cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21258/1720044
title: Swiss national forest inventory - Result table No. 899378
DOI: 10.21258/1703579
title: Swiss national forest inventory - Result table No. 891945
DOI: 10.21258/1676429
title: Swiss national forest inventory - Result table No. 898917
DOI: 10.21258/1678326
title: Swiss national forest inventory - Result table No. 897210
DOI: 10.6084/m9.figshare.13440161
title: Supplementary Figure1

description: Forty-seven isolated Jurkat clones were screened for HDR-edited TRIM5 gene by PCR using a mutated TRIM5 specific primer. The presence of a 157 bp band indicates that the primer could bind and that the clone was modified. Six clones have passed this pre-screen step for 13% of modification.
DOI: 10.6084/m9.figshare.13440161.v1
title: Supplementary Figure1

description: Forty-seven isolated Jurkat clones were screened for HDR-edited TRIM5 gene by PCR using a mutated TRIM5 specific primer. The presence of a 157 bp band indicates that the primer could bind and that the clone was modified. Six clones have passed this pre-screen step for 13% of modification.
DOI: 10.21258/1719168
title: Swiss national forest inventory - Result table No. 897467
DOI: 10.21258/1688862
title: Swiss national forest inventory - Result table No. 902077
DOI: 10.21258/1688789
title: Swiss national forest inventory - Result table No. 897506
DOI: 10.21258/1707437
title: Swiss national forest inventory - Result table No. 899012
DOI: 10.21258/1697140
title: Swiss national forest inventory - Result table No. 899144
DOI: 10.21258/1703260
title: Swiss national forest inventory - Result table No. 898950
DOI: 10.21258/1711375
title: Swiss national forest inventory - Result table No. 897395
DOI: 10.21258/1696423
title: Swiss national forest inventory - Result table No. 897396
DOI: 10.21258/1673278
title: Swiss national forest inventory - Result table No. 899296
DOI: 10.21258/1690791
title: Swiss national forest inventory - Result table No. 891974
DOI: 10.21258/1676010
title: Swiss national forest inventory - Result table No. 897554
DOI: 10.25646/7766
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.21258/1680622
title: Swiss national forest inventory - Result table No. 893018
DOI: 10.21258/1689025
title: Swiss national forest inventory - Result table No. 898501
DOI: 10.21258/1704156
title: Swiss national forest inventory - Result table No. 891830
DOI: 10.21258/1690545
title: Swiss national forest inventory - Result table No. 897362
DOI: 10.21258/1683024
title: Swiss national forest inventory - Result table No. 892006
DOI: 10.6084/m9.figshare.13456028
title: Baseline survey data

description: Data collected in a baseline study of a project testing the effect of a community intervention on sexual and gender-based violence.
DOI: 10.21258/1689461
title: Swiss national forest inventory - Result table No. 893458
DOI: 10.21258/1694300
title: Swiss national forest inventory - Result table No. 897458
DOI: 10.21258/1699044
title: Swiss national forest inventory - Result table No. 898097
DOI: 10.21258/1702278
title: Swiss national forest inventory - Result table No. 898711
DOI: 10.5281/zenodo.11574
title: test collection with random binary data

description: This is a collection with three files containing random binary data.
DOI: 10.21258/1716314
title: Swiss national forest inventory - Result table No. 899662
DOI: 10.21258/1696909
title: Swiss national forest inventory - Result table No. 892945
DOI: 10.21258/1720710
title: Swiss national forest inventory - Result table No. 892068
DOI: 10.21258/1683574
title: Swiss national forest inventory - Result table No. 891867
DOI: 10.21258/1713752
title: Swiss national forest inventory - Result table No. 891901
DOI: 10.5281/zenodo.4361779
title: kgbench: dmgfull and dmg832k

