100+ datasets found
  1. J

    Homogeneity pursuit in panel data models: Theory and application...

    • journaldata.zbw.eu
    • jda-test.zbw.eu
    txt, zip
    Updated Dec 7, 2022
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    Wuyi Wang; Peter C.B. Phillips; Liangjun Su; Wuyi Wang; Peter C.B. Phillips; Liangjun Su (2022). Homogeneity pursuit in panel data models: Theory and application (replication data) [Dataset]. http://doi.org/10.15456/jae.2022327.0706894514
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    zip(59915), txt(2453)Available download formats
    Dataset updated
    Dec 7, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Wuyi Wang; Peter C.B. Phillips; Liangjun Su; Wuyi Wang; Peter C.B. Phillips; Liangjun Su
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel-CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.

  2. f

    Two tests of variance homogeneity for clustered data where group size is...

    • tandf.figshare.com
    text/x-tex
    Updated Jan 26, 2025
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    Mary Gregg; Adam Creuziger; Somnath Datta; Douglas Lorenz (2025). Two tests of variance homogeneity for clustered data where group size is informative [Dataset]. http://doi.org/10.6084/m9.figshare.27956711.v1
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    text/x-texAvailable download formats
    Dataset updated
    Jan 26, 2025
    Dataset provided by
    Taylor & Francis
    Authors
    Mary Gregg; Adam Creuziger; Somnath Datta; Douglas Lorenz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    To evaluate variance homogeneity among groups for clustered data, Iachine et al. (Robust tests for the equality of variances for clustered data. J Stat Comput Simul 2010;80(4):365–377) introduced an extension of the well-known Levene test. However, this method does not account for informative cluster size (ICS) or informative within-cluster group size (IWCGS), which can occur in clustered data when cluster and group sizes are random variables. This article introduces two tests of variance homogeneity that are appropriate for data with ICS and IWCGS, one extending the Levene-style transformation method and one based on a direct comparison of estimates of variance. We demonstrate the properties of our tests in a detailed simulation study and show that they are resistant to the potentially biasing effects of ICS and IWCGS. We illustrate the use of these tests by applying them to a data set of x-ray diffraction measurements collected from a specimen of duplex steel.

  3. d

    Replication Data for: Homogeneity in Housing Development: Benefits for...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Sep 24, 2024
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    Kiselev, Aleksei (2024). Replication Data for: Homogeneity in Housing Development: Benefits for Investors Over Residents [Dataset]. http://doi.org/10.7910/DVN/DYL2B1
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    Dataset updated
    Sep 24, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Kiselev, Aleksei
    Description

    Matlab codes to reproduce the results of the model section.

  4. f

    Data from: Category-Adaptive Variable Screening for Ultra-High Dimensional...

    • tandf.figshare.com
    zip
    Updated Aug 16, 2023
    + more versions
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    Jinhan Xie; Yuanyuan Lin; Xiaodong Yan; Niansheng Tang (2023). Category-Adaptive Variable Screening for Ultra-High Dimensional Heterogeneous Categorical Data [Dataset]. http://doi.org/10.6084/m9.figshare.7819544.v4
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    zipAvailable download formats
    Dataset updated
    Aug 16, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Jinhan Xie; Yuanyuan Lin; Xiaodong Yan; Niansheng Tang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The populations of interest in modern studies are very often heterogeneous. The population heterogeneity, the qualitative nature of the outcome variable and the high dimensionality of the predictors pose significant challenge in statistical analysis. In this article, we introduce a category-adaptive screening procedure with high-dimensional heterogeneous data, which is to detect category-specific important covariates. The proposal is a model-free approach without any specification of a regression model and an adaptive procedure in the sense that the set of active variables is allowed to vary across different categories, thus making it more flexible to accommodate heterogeneity. For response-selective sampling data, another main discovery of this article is that the proposed method works directly without any modification. Under mild regularity conditions, the newly procedure is shown to possess the sure screening and ranking consistency properties. Simulation studies contain supportive evidence that the proposed method performs well under various settings and it is effective to extract category-specific information. Applications are illustrated with two real datasets. Supplementary materials for this article are available online.

  5. f

    Data from: Additional file 2 of Homogeneity score test of AC1 statistics and...

    • springernature.figshare.com
    txt
    Updated Feb 18, 2020
    + more versions
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    Chikara Honda; Tetsuji Ohyama (2020). Additional file 2 of Homogeneity score test of AC1 statistics and estimation of common AC1 in multiple or stratified inter-rater agreement studies [Dataset]. http://doi.org/10.6084/m9.figshare.11814150.v1
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    txtAvailable download formats
    Dataset updated
    Feb 18, 2020
    Dataset provided by
    figshare
    Authors
    Chikara Honda; Tetsuji Ohyama
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Additional file 2. R code for type I errors and power.

