood at age 4. To this end, we conducted epigenome-wide association analyses including data from 255 mother-child pairs within the INMA project and performed a follow-up functional analysis to help the interpretation of the findings. After multiple-testing correction, we found that methylation at 4 CpGs (cg1548200, cg02986379, cg00866476 and cg14113931) was significantly associated with the general cognitive score, and 2 distinct differentially methylated regions (DMRs) (including 27 CpGs) were significantly associated with each cognitive dimension. <a name="_Hlk168395284"></a><a name="_Hlk168394861"></a><span>Interestingly, the genes annotated to these CpGs, <span>such as <em>DAB2, CEP76</em>, <em>PSMG2,</em> or <em>MECOM,</em> </span>are involved in placenta, foetal, and brain development</span><span>. </span>Moreover, functional enrichment analyses of suggestive CpGs (<em>p</em><1x10<sup>-4</sup>) revealed gene-sets involved in placenta development, foetus formation and brain growth. These findings suggest that placental DNAm could be a mechanism contributing to the alteration of important pathways in the placenta that have a consequence on the offspring’s brain development and cognitive function. </span></span></p>
Hexapeptide propensity scores and associated metadata
<p>Amyloid propensity scores for all possible hexapeptide sequences and associated metadata</p>
Visualization of the numerical pose optimization with the JAMDA scoring function using the BFGS and the LSL-BFGS algorithm
<p>These videos demonstrate the behavior of two different optimization algorithms (BFGS and LSL-BFGS) during pose optimization using the JAMDA protein-ligand scoring function.</p> <p>Flachsenberg et al. (2020) (<a href="http://doi.org/10.1021/acs.jcim.0c01095" target="_blank" rel="noopener">10.1021/acs.jcim.0c01095</a>) describes the JAMDA protein-ligand scoring function and the LSL-BFGS algorithm.<br>The data for these videos stems from Experiment 5 in Flachsenberg et al. (2020). In this experiment, the crystal structure of a ligand was numerically optimized in the binding site with respect to the JAMDA scoring function to create the JAMDA-minimized crystal structure. The JAMDA-minimized crystal structure was randomly deflected to generate various starting poses for the numerical optimization.</p> <p>These videos demonstrate the behavior of two optimization algorithms (BFGS and LSL-BFGS) when optimizing one of the generated starting poses. The chosen example for the videos is a structure of ribonuclease A with a 5'-deoxy-5'-N-piperidinouridine inhibitor (PDB code 3d6q, <a href="https://doi.org/10.2210/pdb3D6Q/pdb" target="_blank" rel="noopener">10.2210/pdb3D6Q/pdb</a>, <a href="https://doi.org/10.1021/jm800724t" target="_blank" rel="noopener">10.1021/jm800724t</a>). Each of the videos shows all the intermediate steps the optimization algorithm takes until convergence.</p> <p>The main observation (that is discussed in detail in Flachsenberg et al. (2020)) is that the BFGS algorithm tends to take inappropriately large steps when clashes are present in the structure, resulting in unwanted binding mode changes. This is <em>not</em> the case for the LSL-BFGS algorithm.</p> <h3>Legend</h3> <p>For each iteration, the JAMDA score value, the RMSD to the JAMDA-minimized crystal structure (yellow), and the RMSD to the optimization's starting point (blue) are given. Furthermore, the gradient's norm (representing the main convergence criterion) is shown. In addition to the optimized ligand, also the JAMDA-minimized crystal structure (yellow) and the optimization's starting structure (blue) are shown.</p> <p><br>Each optimization algorithm is shown in two videos: In one video, the optimized ligand is colored by elements. Here, atoms with clashes (positive JAMDA scores) are marked with orange balls. In the other video, the atoms and bonds of the optimized ligand are colored by their individual JAMDA score.</p> <h3>Used Software</h3> <p>The snapshots of the optimization algorithms were rendered using PyMOL 3.0 (<a href="https://pymol.org/" target="_blank" rel="noopener">https://pymol.org/</a>) and further processed using the Pillow 10.4 Python library (<a href="https://doi.org/10.5281/zenodo.12606429" target="_blank" rel="noopener">10.5281/zenodo.12606429</a>). Videos were created from the individual snapshots using FFmpeg 7.0 (<a href="https://www.ffmpeg.org/" target="_blank" rel="noopener">https://www.ffmpeg.org/</a>).</p>
Baseline and Future (2050s and 2090s) Climate Suitability Scores for 116 Useful Tree Species and 220 locations from Côte d'Ivoire, Ghana and Guinea
<p>Climate suitability scores were calculated for 116 Useful Tree Species identified by filtering Top830+ native tree species from Côte d'Ivoire, Ghana and Guinea via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database.</p> <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables. For some variables, the planting site occurs outside the 25% - 75% species's range.</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables. For some variables, the planting site occurs outside the 5% - 95% species's range.</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p>Locations corresponded to cities and weather stations from the three target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> and <a href="https://doi.org/10.5281/zenodo.12679832">ClimateForecasts</a> databases, respectively. Both these databases provide bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s.