cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr38214.v1
title: Federal Justice Statistics Program: Offenders Admitted to Prison, 2017
title: Version 1

description: The data contain records of sentenced offenders committed to the custody of the Bureau of Prisons (BOP) during fiscal year 2017. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, D.C.) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute through 2012. Data from 2013 and on were prepared by Abt Associates.
description: Datasets: DS1: Federal Justice Statistics Program: Offenders Admitted to Prison, 2017
description: Offenders in the custody of the United States Bureau of Prisons during fiscal year 2017.
DOI: 10.5281/zenodo.7236730
title: Inductive Freebase and Wikidata for KG Completion

description: UPD 2.0: Regenerated datasets free of potential test set leakages This repository contains 10 inductive link prediction datasets (graphs only) published in "Inductive Logical Query Answering in Knowledge Graphs" (NeurIPS 2022). 9 datasets (106-550) were created from FB15k-237, the wikikg dataset was created from OGB WikiKG 2 graph. In the datasets, all inference graphs extend training graphs and include new nodes and edges. Dataset numbers indicate a relative size of the inference graph compared to the training graph, e.g., in 175, the number of nodes in the inference graph is 175% compared to the number of nodes in the training graph. The higher the ratio, the more new unseen nodes appear at inference time, the more complex the task is. The Wikikg split has a fixed 133% ratio. Each dataset is a zip archive containing 5 files: train_graph.txt (pt for wikikg) - original training graph val_inference.txt (pt) - inference graph (validation split), new nodes in validation are disjoint with the test inference graph val_predict.txt (pt) - missing edges in the validation inference graph to be predicted. test_intference.txt (pt) - inference graph (test splits), new nodes in test are disjoint with the validation inference graph test_predict.txt (pt) - missing edges in the test inference graph to be predicted; This is a light-weight version of the full datasets for inductive query answering published here: https://zenodo.org/record/7231344 Here, we only provide graph data for training inductive link prediction models. Paper pre-print: https://arxiv.org/abs/2210.08008 The full source code of training/inference models is available at https://github.com/DeepGraphLearning/InductiveQE
description: {"references": ["Galkin et al. Inductive Logical Query Answering in Knowledge Graphs. NeurIPS 2022"]}
DOI: 10.5281/zenodo.7236731
title: Inductive Freebase and Wikidata for KG Completion

description: This repository contains 10 inductive link prediction datasets (graphs only) published in "Inductive Logical Query Answering in Knowledge Graphs" (NeurIPS 2022). 9 datasets (106-550) were created from FB15k-237, the wikikg dataset was created from OGB WikiKG 2 graph. In the datasets, all inference graphs extend training graphs and include new nodes and edges. Dataset numbers indicate a relative size of the inference graph compared to the training graph, e.g., in 175, the number of nodes in the inference graph is 175% compared to the number of nodes in the training graph. The higher the ratio, the more new unseen nodes appear at inference time, the more complex the task is. The Wikikg split has a fixed 133% ratio. Each dataset is a zip archive containing 7 files: train_graph.txt (pt for wikikg) - original training graph val_inference.txt (pt) - inference graph (validation split), new nodes in validation are disjoint with the test inference graph val_predict.txt (pt) - missing edges in the validation inference graph to be predicted. test_intference.txt (pt) - inference graph (test splits), new nodes in test are disjoint with the validation inference graph test_predict.txt (pt) - missing edges in the test inference graph to be predicted; og_mappings.pkl - contains entity2id / relation2id dictionaries mapping local node/relation IDs from a respective dataset to the original fb15k237 / wikikg2 stats.txt - a small file with dataset stats This is a light-weight version of the full datasets for inductive query answering published here: https://zenodo.org/record/7231344 Here, we only provide graph data for training inductive link prediction models. Paper pre-print: https://arxiv.org/abs/2210.08008 The full source code of training/inference models is available at https://github.com/DeepGraphLearning/InductiveQE
description: {"references": ["Galkin et al. Inductive Logical Query Answering in Knowledge Graphs. NeurIPS 2022"]}
DOI: 10.5281/zenodo.7306061
title: Inductive Freebase and Wikidata for KG Completion

