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1,048 results for “interpreting”
The Qualitative Analysis of Repertory Grid Data: Interpretive Clustering (datasets only)
<p>Datasets accompanying the publication "The Qualitative Analysis of Repertory Grid Data: Interpretive Clustering" by </p> <p>Burr, King, and Heckmann.</p>
MiRoR10 - P1 - Dataset A systematic review finds that spin or interpretation bias is abundant in evaluations of ovarian cancer biomarkers
<p>This dataset is related to "A systematic review finds that spin or interpretation bias is abundant in evaluations of ovarian cancer biomarkers" (Mona Ghannad, Maria Olsen, Isabelle Boutron, Patrick M. Bossuyt)</p>
FIGURE 12 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 12. Indian Ocean. # indicates terrestrial. + indicates coastal. Red indicates Protorchestoidae taxa. Blue indicates Talitroidae taxa.
FIGURE 9 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 9. South Atlantic Ocean. # indicates terrestrial. + indicates coastal. Red indicates Protorchestoidae taxa. Blue indicates Talitroidae taxa.
FIGURE 8 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 8. North Atlantic Ocean. # indicates terrestrial. + indicates coastal. Blue indicates Talitroidae taxa.
FIGURE 7 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 7. Cladogram of relationships in the Talitroidea. Significant synapomorphies are indicated (see table 1).
FIGURE 6. Epimera 1–3. 38.2 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 6. Epimera 1–3. 38.2 with slits (after Lowry & Myers 2013). Uropod 1 peduncle 39.1 with distolateral robust seta (after Friend 1987). Uropods 1 and 2 rami. 40.2 dorsally flattened (after Lowry & Baldanzi 2016). Uropod 2 exopod 43.2 with apical spear-shaped setae (after Momtazi et al. 2017); Telson. 48.1 tapering distally (after Duncan 1994); 48.2 rounded distally (after Friend 1987); 48.3 subrectangular (after Hurley 1957); 49.2 with apical robust setae only (after Duncan 1994, Hurley 1957); 49.3 with apical and marginal robust setae (after Friend 1987).
FIGURE 3. Male gnathopod 1 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 3. Male gnathopod 1 propodus anterior margin; 24.1 with 1–2 groups of robust setae (after Morino 2014); 24.2 with 3–4 groups of robust setae (after Marsden & Fenwick 1984); 24.3 with 5–9 groups of robust setae (after Lowry & Myers 2019a); 24. 4 with scattered groups of very short setae (after Bousfield 1982). Male gnathopod 1 propodus 25.1 subrectangular (after Stock & Martin 1988); 25.2 enlarged subrectangular (after Hurley 1957); 25.3 subovoid (after Jo 1988); 25.4 subtriangular with well developed posterodistal lobe (after Marsden & Fenwick, 1984)); 25.5 subrectangular tapering distally (after Shoemaker 1936). Male gnathopod 1 palm 26.1 transverse (White et al. 2013); 26.2 acute or obtuse (Friend 1982); 26.3 absent (after Lowry & Myers 2019a)
FIGURE 11 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 11. South Pacific Ocean. # indicates terrestrial. + indicates coastal. Red indicates Protorchestoidae taxa. Blue indicates Talitroidae taxa.
FIGURE 5. Pereopod 5 merus and carpus. 32. 1 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 5. Pereopod 5 merus and carpus. 32. 1. Not expanded (after Stock 1997); 32.2 merus and carpus expanded (after Bellan-Santini & Ruffo 1991). Pereopod 5 dactylus. 33.1 long, slender (after Stock 1997); 33.2 short, inflated (after Bellan-Santini & Ruffo 1991). Pereopod 7 sexual dimorphism.; 35.2 merus and carpus slightly incrassate (after Duncan 1994); 35.3 merus and carpus strongly incrassate (after Smith 1998); 35.4 carpus massive, plate-like (after Hurley 1956). Oostegites 36.1 with curled tips (after Bousfield 1982); 36.2 with simple smooth tips (after Bousfield 1982); 36.3 with multifurcated tips (after Serejo & Lowry 2008); 36.4 with spatulate tips (after Wildish & LeCroy 2014).
