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Figure 36 in Morphological variability and evaluation of taxonomic characters in the genus Erythemis Hagen, 1861 (Odonata: Libellulidae: Sympetrinae)
Figure 36. Erythemis plebeja distribution map.
Figure 35 in Morphological variability and evaluation of taxonomic characters in the genus Erythemis Hagen, 1861 (Odonata: Libellulidae: Sympetrinae)
Figure 35. Erythemis peruviana distribution map.
Figure 31 in Morphological variability and evaluation of taxonomic characters in the genus Erythemis Hagen, 1861 (Odonata: Libellulidae: Sympetrinae)
Figure 31. Erythemis collocata distribution map.
Figure 30 in Morphological variability and evaluation of taxonomic characters in the genus Erythemis Hagen, 1861 (Odonata: Libellulidae: Sympetrinae)
Figure 30. Erythemis carmelita distribution map.
FIGURE 2 in Arhodeoporus, Camactognathus, Plegadognathus, and Winlundia (Acari: Halacaridae), re-evaluation and geographical distribution
FIGURE 2: Phylogenetic tree of Arhodeoporus s.l., Camactognathus and Winlundia and a new classification, prepared on the basis of characters mentioned in Table 1, with Copidognathus as outgroup. (A. eclogarius and A. thyreophorus omitted)
FIGURE 4 in Arhodeoporus, Camactognathus, Plegadognathus, and Winlundia (Acari: Halacaridae), re-evaluation and geographical distribution
FIGURE 4: Geographical distribution of the genera Arhodeoporus s.str., Camactognathus, Plegadognathus, Winlundia, and Maracarus n. gen., the latter with the natural groups eclogarius (diamond), gracilipes (circle), longirostris (quadrat) and five species not belonging to any of these groups (triangle); Arhodeoporus: 1, arenarius; 2, submarinus. Camactognathus: 1, borealis; 2, grossipes; 3, tesselatus. Plegadognathus: 1, bonairensis; 2, bucculentus; 3, disparilis; 4, labronicus; 5, robustus; 6, thyreophorus; 7, wadjemupis, x, spec. Maracarus eclogarius group (diamond): 1, eclogarius; 2, leptopus; 3, longicrus; 4, mammillifer; 5, mirabilis; 6, mooreus; x - spec. M. gracilipes group (circle): 1, gracilipes; 2, minor; 3, perlucidus; 4, subtilis; x, spec. M. longirostris group (quadrat): 1, caudatus; 2, clypeatus; 3, corallicolus; 4, lizardensis; 5, longirostris; 6, mactanus; 7, psammophilus; 8, tanzanicus. Maracarus, others (triangle): 1, brevocularis; 2, kunzi; 3, lineatus; 4, minusculus; 5, nanus.
FIGURE 1 in Arhodeoporus, Camactognathus, Plegadognathus, and Winlundia (Acari: Halacaridae), re-evaluation and geographical distribution
FIGURE 1: A-E: Winlundia filistoma Newell, 1984, male: A – tarsus II, medial (lateral setae in broken line, lateral claw omitted); B – OC; C – PD (of a slightly compressed male); D – AE (areolae with ostia illustrated on one side only); E – GO. F-I: Winlundia forcipata Newell, 1984, male: F – part of left AE with epimeral pore; G – leg I, lateral; H – tarsus I, lateral; I – tarsus II, medial. (ep, epimeral pore; gac, genital acetabula; glp, gland pore; l-pas, lateral parambulacral seta; m-pas, medial parambulacral seta; os, ostia of rosette pores; so, solenidion) Scale = 50 µm A-E, Chile, Arica, 20 June 1966, stones and worm tubes, intertidal; F-I, Peru, Lima, La Herradura, 16 June 1966, coarse sand pockets between boulders.