description: UPD 2.0: Regenerated datasets free of potential test set leakages This repository contains 10 inductive link prediction datasets (graphs only) published in "Inductive Logical Query Answering in Knowledge Graphs" (NeurIPS 2022). 9 datasets (106-550) were created from FB15k-237, the wikikg dataset was created from OGB WikiKG 2 graph. In the datasets, all inference graphs extend training graphs and include new nodes and edges. Dataset numbers indicate a relative size of the inference graph compared to the training graph, e.g., in 175, the number of nodes in the inference graph is 175% compared to the number of nodes in the training graph. The higher the ratio, the more new unseen nodes appear at inference time, the more complex the task is. The Wikikg split has a fixed 133% ratio. Each dataset is a zip archive containing 5 files: train_graph.txt (pt for wikikg) - original training graph val_inference.txt (pt) - inference graph (validation split), new nodes in validation are disjoint with the test inference graph val_predict.txt (pt) - missing edges in the validation inference graph to be predicted. test_intference.txt (pt) - inference graph (test splits), new nodes in test are disjoint with the validation inference graph test_predict.txt (pt) - missing edges in the test inference graph to be predicted; This is a light-weight version of the full datasets for inductive query answering published here: https://zenodo.org/record/7231344 Here, we only provide graph data for training inductive link prediction models. Paper pre-print: https://arxiv.org/abs/2210.08008 The full source code of training/inference models is available at https://github.com/DeepGraphLearning/InductiveQE
description: {"references": ["Galkin et al. Inductive Logical Query Answering in Knowledge Graphs. NeurIPS 2022"]}
DOI: 10.6073/pasta/fdede7a3e77014cc7eafa7e8840bbe7e
title: North Temperate Lakes LTER: Chemical Limnology of Primary Study Lakes: Nutrients, pH and Carbon 1981 - current

description: Parameters characterizing the nutrient chemistry of the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra, and Fish) are measured at one station in the deepest part of each lake at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. These parameters include total nitrogen, total dissolved nitrogen (only in the northern lakes), nitrate, ammonia, total phosphorus, total dissolved phosphorus (only in the northern lakes), dissolved reactive phosphorus (only in the southern lakes and not in Wingra and Fish after 2003), bicarbonite-reactive filtered and unfiltered silica (both discontinued in 2003), dissolved reactive silica, field pH, air equilibrated pH, total alkalinity, total inorganic carbon, dissolved inorganic carbon, total organic carbon, dissolved organic carbon, and total particulate matter (only in the northern lakes in this data set. Total particulate matter in southern lakes starting in 2000 is available in a separate dataset). Sampling Frequency: monthly during ice-free season - every 6 weeks during ice-covered season for the northern lakes. The southern lakes are similar except that sampling occurs monthly during the fall and typically only once during the winter (depending on ice conditions). Number of sites: 11
DOI: 10.6084/m9.figshare.20288497
title: Reversible Jump PDMP Samplers for Variable Selection

description: A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), has recently shown great promise: they are nonreversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios. However, current PDMP samplers can only sample from posterior densities that are differentiable almost everywhere, which precludes their use for model choice. Motivated by variable selection problems, we show how to develop reversible jump PDMP samplers that can jointly explore the discrete space of models and the continuous space of parameters. Our framework is general: it takes any existing PDMP sampler, and adds two types of trans-dimensional moves that allow for the addition or removal of a variable from the model. We show how the rates of these trans-dimensional moves can be calculated so that the sampler has the correct invariant distribution. We remove a variable from a model when the associated parameter is zero, and this means that the rates of the trans-dimensional moves do not depend on the likelihood. It is, thus, easy to implement a reversible jump version of any PDMP sampler that can explore a fixed model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20288497.v3
title: Reversible Jump PDMP Samplers for Variable Selection

description: A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), has recently shown great promise: they are nonreversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios. However, current PDMP samplers can only sample from posterior densities that are differentiable almost everywhere, which precludes their use for model choice. Motivated by variable selection problems, we show how to develop reversible jump PDMP samplers that can jointly explore the discrete space of models and the continuous space of parameters. Our framework is general: it takes any existing PDMP sampler, and adds two types of trans-dimensional moves that allow for the addition or removal of a variable from the model. We show how the rates of these trans-dimensional moves can be calculated so that the sampler has the correct invariant distribution. We remove a variable from a model when the associated parameter is zero, and this means that the rates of the trans-dimensional moves do not depend on the likelihood. It is, thus, easy to implement a reversible jump version of any PDMP sampler that can explore a fixed model. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.7217655
title: InductiveQE Datasets