  6. d

    Data from: Ethnoracial Homogeneity and Public Outcomes: The (Non)effects of...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Kustov, Alexander; Pardelli, Giuliana (2023). Ethnoracial Homogeneity and Public Outcomes: The (Non)effects of Diversity [Dataset]. http://doi.org/10.7910/DVN/AY32JZ
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Kustov, Alexander; Pardelli, Giuliana
    Description

    Ethnoracial Homogeneity and Public Outcomes: The (Non)effects of Diversity

  7. f

    Data_Sheet_1_Children's Acquisition of Homogeneity in Plural Definite...

    • frontiersin.figshare.com
    pdf
    Updated Jun 2, 2023
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    Lyn Tieu; Manuel Križ; Emmanuel Chemla (2023). Data_Sheet_1_Children's Acquisition of Homogeneity in Plural Definite Descriptions.pdf [Dataset]. http://doi.org/10.3389/fpsyg.2019.02329.s001
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    pdfAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Frontiers
    Authors
    Lyn Tieu; Manuel Križ; Emmanuel Chemla
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Plural definite descriptions give rise to homogeneity effects: the positive The trucks are blue and the negative The trucks aren't blue are both neither true nor false when some of the trucks are blue and some are not, that is, when the group of trucks is not homogeneous with respect to the property of being blue (Löbner, 1987, 2000; Schwarzschild, 1994; Križ, 2015b). The only existing acquisition studies related to the phenomenon have examined children's comprehension only of the affirmative versions of such sentences, and moreover have yielded conflicting data; while one study reports that preschoolers interpret definite plurals maximally (Munn et al., 2006, see also Royle et al., 2018), two other studies report that preschoolers allow non-maximal interpretations of definite plurals where adults do not (Karmiloff-Smith, 1979; Caponigro et al., 2012). Moreover, there is no agreed upon developmental trajectory to adult homogeneity. In this paper, we turn to acquisition data to investigate the predictions of a recent analysis of homogeneity that treats homogeneous meanings as the result of a scalar implicature (Magri, 2014). We conducted two experiments targeting 4- and 5-year-old French-speaking children's interpretations of plural definite descriptions in positive and negative sentences, and tested the same children on standard cases of scalar implicature. The experiments revealed three distinct subgroups of children: those who interpreted the plural definite descriptions existentially and failed to compute implicatures; those who both accessed homogeneous interpretations and computed implicatures; and finally, a smaller subgroup of children who appeared to access homogeneous interpretations without computing implicatures. We discuss the implications of our findings, which appear to speak against the implicature theory as the adult-like means of generating homogeneous meanings.

  8. n

    Scripts and data sets associated with: On testing homogeneity of the...

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated May 6, 2024
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    Lars Jermiin; Vivek Jayaswal; John Robinson (2024). Scripts and data sets associated with: On testing homogeneity of the evolutionary process using alignments of homologous sequences [Dataset]. http://doi.org/10.5061/dryad.n8pk0p2xv
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    zipAvailable download formats
    Dataset updated
    May 6, 2024
    Dataset provided by
    Ollscoil na Gaillimhe – University of Galway
    The University of Sydney
    Queensland University of Technology
    Authors
    Lars Jermiin; Vivek Jayaswal; John Robinson
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    In 2019, Genome Biology and Evolution (11:3341-3352) published three statistical tests for assessing whether alignments of genome sequences violate the phylogenetic assumption of evolution under homogeneous conditions. The new tests extend the matched-pairs tests of symmetry, marginal symmetry, and internal symmetry for alignments of n = 2 homologous sequences of nucleotides or amino acids to cases where alignments of n > 2 sequences are considered. Here we discuss the limitations of these new tests and then outline alternative approaches, which permit formal testing of multiple hypotheses (i.e., by controlling either the family-wise error rate or the false discovery rate). We show that the other approaches provide much greater insight into variation of the evolutionary process across lineages, via informal graphical methods and formal statistical procedures. Using one of the procedures (i.e., the Bonferroni test), we show that evolution under heterogeneous conditions is more prevalent than reported in the paper cited above and that the power of the matched-pairs tests of homogeneity is linked to the number of variant sites in an alignment. We release a new version of Homo, a program that allows for formal testing of multiple hypotheses and calculation of adjusted P values. Using Homo, we analysed an alignment of amino acids encoded by 116 flavivirus genomes, and reveal that these viral genomes are unlikely to have evolved under homogeneous conditions. To our knowledge, this is the first time that this has been reported for medically important Flavivirus genomes. Methods This submission contains batch scripts, sequence data, and protocols describing what was done in five computer-based experiments outlined in the manuscript with the above-mentioned title. The format of the submission was chosen such that it is as FAIR compliant as possible (i.e., that the data are Findable, Accessible, Interoperable, and Resuasable). In other words, the research done in the five experiment is reproducible.

