cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.j512f21p
title: Data from: Compliance with mandatory reporting of clinical trial results on ClinicalTrials.gov: cross sectional study

description: OBJECTIVE: To examine compliance with mandatory reporting of summary
 clinical trial results (within one year of completion of trial) on
 ClinicalTrials.gov for studies that fall under the recent Food and Drug
 Administration Amendments Act (FDAAA) legislation. DESIGN: Registry based
 study of clinical trial summaries. DATA SOURCES: ClinicalTrials.gov,
 searched on 19 January 2011, with cross referencing with Drugs@FDA to
 determine for which trials mandatory reporting was required within one
 year. SELECTION CRITERIA: Studies registered on ClinicalTrials.gov with US
 sites which completed between 1 January and 31 December 2009. MAIN OUTCOME
 MEASURE: Proportion of trials for which results had been reported.
 RESULTS: The ClinicalTrials.gov registry contained 83,579 entries for
 interventional trials, of which 5642 were completed within the timescale
 of interest. We identified trials as falling within the mandatory
 reporting rules if they were covered by the FDAAA (trials of a drug,
 device, or biological agent, which have at least one US site, and are of
 phase II or later) and if they investigated a drug that already had
 approval from the Food and Drug Administration. Of these, 163/738 (22%)
 had reported results within one year of completion of the trial compared
 with 76/727 (10%) trials that were not subject to mandatory reporting (95%
 confidence interval for the difference in proportions 7.8% to 15.5%; χ2
 test, P=2.6x10^-9). Later phase trials were more likely to report results
 (P=4.4x 10^-11), as were industry funded trials (P=2.2 x10^-16).
 CONCLUSION: Most trials subject to mandatory reporting did not report
 results within a year of completion.
description: clinicaltrials.gov_searchThis is complete original dataset.identify completed trialsThis is the R script which when run on "clinicaltrials.gov_search.txt" will produce a .csv file which lists all the completed trials.FDA_table_with_sensThis is the final dataset after cross referencing the trials. An explanation of the variables is included in the supplementary file "2011-10-31 Prayle Hurley Smyth Supplementary file 3 variables in the dataset".analysis_after_FDA_categorization_and_sensThis R script reproduces the analysis from the paper, including the tables and statistical tests. The comments should make it self explanatory.2011-11-02 prayle hurley smyth supplementary file 1 STROBE checklistThis is a STROBE checklist for the study2011-10-31 Prayle Hurley Smyth Supplementary file 2 examples of categorizationThis is a supplementary file which illustrates some of the decisions which had to be made when categorizing trials.2011-10-31 Prayle Hurley Smyth Supplementary file 3 variables in the datasetThis file lists and describes the sources of all the variables used in the final analysis.
DOI: 10.5517/ccdc.csd.cc2phf0n
title: CCDC 2487471: Experimental Crystal Structure Determination

description: Related Article: Yunping Zhoujin, Michael Anim Safo, Navindra Keerthisinghe, Mark D. Smith, Sophya Garashchuk, Hans-Conrad zur Loye|2026|Solid State Sciences||108228|doi:10.1016/j.solidstatesciences.2026.108228
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2phf1p
title: CCDC 2487472: Experimental Crystal Structure Determination

description: Related Article: Yunping Zhoujin, Michael Anim Safo, Navindra Keerthisinghe, Mark D. Smith, Sophya Garashchuk, Hans-Conrad zur Loye|2026|Solid State Sciences||108228|doi:10.1016/j.solidstatesciences.2026.108228
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2phf2q
title: CCDC 2487473: Experimental Crystal Structure Determination

description: Related Article: Yunping Zhoujin, Michael Anim Safo, Navindra Keerthisinghe, Mark D. Smith, Sophya Garashchuk, Hans-Conrad zur Loye|2026|Solid State Sciences||108228|doi:10.1016/j.solidstatesciences.2026.108228
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5061/dryad.36g21
title: Data from: Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models