description: Graph neural networks and other machine learning models offer a promising direction for interpretable machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of of multimodal information to learn from. Here, we introduce a set of new benchmark tasks for node classification on knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models, instead requiring the model to pool information from several steps away in the graph. However, the datasets may also be used for link prediction. For each dataset, we provide test and validation sets of at least 1000 instances, with some containing more than 10\;000 instances. Each task can be performed in a purely relational manner, to evaluate the performance of a relational graph model in isolation, or with multimodal information, to evaluate the performance of multimodal relational graph models. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into \texttt{numpy} and \texttt{pytorch}. We compute performance for several baseline models.
DOI: 10.5281/zenodo.4361778
title: kgbench: dmgfull and dmg832k

description: Graph neural networks and other machine learning models offer a promising direction for interpretable machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of of multimodal information to learn from. Here, we introduce a set of new benchmark tasks for node classification on knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models, instead requiring the model to pool information from several steps away in the graph. However, the datasets may also be used for link prediction. For each dataset, we provide test and validation sets of at least 1000 instances, with some containing more than 10\;000 instances. Each task can be performed in a purely relational manner, to evaluate the performance of a relational graph model in isolation, or with multimodal information, to evaluate the performance of multimodal relational graph models. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into \texttt{numpy} and \texttt{pytorch}. We compute performance for several baseline models.
DOI: 10.5281/zenodo.4361795
title: kgbench: mdgenre and mdgender

description: Graph neural networks and other machine learning models offer a promising direction for interpretable machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of of multimodal information to learn from. Here, we introduce a set of new benchmark tasks for node classification on knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models, instead requiring the model to pool information from several steps away in the graph. However, the datasets may also be used for link prediction. For each dataset, we provide test and validation sets of at least 1000 instances, with some containing more than 10\;000 instances. Each task can be performed in a purely relational manner, to evaluate the performance of a relational graph model in isolation, or with multimodal information, to evaluate the performance of multimodal relational graph models. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into \texttt{numpy} and \texttt{pytorch}. We compute performance for several baseline models.
DOI: 10.5281/zenodo.4361794
title: kgbench: mdgenre and mdgender

description: Graph neural networks and other machine learning models offer a promising direction for interpretable machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of of multimodal information to learn from. Here, we introduce a set of new benchmark tasks for node classification on knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models, instead requiring the model to pool information from several steps away in the graph. However, the datasets may also be used for link prediction. For each dataset, we provide test and validation sets of at least 1000 instances, with some containing more than 10\;000 instances. Each task can be performed in a purely relational manner, to evaluate the performance of a relational graph model in isolation, or with multimodal information, to evaluate the performance of multimodal relational graph models. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into \texttt{numpy} and \texttt{pytorch}. We compute performance for several baseline models.
DOI: 10.21258/1690529
title: Swiss national forest inventory - Result table No. 899243
DOI: 10.5281/zenodo.4361787
title: kgbench: dblp

description: Graph neural networks and other machine learning models offer a promising direction for interpretable machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of of multimodal information to learn from. Here, we introduce a set of new benchmark tasks for node classification on knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models, instead requiring the model to pool information from several steps away in the graph. However, the datasets may also be used for link prediction. For each dataset, we provide test and validation sets of at least 1000 instances, with some containing more than 10\;000 instances. Each task can be performed in a purely relational manner, to evaluate the performance of a relational graph model in isolation, or with multimodal information, to evaluate the performance of multimodal relational graph models. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into \texttt{numpy} and \texttt{pytorch}. We compute performance for several baseline models.
DOI: 10.5281/zenodo.4361786
title: kgbench: dblp