FIGURE 3 in Arhodeoporus, Camactognathus, Plegadognathus, and Winlundia (Acari: Halacaridae), re-evaluation and geographical distribution
FIGURE 3: A – Maracarus eclogarius (AndrØ, 1959), female: tarsus IV, lateral (medial pas obscured, medial claw omitted); B – Maracarus kunzi (Bartsch, 1987), female: tarsus II, medial. C-E: Maracarus brevocularis (Bartsch, 1973), male: C – tarsus III, ventral (dorsal setae in broken line); D – GO and perigenital setae; E – tibia and tarsus IV, ventromedial. F – Plegadognathus bonairensis (Viets, 1936), female: GO and perigenital setae. G-J: Maracarus corallicolus (Otto, 2000): G – right part of AD with gland pore, male; H – genital plate with two pairs of pgs, sgs and internal acetabula, deutonymph; I – right OC, male; J – gnathosoma with suctoria, lateral aspect, male. (ca, canal of gland pore; epg, external porus of gland; gac, genital acetabula; hy, hyaline area; le, lens; l-pas, lateral parambulacral seta; m-pas, medial parambulacral seta; pa, porose areola; p-pgs, pori from perigenital setae; pgs, perigenital setae; sgs, subgenital setae) Scale = 50 µm. A, Gulf of Suez, 30 Dec. 1928, on sponges; B, South Africa, East London, broken shells; C-E, North Atlantic, Josephine Bank, 1 July 1967, 206 m, sediment; F, Indian Ocean, Australia, Dampier, Watering Cove, 8 Aug. 2000, algal turf on pneumatophores; G-J. Southwestern Pacific, Australia, Great Barrier Reef, Lizard Island, 13 Oct. 1998.
Figure 15 in The suprageneric groups of the Pimplinae (Hymenoptera: Ichneumonidae): a cladistic re-evaluation and evolutionary biological study
Figure 15. Cladogram resulting from secondary analysis: section 2 (of 9), Delomeristini.
Novel Datasets for Evaluating Song Popularity Prediction Tasks
<p>We present two novel datasets for hit song prediction (HSP): HSP-S and HSP-L. They are substantially larger than currently available dataset with respect to the number of features contained.</p> <p>Both datasets provide high- and low-level audio features stemming from <a href="https://acousticbrainz.org/data">AcousticBrainz</a> and short representative MP3 samples. Further, we include listener- and play-counts gathered from <a href="https://www.last.fm">last.fm</a> for both datasets. The larger dataset, HSP-L, contains 73,482 songs with audio features, listener- and play-counts, making it substantially larger than previous datasets. In addition, we provide the release year information for 65,575 songs as provided by the <a href="https://millionsongdataset.com">Million Song Dataset</a>.<br> The smaller dataset, HSP-S, contains 7,736 songs, listener- and play-counts as well as <a href="https://www.officialcharts.com/charts/billboard-hot-100-chart/">Billboard Hot 100</a> data for 50% of the songs, and release year information is available for 7,449 songs.</p>
Objective evaluation of Nintendo Wii Fit plus balance program training on postural stability in Multiple Sclerosis patients
<p>The use of the Nintendo Wii system has become a common tool for balance rehabilitation in patients with multiple sclerosis, but few studies verified the effectiveness of such an approach using quantitative measures of postural control. We aimed to evaluate the impact of rehabilitation treatment using the Nintendo Wii Fit Plus balance program on objective stabilometric parameters in multiple sclerosis patients. We enrolled 36 multiple sclerosis patients, with mild-moderate disability, referring to the multiple sclerosis Centre of the University of Catania from September 2013 to June 2014. Twenty participants underwent 20 individual sessions of balance exercise using the Nintendo Wii Fit Plus. They were assessed at baseline (T0) and at the end of rehabilitation program (T1) by Neurocom Balance Manager. Functional independence measure, Barthel index, and Berg balance scale were also administered. At T1, we found a significant improvement in total path length-open eyes, sway area-open eyes, and mean sway velocity-open eyes. Patients showed significant improvement in functional independence measure motor score, Barthel index, and in Berg balance scale. No significant differences between T0 and T1 in closed eyes trials were found. A significant correlation between delta values between T0 and T1 of sway area-open eyes and the Berg balance scale (r = -0.76; P < 0.0001) was found. This study confirmed that balance rehabilitation training performed using the Nintendo Wii with balance board significantly reduced some postural sway parameters in multiple sclerosis patients. It could be a good support to standard rehabilitation program in multiple sclerosis patients.</p>
Supplemental Material: An Experimental Evaluation of ITL, TDD and BDD: An extended study
<p>The supplementary material is distributed as follows:<br> 1) The list of figures that have not been included in the paper due to formatting limitations. <br> 2) A list of tables that have not been included in the paper due to formatting limitations.<br> 3) The data collection files that are part of the experimental process.<br> 4) The experimental plan document describing the activities performed in the experiment.</p>
A comprehensive evaluation of module detection methods for gene expression data