description: BAMM (Bayesian Analysis of Macroevolutionary Mixtures) is a statistical
 framework that uses reversible jump MCMC to infer complex
 macroevolutionary dynamics of diversification and phenotypic evolution on
 phylogenetic trees. A recent article by Moore and coauthors (MEA) reported
 a number of theoretical and practical concerns with BAMM. Major claims
 from MEA are that (1) BAMM's likelihood function is incorrect,
 because it does not account for unobserved rate shifts; (2) the posterior
 distribution on the number of rate shifts is overly sensitive to the
 prior; and (3) diversification rate estimates from BAMM are unreliable.
 Here, we show that these and other conclusions from MEA are generally
 incorrect or unjustified. We first demonstrate that MEA's numerical
 assessment of the BAMM likelihood is compromised by their use of an
 invalid likelihood function. We then show that “unobserved rate shifts”
 appear to be irrelevant for biologically-plausible parameterizations of
 the diversification process. We find that the purportedly extreme prior
 sensitivity reported by MEA cannot be replicated with standard usage of
 BAMM v2.5, or with any other version, when conventional Bayesian model
 selection is performed. Finally, we demonstrate that BAMM performs very
 well at estimating diversification rate variation across the ∼20% of
 simulated trees in MEA's dataset for which it is theoretically
 possible to infer rate shifts with confidence. Due to ascertainment bias,
 the remaining 80% of their purportedly variable-rate phylogenies are
 statistically indistinguishable from those produced by a constant-rate
 birth-death process and were thus poorly-suited for the summary statistics
 used in their performance assessment. We demonstrate that inferences about
 diversification rates have been accurate and consistent across all major
 previous releases of the BAMM software. We recognize an acute need to
 address the theoretical foundations of rate-shift models for phylogenetic
 trees, and we expect BAMM and other modeling frameworks to improve in
 response to mathematical and computational innovations. However, we remain
 optimistic that that the imperfect tools currently available to
 comparative biologists have provided and will continue to provide
 important insights into the diversification of life on Earth.
description: Supplementary
 Information: BAMM AssessmentSupplementaryFeb13.zip
DOI: 10.5517/ccdc.csd.cc2phzv1
title: CCDC 2488024: Experimental Crystal Structure Determination

description: Related Article: Yanfei Liu, Yuzhen Ma, Pei‐Zhi Liu, Chen Shen, Shouyi Cen, Yuan‐Yuan Zhu, Qian Peng, Zhipeng Zhang|2026|Angew.Chem.,Int.Ed.|65|e14745|doi:10.1002/anie.202514745
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pj6v9
title: CCDC 2488241: Experimental Crystal Structure Determination

description: Related Article: Fei- Yu Zhang, Lu Gan, Xin-Yi Hu, Guang-Hai Zhu, Yi-Ling Liao, Guo-Sen Huang, De-an Guo, Wei Li|2026|J.Mol.Struct.|1357||doi:10.1016/j.molstruc.2026.145333
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pjl94
title: CCDC 2488596: Experimental Crystal Structure Determination

description: Related Article: Jing Xi, An-Zhi Huang, Yi-Fei Deng, Yi-Quan Zhang, Yuan-Zhu Zhang|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04614
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pjlf8
title: CCDC 2488600: Experimental Crystal Structure Determination

description: Related Article: Jing Xi, An-Zhi Huang, Yi-Fei Deng, Yi-Quan Zhang, Yuan-Zhu Zhang|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04614
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5061/dryad.1375s
title: Data from: Causes and consequences of genetic background effects illuminated by integrative genomic analysis

description: The phenotypic consequences of individual mutations are modulated by the
 wild-type genetic background in which they occur. Although such background
 dependence is widely observed, we do not know whether general patterns
 across species and traits exist, nor about the mechanisms underlying it.
 We also lack knowledge on how mutations interact with genetic background
 to influence gene expression, and how this in turn mediates mutant
 phenotypes. Furthermore, how genetic background influences patterns of
 epistasis remains unclear. To investigate the genetic basis and genomic
 consequences of genetic background dependence of the scallopedE3 allele on
 the Drosophila melanogaster wing, we generated multiple novel genome-level
 datasets from a mapping-by-introgression experiment and a tagged RNA gene
 expression dataset. In addition we used whole genome re-sequencing of the
 parental lines—two commonly used laboratory strains—to predict polymorphic
 transcription factor binding sites for SD. We integrated these data with
 previously published genomic datasets from expression microarrays and a
 modifier mutation screen. By searching for genes showing a congruent
 signal across multiple datasets, we were able to identify a robust set of
 candidate loci contributing to the background-dependent effects of
 mutations in sd. We also show that the majority of background-dependent
 modifiers previously reported are caused by higher-order epistasis, not
 quantitative non-complementation. These findings provide a useful
 foundation for more detailed investigations of genetic background
 dependence in this system, and this approach is likely to prove useful in
 exploring the genetic basis of other traits as well.
description: Scripts/data zip
 archiveZip file contains all
 analysis scripts and some raw/intermediate/final data files. All remaining
 original raw data are deposited at the NCBI SRA, and remaining
 intermediate files can be reconstructed from the raw data using the
 scripts included in this package. See readme for
 details.CHC_SC_DT_ID_Dryad.zip
DOI: 10.5517/ccdc.csd.cc2pkq11
title: CCDC 2489673: Experimental Crystal Structure Determination

