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23,351 results for “Comparative”
BRAIN Journal-Isolating the Norepinephrine Pathway Comparing Lithium in Bipolar Patients to SSRIs in Depressive Patients-Figure 2. Resting state neuroimaging findings illustrating the action of Lithium following whole brain
<p>The axial, saggital, and coronal MRI activation maps illustrate increased delta frequency band neuronal activity in the 46 patients<br> diagnosed with Bipolar Affective Disorder compared to 32 female patients diagnosed with Major Depressive Disorder of Depressive<br> Episode. The Yellow/Orange shades indicate increased neuronal activity in the right Superior Frontal Gyrus (t=0.920, p=0.05060,<br> BA 6, MNI X=20, Y=0, Z=70) and in the right Cingulate Gyrus (t=0.0846, BA 24, MNI X= 5, Y=0, Z=51). Structural anatomy is<br> shown in grey scale (A – anterior; S – superior; P – posterior; L – left; R – right).</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 100 Schizophrenia Patients to 48 Patients with Major Depression -Figure 3. Resulting comparisons isolating the Dopamine D2 Mesolimbic Pathway
<p>Moreover, a study in Prague identified that Toxoplasmosis triggers schizophrenia in<br> predisposed subjects (Flegr et al., 2014). It may very well be that the Toxoplasmosis parasite<br> increasing dopamine secretions in the frontal lobes may very well lead to decreased cerebral<br> volumes. Cat feces, sexual contact, and eating raw or undercooked pork are well known transmitters<br> of the Toxoplasmosis parasite and may be advisable that patients take precautions of the possibility<br> of these triggering symptoms of psychosis with these possible routes of infection. Another adjunct to Atypical Antipsychotics may be to treat the potential cerebral Toxoplasmosis infection with<br> Cotrimoxazole or Pyrimethamine/Clindamycin.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 100 Schizophrenia Patients to 48 Patients with Major Depression -Figure 2. Resting State neuroimaging findings illustrating the action of atypical antipsychotics on postsynaptic dopamine D2
<p>Contrastingly, the results of the one-hundred males and females diagnosed with<br> Schizophrenia compared to the thirty-two females diagnosed with Major Depressive Disorder or<br> Depressive Episodes. The one-hundred male and female Schizophrenia patients had an average age<br> of 32-years-old and a Standard Deviation of 11.8-years. Whereas, the Depressive Episode and<br> Major Depressive Disorder females had an average age of 50-years old and a Standard Deviation of<br> 11.7-years. The statistically significant neuroimaging results identified the Delta (1.5–6 Hz)<br> frequency band at the neuroanatomical location of the region of the Superior Frontal Gyrus<br> (p=0.007; t=2.08, BA 10, X=25, Y=55, Z=30) with greater neuronal oscillations and synchrony in<br> the one-hundred males and females diagnosed with Schizophrenia than the thirty-two females<br> diagnosed with Depressive Episodes and Major Depressive Disorder.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 100 Schizophrenia Patients to 48 Patients with Major Depression -Figure 1. Group comparisons designed for isolating the mesolimbic Dopamine D2
<p>All patients diagnosed with Schizophrenia were medicated with atypical antipsychotics, Olanzapine (dose 5mg/day to 20mg/day) or Risperidone (dose 1mg/day to 4mg/day) blocking both Dopamine D2 and Serotonin 5-HT2 receptors. Whereas, all of the patients diagnosed with Major Depression or Depressive Episode were medicated with Sertraline (50mg/day to 200mg/day maximum dose) or Citalopram (20mg/day to 40mg/day maximum dose). This research protocol was approved by the Bioethical Commission of the Medical University of Lublin. </p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
BRAIN Journal-Isolating the Norepinephrine Pathway Comparing Lithium in Bipolar Patients to SSRIs in Depressive Patients-Figure 1. Resting state neuroimaging findings illustrating the action of Lithium following whole brain
<p>The axial, saggital, and coronal MRI activation maps illustrate neuronal activity of 46 patients diagnosed with Bipolar Affective<br> Disorder compared to 16 male patients diagnosed with Major Depressive Disorder of Depressive Episode. The Yellow/Orange<br> shades indicate increased neuronal activity in the right Superior Temporal Gyrus (t=1.403, p=0.00780, BA 41, MNI X=45, Y= -35,<br> Z=10) with activation also in the Fusiform Gyrus (t=1.26, BA 20, MNI X= 45, Y= -35, Z=10), the Parahippocampal Gyrus (t=1.29,<br> BA 36, MNI X=45, Y= -35, Z=10). (b) Increased neuronal activity in the Cingulate Gyrus (t=1.06, BA 32, MNI X=45, Y= -35,<br> Z=10). Structural anatomy is shown in grey scale (A – anterior; S – superior; P – posterior; L – left; R – right).</p>
Supplementary dataset for 'A randomised phase III trial of carboplatin compared with docetaxel in BRCA1/2 mutated and pre-specified triple negative breast cancer "BRCAness" subgroups: the TNT Trial'
<p>This dataset corresponds to the PAM50 gene expression profiling of primary tumour samples that were collected as part of the TNT clinical trial (ISRCTN97330959, NCT00532727, CRUK/07/012). NanoString® platform nCounter analysis was performed on the RNA extracts at the Institute of Cancer Research and Royal Marsden Hospital. The manuscript describing the trial outcome is currently under review in Nature Medicine.</p>