    The content in folder Experiment_1 relates to the analysis of the performance of the Maximum Symmetry test. In particular, the experiment was designed to ascertain whether the edge lengths of a tree have an impact on the Type I error rate. The file named 00_README, describe the method used. The content in folder Experiment_2 relates to the analysis of the performance of the Maximum Symmetry test. In particular, the experiment was designed to ascertain whether the edge lengths of a tree have an impact on the Type II error rate. The file named 00_README, describe the method used. The content in folder Experiment_3 relates to the analysis of the performance of the Maximum Symmetry test. In particular, the experiment was designed to ascertain whether alignment length has an impact on the type II error rate. The file named 00_README, describe the method used. The content in folder Experiment_4 relates to the analysis of the performance of four tests concerning the global null hypothesis (H_G) of evolution under SRH conditions. In this case, the tests considered are Bonferroni's (1936) test, Hommel's (1983) test, Simes' (1986) test, and Naser-Khdour et al's (2019) test. The file named 00_README, describe the method used. The content in folder Experiment_5 relates to the analysis of the performance of four tests concerning the global null hypothesis (H_G) of evolution under SRH conditions. In this case, the tests considered are Bonferroni's (1936) test, Hommel's (1983) test, Simes' (1986) test, and Naser-Khdour et al's (2019) test.

    As for the genome data used in Experiment_2, we note:

    The policistronically-encoded amino-acid sequences of 116 flavivirus genomes were retrieved from GenBank (https://www.ncbi.nlm.nih.gov/genbank/) and aligned using MAFFT v7.453 (using ginsi mode) (Mol. Biol. Evol., 30:772-780).

    The completeness of the alignment was surveyed using AliStat v1.13 (NAR Genom. Bioinf., 2:lqaa024) and sites containing ambiguous characters were deleted if the proportion of such characters exceeded 0.2. The resulting alignment of 3,367 sites had a completeness score (Ca) of 0.9880 (for details, see NAR Genom. Bioinf., 2:lqaa024).

    Model selection was done using ModelFinder (Nat. Methods, 14:587-589), which is implemented in IQ-TREE2 v2.1.3 (Mol. Biol. Evol., 37:1530-1534). We only considered substitution models for viral polypeptides. For each model of sequence evolution considered, tree space was searched under the AIC and BIC optimality criteria. The rtREV+FO+I+R9 model was optimal under the AIC and the rtREV+FO+I+R7 model was optimal under the BIC. Using IQ-TREE2, the same tree was identified under the two models.

    The UFBoot2 procedure (Mol. Biol. Evol., 35:518-522) was used to assess the consistency of the phylogenetic signal.

  9. Data from: Failure of the ILD to determine data combinability for slow loris...

    • zenodo.org
    • datadryad.org
    bin
    Updated May 29, 2022
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    Anne D. Yoder; Jodi A. Irwin; Bret A. Payseur; Anne D. Yoder; Jodi A. Irwin; Bret A. Payseur (2022). Data from: Failure of the ILD to determine data combinability for slow loris phylogeny [Dataset]. http://doi.org/10.5061/dryad.656
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    binAvailable download formats
    Dataset updated
    May 29, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anne D. Yoder; Jodi A. Irwin; Bret A. Payseur; Anne D. Yoder; Jodi A. Irwin; Bret A. Payseur
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Tests for incongruence as an indicator of among data partition conflict have played an important role in conditional data combination. When such tests reveal significant incongruence, this has been interpreted as rationale for not combining data in a single phylogenetic analysis. In this study of lorisiform phylogeny, we employ the incongruence length difference (ILD) test to assess conflict among three independent data sets. A large morphological data set and two unlinked molecular data sets, the mitochondrial cytochrome b gene and the nuclear interphotoreceptor retinoid binding protein (exon 1), are analyzed with various optimality criteria and weighting mechanisms in order to determine the phylogenetic relationships among slow lorises (Primates, Loridae). When analyzed separately, the morphological data show impressive statistical support for a monophyletic Loridae. Both molecular data sets resolve the Loridae as paraphyletic, though with different branching order depending on optimality criterion and/or character weighting employed. When the three data partitions are analyzed in various combinations, an inverse relationship between congruence and phylogenetic accuracy is observed. Nearly all combined analyses that recover monophyly indicate strong data partition incongruence (p = 0.00005, in the most extreme case) whereas all analyses that recover paraphyly indicate lack of significant incongruence. Numerous lines of evidence verify that monophyly is the accurate phylogenetic result. Therefore, this study contributes to a growing body of information that affirms that measures of incongruence should not be employed as indicators of data set combinability.