description: <strong>InductiveQE datasets</strong> This repository contains 10 inductive complex query answering datasets published in "Inductive Logical Query Answering in Knowledge Graphs" (NeurIPS 2022). 9 datasets (106-550) were created from FB15k-237, the wikikg dataset was created from OGB WikiKG 2 graph. In the datasets, all inference graphs extend training graphs and include new nodes and edges. Dataset numbers indicate a relative size of the inference graph compared to the training graph, e.g., in 175, the number of nodes in the inference graph is 175% compared to the number of nodes in the training graph. The higher the ratio, the more new unseen nodes appear at inference time, the more complex the task is. The Wikikg split has a fixed 133% ratio. Each dataset is a zip archive containing 16 files: train_graph.txt (pt for wikikg) - original training graph val_inference.txt (pt) - inference graph (validation split), new nodes in validation are disjoint with the test inference graph val_predict.txt (pt) - missing edges in the validation inference graph to be predicted. test_intference.txt (pt) - inference graph (test splits), new nodes in test are disjoint with the validation inference graph test_predict.txt (pt) - missing edges in the test inference graph to be predicted. train/valid/test_queries.pkl - queries of the respective split, 14 query types for fb-derived datasets, 9 types for Wikikg (EPFO-only) *_answers_easy.pkl - easy answers to respective queries that do not require predicting missing links but only edge traversal *_answers_hard.pkl - hard answers to respective queries that DO require predicting missing links and against which the final metrics will be computed train_answers_test.pkl - the extended set of answers for training queries on the bigger test graph, most of training queries have at least 1 more new answers. This is supposed to be an inference-only dataset to measure faithfulness of trained models og_mappings.pkl - contains entity2id / relation2id dictionaries mapping local node/relation IDs from a respective dataset to the original fb15k237 / wikikg2 stats.txt - a small file with dataset stats Overall unzipped size of all datasets combined is about 9 GB. Please refer to the paper for the sizes of graphs and the number of queries per graph. The Wikikg dataset is supposed to be evaluated in the inference-only regime being pre-trained solely on simple link prediction, the number of training complex queries is not enough for such a large dataset. The full source code of training/inference models is available at https://github.com/DeepGraphLearning/InductiveQE
description: {"references": ["Galkin, Mikhail et al. Inductive Logical Query Answering in Knowledge Graphs. NeurIPS 2022"]}
DOI: 10.5281/zenodo.7236747
title: Regional cytoarchitecture of the adult and developing mouse enteric nervous system

description: The organization and cellular composition of tissues are key determinants of their biological function. In the mammalian gastrointestinal (GI) tract, the enteric nervous system (ENS) intercalates between muscular and epithelial layers of the gut wall and can control GI function independent of central nervous system (CNS) input. As in the CNS, distinct regions of the GI tract are highly specialized and support diverse functions, yet the regional and spatial organization of the ENS remains poorly characterized.<sup>2</sup> Cellular arrangements, circuit connectivity patterns, and diverse cell types are known to underpin ENS functional complexity and GI function, but enteric neurons are most typically described only as a uniform meshwork of interconnected ganglia. Here, we present a bird’s eye view of the mouse ENS, describing its previously underappreciated cytoarchitecture and regional variation. We visually and computationally demonstrate that enteric neurons are organized in circumferential neuronal stripes. This organization emerges gradually during the perinatal period, with neuronal stripe formation in the small intestine (SI) preceding that in the colon. The width of neuronal stripes varies throughout the length of the GI tract, and distinct neuronal subtypes differentially populate specific regions of the GI tract, with stark contrasts between SI and colon as well as within subregions of each. This characterization provides a blueprint for future understanding of region-specific GI function and identifying ENS structural correlates of diverse GI disorders.
DOI: 10.5281/zenodo.6364010
title: Captive Rearing Success and Critical Thermal Maxima of Bombus griseocollis (Hymenoptera: Apidae): A Candidate for Commercialization?

description: <em>Commercialized bumble bees (Bombus) are primary pollinators of several crops within open field and</em> <em>greenhouse settings. However, B. impatiens is the only species widely available for purchase in North</em> <em>America. As an eastern species, concerns have been expressed over their transportation outside of their</em> <em>native range. Therefore, there is a need to identify regionally appropriate candidates for commercial crop</em> <em>pollination services, especially in the western U.S.A. In this study, we evaluated the commercialization</em> <em>potential of B. griseocollis, a broadly distributed species throughout the U.S.A., by assessing nest initiation</em> <em>and establishment rates of colonies produced from wild-caught gynes, creating a timeline of colony</em> <em>development, and identifying lab-reared workers’ critical thermal maxima (CT</em><em>Max</em><em>) and lethal temperature</em> <em>(ecological death). From 2019 to 2021, 70.6% of the wild-caught B. griseocollis gynes produced brood in a</em> <em>laboratory setting. Of these successfully initiated nests, 74.8% successfully established a nest (produced a</em> <em>worker), providing guidance for future rearing efforts. Additionally, lab-reared workers produced from wildcaught</em> <em>B. griseocollis gynes had an average CT</em><em>Max </em><em>of 43.5°C and an average lethal temperature of 46.4°C,</em> <em>suggesting B. griseocollis can withstand temperatures well above those commonly found in open field</em> <em>and greenhouse settings. Overall, B. griseocollis should continue to be evaluated for commercial purposes</em> <em>throughout the U.S.A.</em>
description: This repository contains data and software used in our manuscript on "Commercialization Potential of Bombus griseocollis (Hymenoptera: Apidae): Evaluating Captive Rearing Success and Critical Thermal Maxima."
DOI: 10.5281/zenodo.6364009
title: Captive Rearing Success and Critical Thermal Maxima of Bombus griseocollis (Hymenoptera: Apidae): A Candidate for Commercialization?