Comparative Independent Fiscal Institutions Dataset
<p>The <strong>Comparative Independent Fiscal Institution Dataset (CifiD)</strong> provides information about the legal provisions that constitute the <strong>monitoring scope, powers and institutional design</strong> of <strong>41 Independent Fiscal Institutions (IFIs) in 38 countries</strong> including most EU member states. The dataset covers legislative changes to these institutions until 2018.</p> <p>Furthermore, based on the coding <strong>new indices on the political independence and the level of expertise</strong> of these fiscal monitoring bodies were developed, allowing researchers to investigate the roots and effectiveness of fiscal monitoring and its effects on the fiscal performance of national governments more thoroughly.</p> <p><strong>Institutional properties</strong></p> <p>The CifiD contains 42 items that refer to the institutional properties of fiscal monitoring bodies as they are defined in legislation, classified along the following dimensions:</p> <ul> <li>Institutional model, monitoring and intervention powers</li> <li>IFI design: statutory rules, operations, leadership, composition, term rules, knowledge, transparency</li> </ul> <p>Two main indices were calculated and are part of the dataset:</p> <ul> <li>Formal independence from political interference</li> <li>Level of expertise</li> </ul>
Supporting data for "Snap happy: camera traps are an effective sampling tool when compared to alternative methods"
<p>Author recommendations and response ratios extracted from studies comparing camera traps to another survey method. These data underlie the analyses in a the journal article 'Snap happy: camera traps are an effective sampling tool when compared to alternative methods', published in the journal Royal Society Open Science (https://doi.org/10.1098/rsos.181748). </p>
Supplementary Material: Comparing Formal Tools for System Design: a Case Study from the Railway Domain
<p>The package includes a set of models for a railway moving-block system:</p> <p>(a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system;</p> <p>(b) a set of 10 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more model of the moving-block system from (a), developed by means of the tool.</p>
What does Google recommend when you want to compare insurance offerings? – A method and empirical study considering Google's top search results
<p>This dataset is part of a publication and shows Google's search results for German search queries on insurance comparison offerings.</p> <p>Relevant search queries were extracted from a commercial search engine log file consisting of more than 640,000 different search queries. From the log, we extracted a variety of query formulations for the same topic, i.e., queries containing the same word or phrase. The selection was based on pre-defined keywords in the context of insurance comparisons. The queries from the log file were automatically selected by combining the terms "*insurance*" and "*comparison*" (including left as well as right truncation). Examples of such inquiries are "car insurance comparison", "occupational disability insurance comparison", "liability insurance in comparison". This procedure identified a total of 121 different search queries. Scraping of the results took place between 08.05. - 09.05.2018.The adress data were extracted by using a text classification algorithm and a crawler to find the contact data on a website.</p> <p>It is a tab-separated file with the following attributes:</p> <p>ID: Unique row identifier</p> <p>ID Query: Unique search query identifier</p> <p>Query: German search query </p> <p>Position: Result position to the search query </p> <p>URL: URL of the search result </p> <p>Host: Host of the search result </p> <p>Company: Name of the company on the website</p> <p>Street: Street in the address on the website </p> <p>Zipcode: Street in the address on the website </p> <p>Location: Location in the address on the website </p> <p>District: District in the address on the website </p> <p>State: State in the address on the website </p> <p>Country: Country in the address on the website </p>
A Meta-Analysis on the Reliability of Comparative Judgement Data
<p>This is the data and R analysis script with the article "A Meta-Analysis on the Reliability of Comparative Judgement"</p>
Supplementary information for D4.6 CEMCAP comparative techno-economic analysis of CO2 capture in cement plants
<p>Supplementary information for D4.6 CEMCAP comparative techno-economic analysis of CO2 capture in cement plants</p>
A comparative study of experimental configurations in synchrotron pair distribution function