FIGURE 10 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 10. North Pacific Ocean. # indicates terrestrial. + indicates coastal. Red indicates Protorchestoidae taxa. Blue indicates Talitroidae taxa.
FIGURE 2. Male gnathopod 1. 23.2 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 2. Male gnathopod 1. 23.2 posterior margin of merus, carpus and proximal margin of propodus each with palmate setae (after Friend 1982); 23.1 posterior margin of merus, carpus and propodus each with patch of palmate setae (after Hurley 1957); 23.3a. posteriormargin of carpus and propodus each with patch of palmate setae (after Richardson 1991); 23.3b posterior margin of merus and carpus each with patch of palmate setae (after Friend 1987); 23.3c posterior margin of propodus with patch of palmate setae (after Hurley 1957); 23.3d posterior margin of carpus with patch of palmate setae (after Friend 1987); 23.4 posterior margin of merus, carpus and propodus each without patch of palmate setae (after Lowry & Myers 2019a).
FIGURE 1. Antenna 2.18.1 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 1. Antenna 2.18.1 slender (after Bousfield 1991); 18.2 incrassate (after Bousfield 1982). Maxilliped. 20.1 palp article 2 without distomedial lobe (after Hurley 1957); 20.2 palp article 2 with distomedial lobe (after Iaciofano & Lo Brutto 2016); 21.1 palp article 4 large well defined (after Krapp-Schickel 1993); 21.2 palp article 4 small, well defined (after Duncan 1994); 21.3a palp article 4 fused to article 3 (after Bellan-Santini, 1993); 21.3b palp article 4 reduced button shaped (after Hurley 1957). Gnathopod 1. 22.1 subchelate (after White, Lowry & Morino 2013); 22.2 parachelate (after Lindeman 1990); 22.3 simple (after Lowry & Myers 2019a); 22.4 chelate (after Serejo 2004).
FIGURE 4. Female gnathopod 2 merus. 27.2 in A phylogeny and classification of the Talitroidea (Amphipoda, Senticaudata) based on interpretation of morphological synapomorphies and homoplasies
FIGURE 4. Female gnathopod 2 merus. 27.2 posterodistal lappet present (after Bousfield 1982); male gnathopod 2 dactylus 28.1 unmodified, distally blunt (after Bousfield 1958); 28.2 recurved distally (after Afonso 1977); 28.3 attenuated distally (after Lowry & Myers 2019b); 28.4 slender, distally acute (after Chevreux, 1915); 28.5 stout, distally acute (after Stock & Martin 1988). Pereopods 3–7. 29.1 simplidactylate (after Friend 1979). 29.2 cuspidactylate (after Morino 2014, Stock & Abreu 1992). Pereopod 4 dactylus. 30.2. thickened (after Stock & Abreu 1992, Lowry & Coleman 2011).
Penobscot Interpretation Dataset
<p>We have seen in the past years the flourishing of machine and deep learning algorithms in several applications such as image classification and segmentation, object detection and recognition, among many others. This was only possible, in part, because datasets like ImageNet - with +14 million labeled images - were created and made publicly available, providing researches with a common ground to compare their advances and extend the state-of-the-art. Although we have seen an increasing interest in machine learning in geosciences as well, we will only be able to achieve a significant impact in our community if we collaborate to build such a common basis. This is even more difficult when it comes to the Oil & Gas industry, in which confidentiality and commercial interests often hinder the sharing of datasets to others. In this letter, we present the Penobscot interpretation dataset, our contribution to the development of machine learning in geosciences, more specifically in seismic interpretation. The Penobscot 3D seismic dataset was acquired in the Scotian shelf, offshore Nova Scotia, Canada. The data is publicly available and comprises pre- and pos-stack data, 5 horizons and well logs of 2 wells. However, for the dataset to be of practical use for our tasks, we had to reinterpret the seismic, generating 7 horizons separating different seismic facies intervals. The interpreted horizons were used to generated +100,000 labeled images for inlines and crosslines. To demonstrate the utility of our dataset, results of two experiments are presented.</p> <p><strong>Dataset contents</strong></p> <ul> <li>Crosslines: <ul> <li>Classes: 7</li> <li>Slices used for training: 289</li> <li>Records per class for training: 16706</li> <li>Slices used for testing: 192</li> <li>Records per class for testing: 1000</li> </ul> </li> <li>Inlines: <ul> <li>Classes: 7</li> <li>Slices used for training: 358</li> <li>Records per class for training: 14988</li> <li>Slices used for testing: 238</li> <li>Records per class for testing: 1000</li> </ul> </li> </ul>