  10. D

    Data from: Drug-Homogeneity Index in Mass-Spectrometry Imaging

    • phys-techsciences.datastations.nl
    csv, ods, png +2
    Updated Sep 1, 2015
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    L. Morosi; L. Morosi (2015). Drug-Homogeneity Index in Mass-Spectrometry Imaging [Dataset]. http://doi.org/10.17026/dans-xxt-94m9
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    png(3180), png(50679), csv(17605), csv(45092), png(22123), csv(13069), png(5532), csv(14023), png(6003), csv(216683), csv(29252), csv(1000000), csv(64587), png(6379), csv(12199), csv(24811), png(68218), csv(14297), csv(26872), csv(13228), png(31093), png(5720), png(5058), png(15712), csv(15394), csv(10157), png(25174), csv(20504), png(18419), csv(14307), csv(15057), csv(12061), csv(24901), csv(17813), png(39860), type/x-r-syntax(2264), csv(10596), csv(39053), csv(9625), csv(62768), png(5559), png(3341), csv(13071), csv(241498), png(21103), csv(17790), csv(34918), png(31997), csv(1562500), csv(21508), csv(11902), png(5119), png(5407), png(4940), csv(7115), png(16508), png(21310), csv(19592), png(9228), png(7917), csv(33531), csv(13733), csv(20359), csv(12813), csv(12330), csv(17034), csv(15625), png(16934), csv(16862), png(3194), csv(15721), csv(31693), csv(13749), csv(24934), csv(133231), csv(36325), png(18530), png(3441), csv(14120), csv(14745), csv(13814), csv(12593), csv(145606), csv(92784), csv(23703), csv(21129), csv(15562), png(5490), csv(13075), csv(13785), png(7181), png(2609), png(3143), png(37285), png(18880), csv(15778), png(16534), png(14589), png(7349), png(6355), csv(19156), png(16050), csv(141495), png(36082), png(7655), png(17609), csv(19650), csv(35738), csv(11188), csv(12349), png(23529), csv(24072), csv(14180), csv(21164), csv(12109), csv(17962), csv(17501), csv(10458), png(5892), csv(18934), csv(25672), csv(21743), png(18525), png(15096), csv(14419), csv(15062), csv(22892), csv(14632), png(3286), csv(15451), csv(17604), csv(14351), csv(21082), csv(15589), csv(98474), csv(152290), csv(48536), csv(9035), csv(12350), png(43824), csv(19788), csv(13471), csv(37373), csv(22350), png(16632), csv(20571), png(6117), csv(18608), csv(18117), png(13759), png(16890), csv(9242), csv(15360), csv(14321), png(3707), csv(17634), csv(17314), png(18546), csv(13488), png(3136), csv(14857), png(3618), csv(20233), png(2867), png(4860), csv(13765), png(6730), csv(17695), csv(43943), png(6425), png(6383), png(19587), csv(33442), csv(32264), png(24172), csv(27715), png(5369), png(16692), csv(17512), csv(9719), png(40582), csv(24472), csv(9565), png(5641), csv(19943), png(20796), csv(11305), png(6829), csv(12836), csv(23691), csv(20134), png(20677), png(15009), ods(31106), csv(32121), csv(41593), csv(14045), csv(19510), csv(7803), csv(18462), csv(17531), png(5995), csv(13125), png(24182), png(25497), csv(14385), csv(18085), csv(19725), csv(13316), png(24784), png(3887), csv(42557), png(9877), png(16884), png(19882), csv(40826), csv(9602), csv(15650), csv(13455), csv(37809), csv(11593), csv(18389), png(14185), csv(37453), csv(31929), png(17898), csv(20481), png(17699), csv(21072), png(3429), csv(17594), csv(22943), png(18654), csv(7933), png(6433), csv(16346), png(90923), png(5556), png(41059), png(30157), csv(13170), png(17389), png(15056), png(10763), csv(20738), png(37134), csv(20536), csv(13878), png(5635), csv(12908), csv(12521), csv(12893), csv(17255), csv(22107), png(20315), png(8077), csv(276375), png(2821), csv(240885), csv(16133), csv(19610), png(6171), png(6101), png(20347), png(14476), csv(18406), png(3235), csv(12693), png(3026), csv(16842), csv(21267), csv(33924), png(16659), csv(18988), csv(8099), png(20188), csv(42614), csv(322732), csv(11585), csv(16418), csv(18063), csv(15920), csv(19393), csv(15155), png(3607), png(6082), png(37164), png(94869), csv(12613), csv(12637), csv(118894), csv(14610), csv(7273), png(7817), csv(19565), png(6012), csv(12270), png(7316), csv(19149), csv(21903), png(20572), png(22874), csv(29198), csv(9886), csv(27500), png(16726), png(3359), csv(12634), csv(13967), csv(10100), png(43363), csv(19912), csv(11935), png(5882), png(26773), csv(30034), csv(18424), png(20818), png(6977), csv(17728), png(5937), csv(22425), csv(15031), png(5275), csv(43619), csv(11623), png(2789), csv(19068), png(27223), csv(10646), png(13449), csv(22859), csv(34585), csv(15134), csv(10587), png(5664), csv(14147), csv(14839), csv(17425), csv(143691), csv(52171), csv(19691), csv(15873), png(18391), png(21579), png(5899), csv(13609), png(6196), csv(19658), csv(16726), csv(7597), csv(15983), csv(19053), csv(12814), csv(16899), csv(15074), csv(12194), png(18217), png(6309), png(22595), csv(20552), png(3012), png(42953), csv(14549), csv(7954), csv(16926), png(22316), csv(15217), csv(18650), csv(12821), png(4883), png(6619), csv(14787), csv(22389), csv(33435), csv(14437), csv(212686), png(6327), csv(29038), png(8853), csv(17076), csv(19738), csv(15531), png(5847), png(26311), csv(11959), png(4832), csv(16134), csv(21924), png(58404), csv(9709), csv(17278), csv(18165), csv(22055), png(6763), png(26012), png(2985), png(21671), csv(16559), csv(32191), csv(11891), png(6400), csv(44743), png(25542), csv(16693), csv(19016), csv(16703), csv(18114), csv(13315), png(19928), png(6388), csv(124441), csv(12263), png(30274), png(17176), png(5424), png(5267), csv(14221), csv(14765), csv(11053), png(2595), csv(26046), png(22002), csv(18040), png(6119), png(40463), png(40616), csv(14185), png(21947), png(14752), png(3340), csv(252563), png(7313), png(6096), csv(24772), png(6798), csv(12376), csv(14133), png(28687), png(19786), csv(158984), csv(18642), csv(31706), csv(17298), csv(25924), csv(14453), png(2048), png(15349), csv(21988), png(5716), csv(33470), csv(37123), csv(12488), csv(18404), csv(11086), png(22584), png(5858), csv(16380), png(3604), csv(19218), png(6587), csv(31733), csv(23630), png(24142), png(8157), csv(18373), csv(36399), csv(19588), csv(29108), csv(16434), csv(13429), png(20502), csv(16649), csv(38349), csv(24294), zip(345698), csv(17793), png(3401), csv(19882), png(17862), csv(20414), csv(17627), csv(34645), png(5724), csv(13347), csv(251809), png(20430), png(6434), csv(20159), csv(23019), png(38069), png(6340), csv(37181), png(21866), csv(13778), csv(263537), csv(20250), png(3544), csv(11125), csv(16263), csv(16775), csv(13694), png(5661), png(18575), png(2964), png(6052), png(5499), csv(14822), png(20790), png(19044), png(29184), png(20125), csv(16039), png(19464), csv(25284), png(13253), png(3116)Available download formats
    Dataset updated
    Sep 1, 2015
    Dataset provided by
    DANS Data Station Phys-Tech Sciences
    Authors
    L. Morosi; L. Morosi
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The data and R-script are part of the published article ‘Drug-Homogeneity Index in Mass-Spectrometry Imaging’. Date Submitted: 2020-02-15