<p>Raw data for: Synchrotron X-ray pair distribution function analysis of tricalcium silicate pastes; doi: <a href="https://doi.org/10.3390/ma12081347">https://doi.org/10.3390/ma12081347</a></p> <p>The identification and quantification of amorphous components, and nanocrystalline phases with very small crystal sizes, smaller than ~3 nm, within samples containing crystalline phases is very challenging. However, this is important as there are several type of systems that contain these matrices: building materials, glass-ceramics, some alloys, etc. Total scattering synchrotron pair distribution function (PDF) can be used to characterize the local atomic order of the nanocrystalline components and to carry out quantitative analyses in complex mixtures. Although the resolution in momentum transfer space has been widely discussed, the resolution in the interatomic distance space has not been discussed to the best of our knowledge. Here, we report synchrotron PDF data collected at three beamlines in different experimental configurations and X-ray detectors. We not only discuss the effect of the resolution in Q-space, Q<sub>max ins</sub> of the recorded data and Q<sub>max</sub> of the processed data, but we also discuss the resolution in the interatomic distance (real) space. A thorough study for single phase crystalline nickel used as standard was carried out. Then, selected cement-related samples including anhydrous tricalcium and dicalcium silicates, and pastes derived from the hydration of tricalcium silicate and ye’elimite with bassanite were analyzed.</p>
Frequency list for comparing IE613 data with SE607
<p>This is a sample of a list of clean frequencies away from loud RFI used with tutorials for <a href="https://github.com/creaneroDIAS/beamModelTester/">beamModelTester</a>.</p>
Dataset for training classifiers of comparative sentences
<p><br> As there was no large publicly available cross-domain dataset for comparative argument mining, we create one composed of sentences, potentially annotated with BETTER / WORSE markers (the first object is better / worse than the second object) or NONE (the sentence does not contain a comparison of the target objects). The BETTER sentences stand for a pro-argument in favor of the first compared object and WORSE-sentences represent a con-argument and favor the second object. </p> <p>We aim for minimizing dataset domain-specific biases in order to capture the nature of comparison and not the nature of the particular domains, thus decided to control the specificity of domains by the selection of comparison targets. We hypothesized and could confirm in preliminary experiments that comparison targets usually have a common hypernym (i.e., are instances of the same class), which we utilized for selection of the compared objects pairs. </p> <p>The most specific domain we choose, is computer science with comparison targets like programming languages, database products and technology standards such as Bluetooth or Ethernet. Many computer science concepts can be compared objectively (e.g., on transmission speed or suitability for certain applications). The objects for this domain were manually extracted from List of-articles at Wikipedia. In the annotation process, annotators were asked to only label sentences from this domain if they had some basic knowledge in computer science. </p> <p>The second, broader domain is brands. It contains objects of different types (e.g., cars, electronics, and food). As brands are present in everyday life, anyone should be able to label the majority of sentences containing well-known brands such as Coca-Cola or Mercedes. Again, targets for this domain were manually extracted from `List of''-articles at Wikipedia.</p> <p>The third domain is not restricted to any topic: random. For each of 24~randomly selected seed words 10 similar words were collected based on the distributional similarity API of JoBimText (http://www.jobimtext.org). Seed words created using randomlists.com: book, car, carpenter, cellphone, Christmas, coffee, cork, Florida, hamster, hiking, Hoover, Metallica, NBC, Netflix, ninja, pencil, salad, soccer, Starbucks, sword, Tolkien, wine, wood, XBox, Yale.</p> <p>Especially for brands and computer science, the resulting object lists were large (4493 in brands and 1339 in computer science). In a manual inspection, low-frequency and ambiguous objects were removed from all object lists (e.g., RAID (a hardware concept) and Unity (a game engine) are also regularly used nouns). The remaining objects were combined to pairs. For each object type (seed Wikipedia list page or the seed word), all possible combinations were created. These pairs were then used to find sentences containing both objects. The aforementioned approaches to selecting compared objects pairs tend minimize inclusion of the domain specific data, but do not solve the problem fully though. We keep open a question of extending dataset with diverse object pairs including abstract concepts for future work. </p> <p>As for the sentence mining, we used the publicly available index of dependency-parsed sentences from the Common Crawl corpus containing over 14 billion English sentences filtered for duplicates. This index was queried for sentences containing both objects of each pair. For 90% of the pairs, we also added comparative cue words (better, easier, faster, nicer, wiser, cooler, decent, safer, superior, solid, terrific, worse, harder, slower, poorly, uglier, poorer, lousy, nastier, inferior, mediocre) to the query in order to bias the selection towards comparisons but at the same time admit comparisons that do not contain any of the anticipated cues. This was necessary as a random sampling would have resulted in only a very tiny fraction of comparisons. Note that even sentences containing a cue word do not necessarily express a comparison between the desired targets (dog vs. cat: He's the best pet that you can get, better than a dog or cat.). It is thus especially crucial to enable a classifier to learn not to rely on the existence of clue words only (very likely in a random sample of sentences with very few comparisons). For our corpus, we keep pairs with at least 100 retrieved sentences.