A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes - Data
<p>This file contains the data that was used in the paper titled "A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes", which is submitted for review. The preprint is available at <a href="https://www.biorxiv.org/content/10.1101/2020.03.30.016675v1.abstract">https://www.biorxiv.org/content/10.1101/2020.03.30.016675v1.abstract</a></p>
Data from: Bayesian analyses in phylogenetic palaeontology: interpreting the posterior sample
<p>Establishing hypotheses of relationships is a critical prerequisite for any macroevolutionary analysis, but different approaches exist for achieving this goal. Amongst palaeontologists using morphological data the Bayesian approach is increasingly preferred over parsimony, but this shift also alters the way we think about samples of trees. Here we revisit stratigraphic congruence as a comparator between Bayesian and parsimony samples, but in a new visual context: treespace. Such spaces represent an ordination of unique topologies that can also be extended to create a "landscape" where altitude represents some comparative measure (here congruence with stratigraphy). By co-opting existing visualization tools and applying them to a meta-analysis of 128 cladistic data sets we show that there is no consistent favouring of either Bayesian or parsimony according to stratigraphic congruence metrics, and further that empirical treespace visualizations suggest a complex variety of topological landscapes. We conclude by arguing that treespaces should become a standard exploratory tool in phylogenetic analysis.</p>
Recalibrating classifiers for interpretable abusive content detection
<p>Dataset and code for the paper, 'Recalibrating classifiers for interpretable abusive content detection' by Vidgen et al. (2020) -- to appear at the <a href="https://sites.google.com/site/nlpandcss/">NLP + CSS workshop</a> at <a href="https://2020.emnlp.org">EMNLP 2020</a>.</p> <p>We provide:</p> <ol> <li>1,000 annotated tweets, sampled using the Davidson classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>1,000 annotated tweets, sampled using the Perspective classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election</li> <li>Code for recalibration in R and STAN.</li> <li>Annotation guidelines for both datasets.</li> </ol> <p> </p> <p><strong>Paper abstract</strong></p> <p>We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the 'raw' scores are used) align poorly with human evaluations. This limits their use to understand the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election.<br> A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.</p> <p> </p> <p> </p>
Interpreting Lee (2019)'s five dimensions through time and linking them to an 'ideal' PhD graduate
<p>Figure 1: Variation of student potential based on Lee (2019)’s five dimensions relative with time</p> <p>Figure 2: Linking Anne Lee’s approach to the ‘ideal’ profile after earning a PhD</p>
Data/code for Interpretable, calibrated neural networks for analysis and understanding of neutron spectra
<p>Simulated neutron spectra from inelastic neutron scattering on the double perovskite PCSMO. The dataset is related to the publication <em>Interpretable, calibrated neural networks for analysis and understanding of neutron spectra</em>. The paper is yet to be submitted, but the references will be added in due course.</p> <p># Pre-trained model weights</p> <p>`model-weights.tgz`</p> <p>This file contains the weights for the models reported in the paper which can be downloaded and used to re-produce the results.</p> <p>Please download the `model-weights.tgz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github.</p> <p># Data Generation</p> <p>`data_generation.tar.gz`</p> <p> Large data files for github repository associated with the publication _Interpretable, calibrated neural networks for analysis and understanding of neutron spectra_ These files relate to generation of the data in the paper.</p> <p>Please download the `data_generation.tar.gz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github.</p> <p>#Training data</p> <p>`<a href="https://zenodo.org/api/files/47a40b89-ae6f-4655-aa4b-2d8fc11ec597/pcsmo-simulated-spectra.tgz?versionId=1ff107a9-85b9-4b46-9c77-84319ca9d59a">pcsmo-simulated-spectra.tgz</a>`</p> <p>Contains the data used to train the networks. Extract this file to a data directory and then point the `train.py` files for the various models to look for this `<datadir>` in the specified location in those files.</p>
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.