description: Graph neural networks and other machine learning models offer a promising direction for interpretable machine learning on relational and multimodal data. Until now, however, progress in this area is difficult to gauge. This is primarily due to a limited number of datasets with (a) a high enough number of labeled nodes in the test set for precise measurement of performance, and (b) a rich enough variety of of multimodal information to learn from. Here, we introduce a set of new benchmark tasks for node classification on knowledge graphs. We focus primarily on node classification, since this setting cannot be solved purely by node embedding models, instead requiring the model to pool information from several steps away in the graph. However, the datasets may also be used for link prediction. For each dataset, we provide test and validation sets of at least 1000 instances, with some containing more than 10\;000 instances. Each task can be performed in a purely relational manner, to evaluate the performance of a relational graph model in isolation, or with multimodal information, to evaluate the performance of multimodal relational graph models. All datasets are packaged in a CSV format that is easily consumable in any machine learning environment, together with the original source data in RDF and pre-processing code for full provenance. We provide code for loading the data into \texttt{numpy} and \texttt{pytorch}. We compute performance for several baseline models.
DOI: 10.21258/1700902
title: Swiss national forest inventory - Result table No. 891728
DOI: 10.21258/1711638
title: Swiss national forest inventory - Result table No. 899473
DOI: 10.5281/zenodo.4370760
title: Conjunto de dados "Competências e Empregabilidade"

description: Este conjunto de dados contém 108 casos (incluindo <em>missing values</em>) e está disponível em vários formatos aqui &lt;https://doi.org/10.5281/zenodo.4370760&gt; e contém dados reais coletados com alunos de graduação (Administração, Contabilidade ou Economia) recém-formados ou que estavam no final do curso. Esta pesquisa foi desenvolvida na disciplina Métodos Quantitativos de Pesquisa em Administração ministrada pelo autor no Programa de Pós-graduação em Administração de Empresas da Universidade Presbiteriana Mackenzie. A ideia geral foi avaliar se havia relação entre as competências desenvolvidas durante o curso de graduação e a empregabilidade, e para mensurar esses construtos foram usadas as seguintes escalas: GODOY, A. S.; ANTONELLO, C. S.; BIDO, D. S.; SILVA, D. DA. O desenvolvimento das competências de alunos formandos do curso de Administração: um estudo de modelagem de equações estruturais. <em>RAUSP - Revista de Administração</em> da Universidade de São Paulo, v. 44, n. 3, p. 265-278, 2009. CAMPOS, K. C. L. <em>Construção de uma Escala de Empregabilidade: competências e habilidades pessoais, escolares e organizacionais. </em>Tese de Doutorado. São Paulo: Instituto de Psicologia da Universidade de São Paulo, 2006.
DOI: 10.5281/zenodo.4370759
title: Conjunto de dados "Competências e Empregabilidade"

description: Este conjunto de dados contém 108 casos (incluindo <em>missing values</em>) e está disponível em vários formatos aqui &lt;https://doi.org/10.5281/zenodo.4370760&gt; e contém dados reais coletados com alunos de graduação (Administração, Contabilidade ou Economia) recém-formados ou que estavam no final do curso. Esta pesquisa foi desenvolvida na disciplina Métodos Quantitativos de Pesquisa em Administração ministrada pelo autor no Programa de Pós-graduação em Administração de Empresas da Universidade Presbiteriana Mackenzie. A ideia geral foi avaliar se havia relação entre as competências desenvolvidas durante o curso de graduação e a empregabilidade, e para mensurar esses construtos foram usadas as seguintes escalas: GODOY, A. S.; ANTONELLO, C. S.; BIDO, D. S.; SILVA, D. DA. O desenvolvimento das competências de alunos formandos do curso de Administração: um estudo de modelagem de equações estruturais. <em>RAUSP - Revista de Administração</em> da Universidade de São Paulo, v. 44, n. 3, p. 265-278, 2009. CAMPOS, K. C. L. <em>Construção de uma Escala de Empregabilidade: competências e habilidades pessoais, escolares e organizacionais. </em>Tese de Doutorado. São Paulo: Instituto de Psicologia da Universidade de São Paulo, 2006.
DOI: 10.21258/1697928
title: Swiss national forest inventory - Result table No. 898107
DOI: 10.21258/1679569
title: Swiss national forest inventory - Result table No. 891752
DOI: 10.21258/1721388
title: Swiss national forest inventory - Result table No. 897798
DOI: 10.21258/1683804
title: Swiss national forest inventory - Result table No. 897179
DOI: 10.21258/1711992
title: Swiss national forest inventory - Result table No. 898161
DOI: 10.6084/m9.figshare.13456274
title: Mid-term evaluation data