  11. H

    Replication Data for: The long tale of the long tail: The internet as...

    • dataverse.harvard.edu
    Updated Apr 30, 2025
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    James Mellody (2025). Replication Data for: The long tale of the long tail: The internet as counterforce to cultural homogeneity [Dataset]. http://doi.org/10.7910/DVN/SN9ENE
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 30, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    James Mellody
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Replication data for The long tale of the long tail: The internet as counterforce to cultural homogeneity. While raw data is not available, this is a prepared stata file for statistical analysis.

  12. n

    ThermoML Data for: Homogeneity of the water + ethanol + toluene azeotrope at...

    • trc.nist.gov
    json
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    Gomis, V.; Font, A.; Saquete, M. D., ThermoML Data for: Homogeneity of the water + ethanol + toluene azeotrope at 101.3 kPa [Dataset]. http://doi.org/10.1016/j.fluid.2008.01.018.html
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    jsonAvailable download formats
    Authors
    Gomis, V.; Font, A.; Saquete, M. D.
    License

    https://www.nist.gov/open/licensehttps://www.nist.gov/open/license

    Variables measured
    Pressure, kPa; Liquid, Temperature, K; Liquid, Mole fraction - 1 ; Gas, Mole fraction - 3 ; Gas, Mole fraction - 3; Liquid, Mole fraction - 1 ; Liquid, Pressure, kPa; Liquid mixture 1, Temperature, K; Liquid mixture 2, Mole fraction - 1 ; Liquid mixture 1, Mole fraction - 1 ; Liquid mixture 2, and 2 more
    Measurement technique
    CHROM:UFactor:4, Chromatography
    Description

    An all-glass, dynamic recirculating still equipped with an ultrasonic homogenizer has been used in the determination of the vapour liquid equilibrium (VLE) and vapour liquid liquid equilibrium (VLLE). Consistent data for the ternary water + ethanol + toluene system are reported at 101.3 kPa at temperatures in the range of 347 357 K. These data have been compared with previously published VLLE data. The experimental data indicate that a ternary azeotrope is present at 347.60 K. It has been determined experimentally that the ternary azeotrope is homogeneous and not heterogeneous as is claimed in some of the studied references. The experimental results have been used to check the accuracy of the UNIFAC, UNIQUAC and NRTL models, proving that these models predict a bigger heterogeneous region than the experimental one.