<p><strong>A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modeling the regulatory network, or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we could not find any clear advantages of biclustering and network-inference based approaches. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods.</strong></p>
DIALLS Dataset of evaluations of Open Educational Resources – The Cultural Literacy Learning Programme resources
<p>This dataset was used to support the development of the DIALLS project’s (Dialogue and Argumentation for cultural Literacy Learning in Schools, www.dialls2020.eu/) teaching and learning materials as open access online educational resources (OER). It consists of quantitative data (i) evaluating the three OER (i.e., professional development (PD) materials, lesson plans (LP), Scale of Progression for Cultural Literacy Learning (SPCLL)) and (ii) assessing the discussion forum as a platform for building a DIALLS community of practice. <em> N</em> = 140 teachers from Germany, UK, Portugal, and Israel participated in the surveys over the course of six months (September 2020 to February 2021). </p> <p>For each country, there are four datasets from the surveys. For data collection, the surveys have been translated into the partner countries’ language, open feedback is translated from the respective languages and now in English (i.e., Translation from German, English, Portuguese, Hebrew). </p> <p>Based on Kirkpatrick and Kirkpatrick’s (2006) evaluation model and the Value Creation Framework (VCF) (Wenger et al., 2011), the surveys have been developed and the discussion forum designed. From this, items regarding the materials’ application, content, design, and value were included in the assessment of the quality of PD materials, lesson plans, and SPCLL. The quality of the discussion forum was assessed regarding its motivation, participation, topic, and value. </p> <p>Considering the context, the dataset is relevant to the following areas of research: Communities of Practice, OER, Teachers’ professional development. </p> <p>The dataset is organised in 1 file (merged quantitative data from all four surveys and all four partners). A detailed description of the dataset can be accessed in a PDF file. Further, the provided codebook in the "variable view window" in SPSS explains the variables and values. </p>
Datasets for Evaluation of Multimodal Image Registration
<p><strong>Description</strong></p> <ul> <li><strong>Aerial data</strong></li> <li>The Aerial dataset is divided into 3 sub-groups by IDs: {7, 9, 20, 3, 15, 18}, {10, 1, 13, 4, 11, 6, 16}, {14, 8, 17, 5, 19, 12, 2}. Since the images vary in size, each image is subdivided into the maximal number of equal-sized non-overlapping regions such that each region can contain exactly one 300x300 px image patch. Then one 300x300 px image patch is extracted from the centre of each region. The particular 3-folded grouping followed by splitting leads to that each evaluation fold contains 72 test samples. <ul> <li> <p>Modality A: Near-Infrared (NIR)</p> </li> <li> <p>Modality B: three colour channels (in B-G-R order)</p> </li> </ul> </li> <li><strong>Cytological data</strong></li> <li>The Cytological data contains images from 3 different cell lines; all images from one cell line is treated as one fold in 3-folded cross-validation. Each image in the dataset is subdivided from 600x600 px into 2x2 patches of size 300x300 px, so that there are 420 test samples in each evaluation fold. <ul> <li> <p>Modality A: Fluorescence Images</p> </li> <li> <p>Modality B: Quantitative Phase Images (QPI)</p> </li> </ul> </li> <li><strong>Histological dataset</strong></li> <li>For the Histological data, to avoid too easy registration relying on the circular border of the TMA cores, the evaluation images are created by cutting 834x834 px patches from the centres of the original 134 TMA image pairs. <ul> <li> <p>Modality A: Second Harmonic Generation (SHG)</p> </li> <li> <p>Modality B: Bright-Field (BF)</p> </li> </ul> </li> </ul> <p>The evaluation set created from the above three publicly available 2D datasets consists of images undergone 4 levels of (rigid) transformations of increasing size of displacement. The level of transformations is determined by the size of the rotation angle θ and the displacement tx & ty, detailed in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets">this table</a>. Each image sample is transformed exactly once at each transformation level so that all levels have the same number of samples.</p> <ul> <li><strong>Radiological data</strong></li> <li>The Radiological dataset is divided into 3 sub-groups by patient IDs: {109, 106, 003, 006}, {108, 105, 007, 001}, {107, 102, 005, 009}. Since the Radiological dataset is non-isotropic (and also of varying resolution), it is resampled using B-spline interpolation to 1 mm<sup>3</sup> cubic voxels, taking explicit care to not resample twice; displaced volumes are transformed and resampled in one step. <ul> <li> <p>Modality A: T1-weighted MRI</p> </li> <li> <p>Modality B: T2-weighted MRI</p> </li> </ul> </li> </ul> <p>(Run <a href="https://github.com/MIDA-group/MultiRegEval/blob/master/utils/make_rire_patches.py"><code>make_rire_patches.py</code></a> to generate the sub-volumes.)