description: <em>Commercialized bumble bees (Bombus) are primary pollinators of several crops within open field and</em> <em>greenhouse settings. However, B. impatiens is the only species widely available for purchase in North</em> <em>America. As an eastern species, concerns have been expressed over their transportation outside of their</em> <em>native range. Therefore, there is a need to identify regionally appropriate candidates for commercial crop</em> <em>pollination services, especially in the western U.S.A. In this study, we evaluated the commercialization</em> <em>potential of B. griseocollis, a broadly distributed species throughout the U.S.A., by assessing nest initiation</em> <em>and establishment rates of colonies produced from wild-caught gynes, creating a timeline of colony</em> <em>development, and identifying lab-reared workers’ critical thermal maxima (CT</em><em>Max</em><em>) and lethal temperature</em> <em>(ecological death). From 2019 to 2021, 70.6% of the wild-caught B. griseocollis gynes produced brood in a</em> <em>laboratory setting. Of these successfully initiated nests, 74.8% successfully established a nest (produced a</em> <em>worker), providing guidance for future rearing efforts. Additionally, lab-reared workers produced from wildcaught</em> <em>B. griseocollis gynes had an average CT</em><em>Max </em><em>of 43.5°C and an average lethal temperature of 46.4°C,</em> <em>suggesting B. griseocollis can withstand temperatures well above those commonly found in open field</em> <em>and greenhouse settings. Overall, B. griseocollis should continue to be evaluated for commercial purposes</em> <em>throughout the U.S.A.</em>
description: This repository contains data and software used in our manuscript on "Commercialization Potential of Bombus griseocollis (Hymenoptera: Apidae): Evaluating Captive Rearing Success and Critical Thermal Maxima."
DOI: 10.5517/ccdc.csd.cc29734t
title: CCDC 2121985: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Olga Yu. Vassilyeva|2022|CSD Communication|||
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: 10.6084/m9.figshare.8187602
title: Aurora kinase B-phosphorylated HP1α functions in chromosomal instability

description: Heterochromatin Protein 1 α (HP1α) associates with members of the chromosome passenger complex (CPC) during mitosis, at centromeres where it is required for full Aurora Kinase B (AURKB) activity. Conversely, recent reports have identified AURKB as the major kinase responsible for phosphorylation of HP1α at Serine 92 (S92) during mitosis. Thus, the current study was designed to better understand the functional role of this posttranslationally modified form of HP1α. We find that S92-phosphorylated HP1α is generated in cells at early prophase, localizes to centromeres, and associates with regulators of chromosome stability, such as Inner Centromere Protein, INCENP. In mouse embryonic fibroblasts, HP1α knockout alone or reconstituted with a non-phosphorylatable (S92A) HP1α mutant results in mitotic chromosomal instability characterized by the formation of anaphase/telophase chromatin bridges and micronuclei. These effects are rescued by exogenous expression of wild type HP1α or a phosphomimetic (S92D) variant. Thus, the results from the current study extend our knowledge of the role of HP1α in chromosomal stability during mitosis.
DOI: 10.6084/m9.figshare.8187602.v1
title: Aurora kinase B-phosphorylated HP1α functions in chromosomal instability

description: Heterochromatin Protein 1 α (HP1α) associates with members of the chromosome passenger complex (CPC) during mitosis, at centromeres where it is required for full Aurora Kinase B (AURKB) activity. Conversely, recent reports have identified AURKB as the major kinase responsible for phosphorylation of HP1α at Serine 92 (S92) during mitosis. Thus, the current study was designed to better understand the functional role of this posttranslationally modified form of HP1α. We find that S92-phosphorylated HP1α is generated in cells at early prophase, localizes to centromeres, and associates with regulators of chromosome stability, such as Inner Centromere Protein, INCENP. In mouse embryonic fibroblasts, HP1α knockout alone or reconstituted with a non-phosphorylatable (S92A) HP1α mutant results in mitotic chromosomal instability characterized by the formation of anaphase/telophase chromatin bridges and micronuclei. These effects are rescued by exogenous expression of wild type HP1α or a phosphomimetic (S92D) variant. Thus, the results from the current study extend our knowledge of the role of HP1α in chromosomal stability during mitosis.