description: Related Article: Alyson MacKay, Joseph Zsombor-Pindera, Farshid Effaty, Nooshin Sheibany, Federica Gennarini, Maryam Habibian, Fei Chen, Mohammad S. Askari, Mykhailo Brytskyi, Scott Kroeker, Pierre Kennepohl, Xavier Ottenwaelder|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04490
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pkq22
title: CCDC 2489674: Experimental Crystal Structure Determination

description: Related Article: Alyson MacKay, Joseph Zsombor-Pindera, Farshid Effaty, Nooshin Sheibany, Federica Gennarini, Maryam Habibian, Fei Chen, Mohammad S. Askari, Mykhailo Brytskyi, Scott Kroeker, Pierre Kennepohl, Xavier Ottenwaelder|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04490
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pl3l0
title: CCDC 2490062: Experimental Crystal Structure Determination

description: Related Article: Yuxin Guo, Hanru Tang, Zhiyan Liu, Zhiping Wang, Long-Yong Xie, Jian Wen|2026|Org.Chem.Front.|||doi:10.1039/d5qo01607j
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pl4v9
title: CCDC 2490101: Experimental Crystal Structure Determination

description: Related Article: Xin Li, Yu-Jia Liu, Li-Ying Sun, Can Chen, Man Li, Xiao-Xu Liu, Ying-Feng Han
|2025|Angew.Chem.,Int.Ed.|65|e22286|doi:10.1002/anie.202522286
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pl4wb
title: CCDC 2490102: Experimental Crystal Structure Determination

description: Related Article: Xin Li, Yu-Jia Liu, Li-Ying Sun, Can Chen, Man Li, Xiao-Xu Liu, Ying-Feng Han
|2025|Angew.Chem.,Int.Ed.|65|e22286|doi:10.1002/anie.202522286
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pl4xc
title: CCDC 2490103: Experimental Crystal Structure Determination

description: Related Article: Xin Li, Yu-Jia Liu, Li-Ying Sun, Can Chen, Man Li, Xiao-Xu Liu, Ying-Feng Han
|2025|Angew.Chem.,Int.Ed.|65|e22286|doi:10.1002/anie.202522286
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2pl9cz
title: CCDC 2490241: Experimental Crystal Structure Determination

description: Related Article: Aparna V M, Vidya L, Neelima S, Aparna Raj, Vandana S, G Anilkumar, Anabha E R, C Sudarsanakumar|2026|J.Mol.Struct.|1358||doi:10.1016/j.molstruc.2026.145329
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5061/dryad.53bj8
title: Data from: Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice

description: To ensure food security in the face of population growth, decreasing water
 and land for agriculture, and increasing climate variability, crop yields
 must increase faster than the current rates. Increased yields will require
 implementing novel approaches in genetic discovery and breeding. Here we
 demonstrate the potential of field-based high throughput phenotyping (HTP)
 on a large recombinant population of rice to identify genetic variation
 underlying important traits. We find that detecting quantitative trait
 loci (QTL) with HTP phenotyping is as accurate and effective as
 traditional labor-intensive measures of flowering time, height, biomass,
 grain yield, and harvest index. Genetic mapping in this population,
 derived from a cross of an modern cultivar (IR64) with a landrace
 (Aswina), identified four alleles with negative effect on grain yield that
 are fixed in IR64, demonstrating the potential for HTP of large
 populations as a strategy for the second green revolution.
description: AllDataTanger2017
DOI: 10.5061/dryad.32b87rn
title: Data from: The cost of being big: local competition, importance of dispersal and experimental evolution of reversal to unicellularity