</p> <p>Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis:</p> <ul> <li>One set of bioclimatic variables included BIO01 (mean annual temperature), BIO12 (total annual precipitation), climaticMoistureIndex, monthCountByTemp10 (number of months with average temperature above 10 degrees), growingDegDays5, BIO05 (maximum temperature of the warmest month), BIO06 (minimum temperature of teh coldest month), BIO16 (precipitation of the wettest quarter), BIO17 (precipitation of the driest quarter) and MCWD (Maximum Climatological Water Deficit). These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (mean annual temperature), which is the single bioclimatic variables available for the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> <p>Calculations were made with similar scripting pipelines in the <em>R</em> statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.</p> <p>The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 18 species not documented by the TreeGOER.</p> <p> </p> <p><strong>References</strong></p> <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. <em>et al.</em> (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created within the context of an agreement between The International Centre for Research in Agroforestry (ICRAF) and WORLD UNIVERSITY SERVICE OF CANADA (WUSC) for a <em><a href="https://ceci.org/en/projects/nature-based-climate-adaptation-guinean-forest-west-africa-sbn-guinean-forests">Nature-based climate adaptation project in the Guinean forests of West Africa (NbS Guinean Forests)</a></em> funded by <a href="https://www.international.gc.ca/global-affairs-affaires-mondiales/home-accueil.aspx?lang=eng">Global Affairs Canada</a>.</p>
Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper
<p>Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper</p>
Scoring of wing wear of peacock butterflies in belgium 2009-2020. Link to original pictures and classification
<p>We show per record the assigned classification of the wear. We classified the condition of wear of the upperwings in 12,425 butterflies according to four categories: (1) immaculate: fresh, or with at most one tiny scratch on the upper wings or one minor dent to the wing edges, (2) slightly worn: some scratches on the upper wing and/or small dents to the edges, (3) moderately worn: many scratches to the upper wing and/or dented edges, and (4) heavily worn: (parts with) colors faded and/or heavily dented edges. The categories were chosen to allow the distinction of recently emerged individuals in particular: there is “more wear” between categories 3 and 4 than between the first two. Persistent wear and abrasions with multiple impacts at several spots was given more weight in the classification than a single major incident that resulted in larger parts of the wing missing. Pictures were randomly sorted before being classified by Jos Van Kerckhoven, who worked from a series of reference examples.</p>
Double-tagging scores of seabirds reveals that light-level geolocator accuracy is limited by species idiosyncrasies and equatorial solar profiles
<p>Light-level geolocators are popular bio-logging tools, with advantageous sizes, longevity, and affordability. Biologists tracking seabirds often presume geolocator spatial accuracies between 186-202 km from previously-innovative, yet taxonomically, spatially, and computationally limited, studies. Using recently developed methods, we investigated whether assumed uncertainty norms held across a larger-scale, multispecies study.</p> <p>We field-tested geolocator spatial accuracy by synchronously deploying these with GPS loggers on scores of seabirds across five species and 11 Mediterranean Sea, East Atlantic and South Pacific breeding colonies. We first interpolated geolocations using the geolocation package FLightR without prior knowledge of GPS tracked routes. We likewise applied another package, probGLS, additionally testing whether sea-surface temperatures could improve route accuracy.</p> <p>Geolocator spatial accuracy was lower than the ~200km often assumed. probGLS produced the best accuracy (mean ± SD = 304 ± 413 km, <i>n</i> = 185 deployments) with 84.5% of GPS-derived latitudes and 88.8% of longitudes falling within resulting uncertainty estimates. FLightR produced lower spatial accuracy (408 ± 473 km, <i>n</i> = 171 deployments) with 38.6% of GPS-derived latitudes and 27% of longitudes within package-specific uncertainty estimates. Expected inter-twilight period (from GPS position and date) was the strongest predictor of accuracy, with increasingly equatorial solar profiles (i.e., closer temporally to equinoxes and/or spatially to the Equator) inducing more error. Individuals, species and geolocator model also significantly affected accuracy, while the impact of distance travelled between successive twilights depended on the geolocation package.</p> <p>Geolocation accuracy is not uniform among seabird species and can be considerably lower than assumed. Individual idiosyncrasies and spatiotemporal dynamics (i.e., shallower inter-twilight shifts by date and latitude) mean that practitioners should exercise greater caution in interpreting geolocator data and avoid universal uncertainty estimates. We provide a function capable of estimating relative accuracy of positions based on geolocator-observed inter-twilight period.</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.