description: Data collected in a mid-term evaluation of a project testing the effect of a community intervention on sexual and gender-based violence.
DOI: 10.6084/m9.figshare.13456274.v1
title: Mid-term evaluation data

description: Data collected in a mid-term evaluation of a project testing the effect of a community intervention on sexual and gender-based violence.
DOI: 10.21258/1681633
title: Swiss national forest inventory - Result table No. 902182
DOI: 10.21258/1714005
title: Swiss national forest inventory - Result table No. 891689
DOI: 10.34740/kaggle/dsv/1764342
title: US counties COVID 19 dataset
DOI: 10.21258/1714695
title: Swiss national forest inventory - Result table No. 898184
DOI: 10.21258/1697651
title: Swiss national forest inventory - Result table No. 898053
DOI: 10.21258/1678288
title: Swiss national forest inventory - Result table No. 896935
DOI: 10.21258/1699189
title: Swiss national forest inventory - Result table No. 897507
DOI: 10.21258/1695796
title: Swiss national forest inventory - Result table No. 899226
DOI: 10.21258/1720391
title: Swiss national forest inventory - Result table No. 899059
DOI: 10.21258/1707979
title: Swiss national forest inventory - Result table No. 902137
DOI: 10.21258/1722204
title: Swiss national forest inventory - Result table No. 891910
DOI: 10.21258/1687065
title: Swiss national forest inventory - Result table No. 891981
DOI: 10.21258/1717582
title: Swiss national forest inventory - Result table No. 897935
DOI: 10.21258/1683457
title: Swiss national forest inventory - Result table No. 891823
DOI: 10.21258/1722576
title: Swiss national forest inventory - Result table No. 898022
DOI: 10.21258/1690112
title: Swiss national forest inventory - Result table No. 892056
DOI: 10.21258/1711701
title: Swiss national forest inventory - Result table No. 898892
DOI: 10.21258/1706331
title: Swiss national forest inventory - Result table No. 891925
DOI: 10.21258/1678140
title: Swiss national forest inventory - Result table No. 896988
DOI: 10.21258/1716164
title: Swiss national forest inventory - Result table No. 897673
DOI: 10.21258/1701770
title: Swiss national forest inventory - Result table No. 902033
DOI: 10.21258/1679537
title: Swiss national forest inventory - Result table No. 899457
DOI: 10.21258/1687550
title: Swiss national forest inventory - Result table No. 897790
DOI: 10.21258/1704672
title: Swiss national forest inventory - Result table No. 897693
DOI: 10.21258/1695191
title: Swiss national forest inventory - Result table No. 897891
DOI: 10.21258/1692068
title: Swiss national forest inventory - Result table No. 898702
DOI: 10.21258/1679239
title: Swiss national forest inventory - Result table No. 898675
DOI: 10.21258/1678207
title: Swiss national forest inventory - Result table No. 891876
DOI: 10.21258/1695837
title: Swiss national forest inventory - Result table No. 897834
DOI: 10.21258/1687686
title: Swiss national forest inventory - Result table No. 897751
DOI: 10.21258/1678753
title: Swiss national forest inventory - Result table No. 893445
DOI: 10.21258/1697345
title: Swiss national forest inventory - Result table No. 897967
DOI: 10.21258/1707831
title: Swiss national forest inventory - Result table No. 898511
DOI: 10.21258/1711631
title: Swiss national forest inventory - Result table No. 892054
DOI: 10.21258/1701328
title: Swiss national forest inventory - Result table No. 902085
DOI: 10.21258/1706745
title: Swiss national forest inventory - Result table No. 897867
DOI: 10.21258/1712040
title: Swiss national forest inventory - Result table No. 892021
DOI: 10.21258/1681762
title: Swiss national forest inventory - Result table No. 899644
DOI: 10.21258/1680595
title: Swiss national forest inventory - Result table No. 902257
DOI: 10.21258/1674229
title: Swiss national forest inventory - Result table No. 892044
DOI: 10.21258/1676604
title: Swiss national forest inventory - Result table No. 898202
DOI: 10.21258/1695091
title: Swiss national forest inventory - Result table No. 891969
DOI: 10.21258/1702064
title: Swiss national forest inventory - Result table No. 897213
DOI: 10.21258/1676552
title: Swiss national forest inventory - Result table No. 897574
DOI: 10.21258/1699147
title: Swiss national forest inventory - Result table No. 899435
DOI: 10.21258/1712780
title: Swiss national forest inventory - Result table No. 897139
DOI: 10.21258/1695744
title: Swiss national forest inventory - Result table No. 897275
DOI: 10.21258/1722898
title: Swiss national forest inventory - Result table No. 899545
DOI: 10.21258/1699641
title: Swiss national forest inventory - Result table No. 891911
DOI: 10.21258/1699767
title: Swiss national forest inventory - Result table No. 897471
DOI: 10.21258/1719362
title: Swiss national forest inventory - Result table No. 892030
DOI: 10.21258/1706157
title: Swiss national forest inventory - Result table No. 897838
DOI: 10.21258/1700843
title: Swiss national forest inventory - Result table No. 893027
DOI: 10.21258/1674324
title: Swiss national forest inventory - Result table No. 897239
DOI: 10.21258/1706029
title: Swiss national forest inventory - Result table No. 899436
DOI: 10.21258/1707611
title: Swiss national forest inventory - Result table No. 897761
DOI: 10.21258/1701453
title: Swiss national forest inventory - Result table No. 897380
DOI: 10.5281/zenodo.4372720
title: Supplemetary files