  13. f

    MOESM2 of A framework for the identification and classification of...

    • springernature.figshare.com
    txt
    Updated May 31, 2023
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    Ludovico Pinzari; Soumya Mazumdar; Federico Girosi (2023). MOESM2 of A framework for the identification and classification of homogeneous socioeconomic areas in the analysis of health care variation [Dataset]. http://doi.org/10.6084/m9.figshare.7422266.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    figshare
    Authors
    Ludovico Pinzari; Soumya Mazumdar; Federico Girosi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Additional file 2. SA3 dataset.

  14. Z

    Heterogeneous/Homogeneous Change Detection dataset

    • data.niaid.nih.gov
    Updated Nov 21, 2023
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    David Alejandro Jimenez Sierra (2023). Heterogeneous/Homogeneous Change Detection dataset [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8269854
    Explore at:
    Dataset updated
    Nov 21, 2023
    Dataset provided by
    Juan Felipe Florez Ospina
    David Alejandro Jimenez Sierra
    Behnood Rasti
    Hernán Darío Benítez Restrepo
    Joceyn Chanussot
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    "Please if you use this datasets we appreciated that you reference this repository and cite the works related that made possible the generation of this dataset." This change detection datastet has different events, satellites, resolutions and includes both homogeneous/heterogeneous cases. The main idea of the dataset is to bring a benchmark on semantic change detection in remote sensing field.This dataset is the outcome of the following publications:

    @article{ JimenezSierra2022graph,author={Jimenez-Sierra, David Alejandro and Quintero-Olaya, David Alfredo and Alvear-Mu{~n}oz, Juan Carlos and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Florez-Ospina, Juan Felipe and Chanussot, Jocelyn},journal={IEEE Transactions on Geoscience and Remote Sensing},title={Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection},year={2022},volume={60},number={},pages={1-16},doi={10.1109/TGRS.2022.3168126}} @article{ JimenezSierra2020graph,title={Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops},author={Jimenez-Sierra, David Alejandro and Ben{\'i}tez-Restrepo, Hern{\'a}n Dar{\'i}o and Vargas-Cardona, Hern{\'a}n Dar{\'i}o and Chanussot, Jocelyn},journal={Remote Sensing},volume={12},number={17},pages={2683},year={2020},publisher={Multidisciplinary Digital Publishing Institute},doi={10.3390/rs12172683}} @inproceedings{jimenez2021blue,title={Blue noise sampling and Nystrom extension for graph based change detection},author={Jimenez-Sierra, David Alejandro and Ben{\'\i}tez-Restrepo, Hern{\'a}n Dar{\'\i}o and Arce, Gonzalo R and Florez-Ospina, Juan F},booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},ages={2895--2898},year={2021},organization={IEEE},doi={10.1109/IGARSS47720.2021.9555107}} @article{florez2023exploiting,title={Exploiting variational inequalities for generalized change detection on graphs},author={Florez-Ospina, Juan F and Jimenez Sierra, David A and Benitez-Restrepo, Hernan D and Arce, Gonzalo},journal={IEEE Transactions on Geoscience and Remote Sensing}, year={2023},volume={61},number={},pages={1-16},doi={10.1109/TGRS.2023.3322377}} @article{florez2023exploitingxiv,title={Exploiting variational inequalities for generalized change detection on graphs},author={Florez-Ospina, Juan F. and Jimenez-Sierra, David A. and Benitez-Restrepo, Hernan D. and Arce, Gonzalo R},year={2023},publisher={TechRxiv},doi={10.36227/techrxiv.23295866.v1}} In the table on the html file (dataset_table.html) are tabulated all the metadata and details related to each case within the dasetet. The cases with a link, were gathered from those sources and authors, therefore you should refer to their work as well. The rest of the cases or events (without a link), were obtained through the use of open sources such as:

    Copernicus European Space Agency Alaska Satellite Facility (Vertex) Earth Data In addition, we carried out all the processing of the images by using the SNAP toolbox from the European Space Agency. This proccessing involves the following:

    Data co-registration Cropping Apply Orbit (for SAR data) Calibration (for SAR data) Speckle Filter (for SAR data) Terrain Correction (for SAR data) Lastly, the ground truth was obtained from homogeneous images for pre/post events by drawing polygons to highlight the areas where a visible change was present. The images where layout and synchorized to be zoomed over the same are to have a better view of changes. This was an exhaustive work in order to be precise as possible.Feel free to improve and contribute to this dataset.