description: Multicellularity provides multiple benefits. Nonetheless, unicellularity
 is ubiquitous and there have been multiple cases of evolutionary reversal
 to a unicellular organization. In this paper, we explore some of the costs
 of multicellularity as well as the possibility and dynamics of
 evolutionary reversals to unicellularity. We hypothesize that recently
 evolved multicellular organisms would face a high cost of increased
 competition for local resources in spatially structured environments
 because of larger size and increased cell densities. To test this
 hypothesis we conducted competition assays, computer simulations, and
 selection experiments using isolates of Saccharomyces cerevisiae that
 recently evolved multicellularity. In well-mixed environments,
 multicellular isolates had lower growth rates relative to their
 unicellular ancestor due to limitations of space and resource acquisition.
 In structured environments with localized resources, cells in both
 multicellular and unicellular isolates grew at a similar rate. Despite
 similar growth, higher local density of cells in multicellular groups led
 to increased competition and higher fitness costs in spatially structured
 environments. In structured environments all of the multicellular isolates
 rapidly evolved a predominantly unicellular life cycle, while in
 well-mixed environments reversal was more gradual. Taken together, these
 results suggest that a lack of dispersal, leading to higher local
 competition, might have been one of the main constraints in the evolution
 of early multicellular forms.
description: Directory with cluster
 size measurmentsData from reversibility
 experiment and R code to analyze data and make figure
 5.Biomass.zipFitnessCostsDirectory with data and R code to make figure 2. (Competition assays). README file (biomass) contains metadata for all the files.GrowthR code and data from growth curves and BSA standards. For figures 1 and A2 and estimating the growth parameters used in the spatial simulations.SpatialModelCode for model simulations and data to calculate mean number of cells in multicellular clusters.
DOI: 10.5061/dryad.j01ph32
title: Data from: Phylogenomic relationships of diploids and the origins of Allotetraploids in Dactylorhiza (Orchidaceae)

description: Disentangling phylogenetic relationships proves challenging for groups
 that have evolved recently, especially if there is ongoing reticulation.
 Although they are in most cases immediately isolated from diploid
 relatives, sets of sibling allopolyploids often hybridize with each other,
 thereby increasing the complexity of an already challenging situation.
 Dactylorhiza (Orchidaceae: Orchidinae) is a genus much affected by
 allopolyploid speciation and reticulate phylogenetic relationships. Here
 we use genetic variation at tens of thousands of genomic positions to
 unravel the convoluted evolutionary history of Dactylorhiza. We first
 investigate circumscription and relationships of diploid species in the
 genus using coalescent and maximum likelihood methods, and then group 16
 allotetraploids by maximum affiliation to their putative parental
 diploids, implementing a method based on genotype likelihoods. The
 direction of hybrid crosses is inferred for each allotetraploid using
 information from maternally inherited plastid RADseq loci. Starting from
 age estimates of parental taxa, the relative ages of these allotetraploid
 entities are inferred by quantifying their genetic similarity to the
 diploids and numbers of private alleles compared with sibling
 allotetraploids. Whereas northwestern Europe is dominated by young
 allotetraploids of postglacial origins, comparatively older
 allotetraploids are distributed further south, where climatic conditions
 remained relatively stable during the Pleistocene glaciations. Our
 bioinformatics approach should prove effective for the study of other
 naturally occurring, non-model, polyploid plant complexes.
description: Phylip4DiploidsFigure3.phylip.txtSFiguresSupplementary FiguresSupplementaryTablesSupplementary TablespipelineBioinformatics pipelineBayeasian Factor Delimitation of species for fuchsii-maculataxml filebfd_fuc.xmlBFD for incarnata-euxinaxml file for Bayeasian Factor Delimitation of speciesbfd_inc.xmlPolyRel_2xInput for PolyRelatedness for diploidsPolyRel_4xInput file for PolyRelatedness for allotetraploids.PolyRel_allInput file for PolyRelatedness for all individualsPolyRel_plastidInput for PolyRelatedness for the RADseq locai that localize on the plastid genomesnappSNAPP species treessnapp_MarInput xml file for SNAPPSFigure1.phyPhylip file for RAxML analysis