</p> <p>From all sentences of those pairs, 2500 for each category were randomly sampled as candidates for a crowdsourced annotation that we conducted on figure-eight.com in several small batches. Each sentence was annotated by at least five trusted workers. We ranked annotations by confidence, which is the figure-eight internal measure of combining annotator trust and voting, and discarded annotations with a confidence below 50%. Of all annotated items, 71% received unanimous votes and for over 85% at least 4 out of 5 workers agreed -- rendering the collection procedure aimed at ease of annotation successful.</p> <p>The final dataset contains 7199 sentences with 271 distinct object pairs. The majority of sentences (over 72%) are non-comparative despite biasing the selection with cue words; in 70% of the comparative sentences, the favored target is named first.</p> <p>You can browse though the data here: https://docs.google.com/spreadsheets/d/1U8i6EU9GUKmHdPnfwXEuBxi0h3aiRCLPRC-3c9ROiOE/edit?usp=sharing </p> <p>Full description of the dataset is available in the <a href="https://arxiv.org/abs/1809.06152">workshop paper at ACL 2019 conference</a>. Please cite this paper if you use the data: Franzek, Mirco, Alexander Panchenko, and Chris Biemann. "Categorization of Comparative Sentences for Argument Mining." <em>arXiv preprint arXiv:1809.06152</em> (2018).</p> <pre>@inproceedings{franzek2018categorization, title={Categorization of Comparative Sentences for Argument Mining}, author={Panchenko, Alexander and Bondarenko, and Franzek, Mirco and Hagen, Matthias and Biemann, Chris}, booktitle={Proceedings of the 6th Workshop on Argument Mining at ACL'2019}, year={2019}, address={Florence, Italy} }</pre> <p> </p> <p> </p>
Text-fig. 9.—Anterior portion of maxilla of the Jordan theropod (stippled) compared with the corresponding region of the maxillae of other tyrannosaurs. All reproduced to equal length from anterior end of maxilla to anterior margin of antorbital recess (this length in the actual specimens differs by less than 15 percent). A, Juvenile Tyrannosaurus rex. B, Albertosaurus lancensis. C, Juvenile Albertosaurus libratus (AMNH 5664). (A, redrawn from Lawson, 1976; B and C, redrawn from Gilmore, 1946). in A new Theropod Dinosaur from the Upper Cretaceous of Central Montana
Text-fig. 9.—Anterior portion of maxilla of the Jordan theropod (stippled) compared with the corresponding region of the maxillae of other tyrannosaurs. All reproduced to equal length from anterior end of maxilla to anterior margin of antorbital recess (this length in the actual specimens differs by less than 15 percent). A, Juvenile Tyrannosaurus rex. B, Albertosaurus lancensis. C, Juvenile Albertosaurus libratus (AMNH 5664). (A, redrawn from Lawson, 1976; B and C, redrawn from Gilmore, 1946).
Comparing the route-choice behavior of pedestrians around obstacles in a virtual experiment and a field study
<p>There is a great controversy about the application of virtual experiment in pedestrian routing behavior research. We conducted field observations and virtual experiment to study the route choice behavior of pedestrians. The route choice behavior around obstacles are compared qualitatively. The results shows that distance to the exit routes as well as the density around the exits show great influence on the pedestrians' route choice behavior while the speed of frontal pedestrians shows no obvious impact on the route choice. Pedestrians prefer to choose local closer exit or the exit with less occupants. The results of logistic regression show that the similar results can be obtained in virtual experiment and field observation qualitatively. This work can verify the validity of the virtual experiment for studying route choice behavior of pedestrians, which is of great importance for the application of virtual experiment.</p> <p>The files are trajectories of the recorded videos in the field observation.</p>
Data set of the article: Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus
<p>Data of investigation published in the article "Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus".</p> <p>Abstract of the article:</p> <p>Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyse and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyse two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, WoS used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.</p>
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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.