  15. d

    Data from: Global Homogeneous Response Units

    • search.dataone.org
    • doi.pangaea.de
    Updated Jan 7, 2018
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    Skalsky, Rastislav; Tarasovicová, Zuzana; Balkovic, Juraj; Schmid, Erwin; Fuchs, Michael; Moltchanova, Elena; Kindermann, Georg; Scholtz, Peter; McCallum, Ian; Havlik, Petr; Schneider, Uwe A; Böttcher, Hannes; Fritz, Steffen; Aoki, Kentaro; De Cara, Stéphane; Kraxner, Florian; Leduc, Sylvain; Mosnier, Aline; Sauer, Timm; Obersteiner, Michael (2018). Global Homogeneous Response Units [Dataset]. http://doi.org/10.1594/PANGAEA.775369
    Explore at:
    Dataset updated
    Jan 7, 2018
    Dataset provided by
    PANGAEA Data Publisher for Earth and Environmental Science
    Authors
    Skalsky, Rastislav; Tarasovicová, Zuzana; Balkovic, Juraj; Schmid, Erwin; Fuchs, Michael; Moltchanova, Elena; Kindermann, Georg; Scholtz, Peter; McCallum, Ian; Havlik, Petr; Schneider, Uwe A; Böttcher, Hannes; Fritz, Steffen; Aoki, Kentaro; De Cara, Stéphane; Kraxner, Florian; Leduc, Sylvain; Mosnier, Aline; Sauer, Timm; Obersteiner, Michael
    Description

    The concept of homogenous response units (HRU) was designed as a general concept for the delineation of basic spatial units. Only those characteristics of landscape, which are relatively stable over time (even under climate change) and largely unsusceptible to anthropogenic influence, were selected. The HRU can be seen as a basic spatial framework for the implementation of climate change and land management alternative scenarios into global modeling and therefore is a basic input for delineation of landscape units. HRUs are defined based on classifications of altitude (five classes: 1 (0 - 300m), 2 (300 - 600m), 3 (600 - 1100m), 4 (1100 - 2500m), 5 (> 2500m)), slope (seven classes(degrees): 1 (0 - 3), 2 (3 - 6), 3 (6 - 10), 4 (10 - 15), 5 (15 - 30), 6 (30 - 50), 7 (> 50)) and soil composition (five classes: 1 (sandy), 2 (loamy), 3 (clay), 4 (stony), 5 (peat)). e.g. HRU111 refers to Altitude class 1: 0-300m; Slope class 1: 0-3 degrees; and Soil class 1: sandy. Areas of non-soil are assigned 88. HRUs have a spatial resolution of approximately 10 km**2.

  16. D

    A methodological approach to correlate tumor heterogeneity with drug...

    • phys-techsciences.datastations.nl
    bin, type/x-r-syntax +1
    Updated Sep 1, 2016
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    L Morosi; L Morosi (2016). A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging (MSI) data [Dataset]. http://doi.org/10.17026/dans-26h-kstf
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    type/x-r-syntax(2654), bin(56020), bin(912797728), bin(384), bin(118792), bin(103076800), bin(124700520), bin(196910300), bin(123860220), bin(143775330), bin(122935890), bin(179488080), bin(165062930), bin(422754930), bin(56024), bin(164110590), bin(202344240), bin(314020110), bin(259260560), bin(152682510), type/x-r-syntax(11339), bin(252370100), bin(92433000), bin(157136100), bin(204192900), bin(237653808), bin(250521440), bin(170776970), zip(88647), bin(205873500), bin(150133600), bin(130638640), bin(279427760), bin(192904870), bin(146380260), bin(182849280), bin(167455736), bin(262173600), bin(290463700), bin(90304240), bin(318501710), type/x-r-syntax(2693), bin(149741460), bin(462113964), bin(500146560), bin(183325450), bin(154055000), bin(194109300), bin(487037880), bin(271136800), bin(249457060), bin(219822480)Available download formats
    Dataset updated
    Sep 1, 2016
    Dataset provided by
    DANS Data Station Phys-Tech Sciences
    Authors
    L Morosi; L Morosi
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The dataset folder contains MSI data analyzed in the above paper. The main goal of the study was to use a methodological approach to link tumour heterogeneity with drug homogeneity. The MSI data files in the folder derived from two different cancer cell-lines (ovarian and colon) under two treatment conditions (with and without bevacizumab). Date Submitted: 2020-02-17

  17. d

    Data from: Morphology, molecules, and the phylogenetics of cetaceans

    • search.dataone.org
    • data.niaid.nih.gov
    • +1more
    Updated Apr 19, 2025
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    Sharon L. Messenger; Jimmy A. McGuire (2025). Morphology, molecules, and the phylogenetics of cetaceans [Dataset]. http://doi.org/10.5061/dryad.62
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    Dataset updated
    Apr 19, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Sharon L. Messenger; Jimmy A. McGuire
    Time period covered
    Feb 13, 2008
    Description

    Recent phylogenetic analyses of cetacean relationships based on DNA sequence data have challenged the traditional view that baleen whales (Mysticeti) and toothed whales (Odontoceti) are each monophyletic, arguing instead that baleen whales are the sister group of the odontocete family Physeteridae (sperm whales). We reexamined this issue in light of a morphological data set composed of 207 characters and molecular data sets of published 12S, 16S, and cytochrome b mitochondrial DNA sequences. We reach four primary conclusions: (1) Our morphological data set strongly supports the traditional view of odontocete monophyly; (2) the unrooted molecular and morphological trees are very similar, and most of the conflict results from alternative rooting positions; (3) the rooting position of the molecular tree is sensitive to choice of artiodactyl outgroup taxa and the treatment of two small but ambiguously aligned regions of the 12S and 16S sequences, whereas the morphological root is strongly ...