</p> <p>Reference sub-volumes of size 210x210x70 voxels are cropped directly from centres of the (non-displaced) resampled volumes. Similarly as for the aforementioned 2D datasets, random (uniformly-distributed) transformations are composed of rotations θx, θy ∈ [-4, 4] degrees around the x- and y-axes, rotation θz ∈ [-20, 20] degrees around the z-axis, translations tx, ty ∈ [-19.6, 19.6] voxels in x and y directions and translation tz ∈ [-6.5, 6.5] voxels in z direction. 40 rigid transformations of increasing sizes of displacement are applied to each volume. Transformed sub-volumes, of size 210x210x70 voxels, are cropped from centres of the transformed and resampled volumes.</p> <p> </p> <p>In total, it contains 864 image pairs created from the aerial dataset, 5040 image pairs created from the cytological dataset, 536 image pairs created from the histological dataset, and metadata with scripts to create the 480 volume pairs from the radiological dataset. Each image pair consists of a reference patch <span class="math-tex">\(I^{\text{Ref}}\)</span> and its corresponding initial transformed patch <span class="math-tex">\(I^{\text{Init}}\)</span> in both modalities, along with the ground-truth transformation parameters to recover it.</p> <p>Scripts to calculate the registration performance and to plot the overall results can be found in <a href="https://github.com/MIDA-group/MultiRegEval">https://github.com/MIDA-group/MultiRegEval</a>, and instructions to generate more evaluation data with different settings can be found in <a href="https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data">https://github.com/MIDA-group/MultiRegEval/tree/master/Datasets#instructions-for-customising-evaluation-data</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>In the <code>*.zip</code> files, each row in <code>{Zurich,Balvan}_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> or <code>Eliceiri_patches/patch_tlevel[1-4]/info_test.csv</code> provides the information of an image pair as follow:</p> <ul> <li> <p>Filename: identifier(ID) of the image pair</p> </li> <li> <p>X1_Ref: x-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y1_Ref: y-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X2_Ref: x-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y2_Ref: y-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X3_Ref: x-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y3_Ref: y-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X4_Ref: x-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>Y4_Ref: y-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></p> </li> <li> <p>X1_Trans: x-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y1_Trans: y-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X2_Trans: x-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y2_Trans: y-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X3_Trans: x-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y3_Trans: y-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>X4_Trans: x-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Y4_Trans: y-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></p> </li> <li> <p>Displacement: mean Euclidean distance between reference corner points and transformed corner points</p> </li> <li> <p>RelativeDisplacement: the ratio of displacement to the width/height of image patch</p> </li> <li> <p>Tx: randomly generated translation in the x-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>Ty: randomly generated translation in the y-direction to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleDegree: randomly generated rotation in degrees to synthesise the transformed patch I<sub>Init</sub></p> </li> <li> <p>AngleRad: randomly generated rotation in radian to synthesise the transformed patch I<sub>Init</sub></p> </li> </ul> <p>In addition, each row in <code>RIRE_patches/fold[1-3]/patch_tlevel[1-4]/info_test.csv</code> has following columns:</p> <ul> <li>Z1_Ref: z-coordinate of the upper-left corner of reference patch I<sub>Ref</sub></li> <li>Z2_Ref: z-coordinate of the lower-left corner of reference patch I<sub>Ref</sub></li> <li>Z3_Ref: z-coordinate of the lower-right corner of reference patch I<sub>Ref</sub></li> <li>Z4_Ref: z-coordinate of the upper-right corner of reference patch I<sub>Ref</sub></li> <li>Z1_Trans: z-coordinate of the upper-left corner of transformed patch I<sub>Init</sub></li> <li>Z2_Trans: z-coordinate of the lower-left corner of transformed patch I<sub>Init</sub></li> <li>Z3_Trans: z-coordinate of the lower-right corner of transformed patch I<sub>Init</sub></li> <li>Z4_Trans: z-coordinate of the upper-right corner of transformed patch I<sub>Init</sub></li> <li>(...and similarly, coordinates of the 5th-8th corners)</li> <li>Tz: randomly generated translation in z-direction to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeX: randomly generated rotation around X-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadX: randomly generated rotation around X-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeY: randomly generated rotation around Y-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadY: randomly generated rotation around Y-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleDegreeZ: randomly generated rotation around Z-axis in degrees to synthesise the transformed patch I<sub>Init</sub></li> <li>AngleRadZ: randomly generated rotation around Z-axis in radian to synthesise the transformed patch I<sub>Init</sub></li> </ul> <p> </p> <p><strong>Naming convention</strong></p> <ul> <li><strong>Aerial Data</strong> <ul> <li> <pre> zh{ID}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>zh5_03_02_R.png</code> indicates the <em>Reference </em>patch of the <em>3rd row</em> and <em>2nd column</em> cut from the image with ID <code>zh5</code>.