  18. d

    Data from: Plasticity matches phenotype to local conditions despite genetic...

    • search.dataone.org
    • datadryad.org
    Updated Apr 24, 2025
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    Xavier Bonnet (2025). Plasticity matches phenotype to local conditions despite genetic homogeneity across 13 snake populations [Dataset]. http://doi.org/10.5061/dryad.sxksn031s
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    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Dryad Digital Repository
    Authors
    Xavier Bonnet
    Time period covered
    Jan 1, 2020
    Description

    In a widespread species, a matching of phenotypic traits to local environmental optima is generally attributed to site-specific adaptation. However, the same matching can occur via adaptive plasticity, without requiring genetic differences among populations. Adult sea kraits (Laticauda saintgironsi) are highly philopatric to small islands, but the entire population within the Neo-Caledonian lagoon is genetically homogenous because females migrate to the mainland to lay their eggs at communal sites; recruits disperse before settling, mixing up alleles. Consequently, any adaptive matching between local environments (e.g., prey sizes) and snake phenotypes (e.g., body sizes and relative jaw sizes) must be achieved via phenotypic plasticity rather than spatial heterogeneity in gene frequencies. We sampled 13 snake colonies spread along a ~200km northwest-southeast gradient (N>4,500 individuals) to measure two morphological features that affect maximum ingestible prey size in gape-limited ...

  19. i

    data of teleoperation under homogenous controller

    • ieee-dataport.org
    Updated Nov 8, 2018
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    lingyang hu (2018). data of teleoperation under homogenous controller [Dataset]. https://ieee-dataport.org/documents/data-teleoperation-under-homogenous-controller
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    Dataset updated
    Nov 8, 2018
    Authors
    lingyang hu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A continuous finite-time control scheme is introduced for bilateral teleoperation systems with asymmetric timevarying delays. Specifically

  20. H

    Replication Data for: Increasing homogeneity in global food supplies and the...

    • dataverse.harvard.edu
    xls
    Updated Jun 7, 2019
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    Harvard Dataverse (2019). Replication Data for: Increasing homogeneity in global food supplies and the implications for food security [Dataset]. http://doi.org/10.7910/DVN/HYOWIC
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    xls(11435491)Available download formats
    Dataset updated
    Jun 7, 2019
    Dataset provided by
    Harvard Dataverse
    License
    Time period covered
    1961 - 2009
    Description

    The narrowing of diversity in crop species contributing to the world’s food supplies has been considered a potential threat to food security. However, changes in this diversity have not been quantified globally. We assess trends over the past 50 y in the richness, abundance, and composition of crop species in national food supplies worldwide. Over this period, national per capita food supplies expanded in total quantities of food calories, protein, fat, and weight, with increased proportions of those quantities sourcing from energy-dense foods. At the same time the number of measured crop commodities contributing to national food supplies increased, the relative contribution of these commodities within these supplies became more even, and the dominance of the most significant commodities decreased. As a consequence, national food supplies worldwide became more similar in composition, correlated particularly with an increased supply of a number of globally important cereal and oil crops, and a decline of other cereal, oil, and starchy root species. The increase in homogeneity worldwide portends the establishment of a global standard food supply, which is relatively species-rich in regard to measured crops at the national level, but species-poor globally. These changes in food supplies heighten interdependence among countries in regard to availability and access to these food sources and the genetic resources supporting their production, and give further urgency to nutrition development priorities aimed at bolstering food security. This dataset is based on FAO national per capita data. For information on the value addition of the data please see the Materials and Methods section of the publication.

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Wuyi Wang; Peter C.B. Phillips; Liangjun Su; Wuyi Wang; Peter C.B. Phillips; Liangjun Su (2022). Homogeneity pursuit in panel data models: Theory and application (replication data) [Dataset]. http://doi.org/10.15456/jae.2022327.0706894514

Homogeneity pursuit in panel data models: Theory and application (replication data)

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zip(59915), txt(2453)Available download formats
Dataset updated
Dec 7, 2022
Dataset provided by
ZBW - Leibniz Informationszentrum Wirtschaft
Authors
Wuyi Wang; Peter C.B. Phillips; Liangjun Su; Wuyi Wang; Peter C.B. Phillips; Liangjun Su
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel-CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.

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