</li> </ul> </li> <li><strong>Cytological data</strong> <ul> <li> <pre> {{cellline}_{treatment}_{fieldofview}_{iFrame}}_{iRow}_{iCol}_{ReferenceOrTransformed}.png</pre> </li> <li>Example: <code>PNT1A_do_1_f15_02_01_T.png</code> indicates the <em>Transformed </em>patch of the <em>2nd row</em> and <em>1st column</em> cut from the image with ID <code>PNT1A_do_1_f15</code>.</li> </ul> </li> <li><strong>Histological data</strong> <ul> <li> <pre> {ID}_{ReferenceOrTransformed}.tif</pre> </li> <li>Example: <code>1B_A4_T.tif</code> indicates the <em>Transformed </em>patch cut from the image with ID <code>1B_A4</code>.</li> </ul> </li> </ul> <ul> <li><strong>Radiological Data</strong> <ul> <li> <pre> patient_{ID}_{iTransform}_T.mhd</pre> </li> <li> <pre> patient_{ID}_R.mhd </pre> </li> <li>Example: <code>patient_003_8_T.mhd</code> indicates the sub-volume <em>Transformed </em>with the <em>8th random transformation</em> cut from the volume with patient ID <code>003</code>; <code>patient_003_R.mhd</code> indicates the <em>Reference </em>sub-volume the volume with patient ID <code>003</code>.</li> </ul> </li> </ul> <p> </p> <p>This dataset was originally produced by the authors of <em><a href="https://arxiv.org/abs/2103.16262">Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study</a></em>.</p>
daleihao/Topographic_Effects: Codes and data for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"
<p>Codes and data to reproduce all results and plot all figures for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"</p>
Data accompanying the paper titled: Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain.
<p>The present repository contains the namelists, output data from the Weather Research and Forecasting (WRF) numerical simulations and observations described in the article "Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain" submitted to the Geosciences Model Development journal.</p> <p>The numerical simulations were developed to evaluate the influence of a forest parametrization on the simulation of the boundary layer flow over moderate complex terrain in the context of the Perdigão 2017 field campaign. The numerical simulations used WRF large eddy simulation mode (WRF-LES). The short-term high resolution (40 m horizontal grid spacing) and long-term (200 m horizontal grid spacing) WRF-LES were run for an integration time of 12 hours and 1.5 months, respectively, with and without forest parameterization. The short-term simulations focus on low-level jet events over the valley, while the long-term simulations cover the whole intensive observation period (IOP) of the field campaign. The results are validated using lidar and meteorological tower observations.</p> <p>The files in the repository are organized as follows:</p> <p>Namelists used to run the WRF model are located in the 'namelists' sub-directory.</p> <p>Data slices of domains d03 and d04 covering the location of the three meteorological towers and LIDAR for the whole integration time are located in the 'reduced_domain' sub-directory.</p> <p>Direct WRF output for selected periods (representing the low-level-jet episodes observed) for domains d03 and d04 are located in the 'files_select_time' sub-directory.</p> <p>The observations used to validate the model are located in the 'observations' sub-directory. The complete dataset of observations from the field campaign can be found in the official campaign data repository: re3data.org: Perdigao Field Experiment; editing status 2020-03-20; re3data.org - Registry of Research Data Repositories. http://doi.org/10.17616/R31NJMN4 last accessed: 2021-10-13. The WRF model can be downloaded from https://www2.mmm.ucar.edu/wrf/users/download/get_sources_new.php.</p>
Design Space Evaluation for Confidentiality under Architectural Uncertainty - Dataset
<p>This dataset contains additional files used, adapted and/or created for the bachelor's thesis of Oliver Liu. It includes the code for the architecture candidate filter, as well as models and configuration files used for the evaluation.</p>
Evaluating computational approaches for comparison of protein expression across cancer indications
<p>########################################################################################################<br># CPTAC PAN cancer imputed and normalized data <br>########################################################################################################<br># R Code snippet<br>iBAQ = readRDS("/your path/iBAQ.rds")<br>table(iBAQ$normalized)<br># CN No QN QN riBAQ Smooth QN <br># 17435640 17435640 17435640 17435640 17435640 <br>library(dplyr)<br>full.qn <- iBAQ %>% filter(normalized=="QN")<br>OV <- full.qn %>% filter(Indication=="OV")%>% filter(Tissue=="Tumor")<br>OV_4genes <- OV[OV$Gene%in% c("CDH6", "FOLR1", "ERBB2", "TACSTD2"),]</p>
For MACHINE LEARNING DATABASE evaluation: old-version SEM images of TiO2 particles UNITO
Test images recorded with old ZEISS software, metadata version could differ to the up-to-date version.
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.