description: Supplemetary files
DOI: 10.5281/zenodo.4372719
title: Supplemetary files

description: Supplemetary files
DOI: 10.21258/1698921
title: Swiss national forest inventory - Result table No. 899257
DOI: 10.21258/1678924
title: Swiss national forest inventory - Result table No. 897224
DOI: 10.21258/1674441
title: Swiss national forest inventory - Result table No. 899288
DOI: 10.5281/zenodo.4372812
title: Supplemetary files

description: Supplemetary files
DOI: 10.5281/zenodo.4372811
title: Supplemetary files

description: Supplemetary files
DOI: 10.21258/1707187
title: Swiss national forest inventory - Result table No. 898871
DOI: 10.21258/1694431
title: Swiss national forest inventory - Result table No. 897074
DOI: 10.21258/1705400
title: Swiss national forest inventory - Result table No. 899638
DOI: 10.5281/zenodo.4372876
title: Supplemetary files

description: Supplemetary files
DOI: 10.5281/zenodo.4372875
title: Supplemetary files

description: Supplemetary files
DOI: 10.21258/1680766
title: Swiss national forest inventory - Result table No. 898715
DOI: 10.21258/1722906
title: Swiss national forest inventory - Result table No. 899623
DOI: 10.21258/1682107
title: Swiss national forest inventory - Result table No. 897719
DOI: 10.21258/1698070
title: Swiss national forest inventory - Result table No. 897929
DOI: 10.21258/1683997
title: Swiss national forest inventory - Result table No. 899300
DOI: 10.21258/1702903
title: Swiss national forest inventory - Result table No. 892036
DOI: 10.21258/1687512
title: Swiss national forest inventory - Result table No. 891788
DOI: 10.21258/1691676
title: Swiss national forest inventory - Result table No. 892062
DOI: 10.21258/1688835
title: Swiss national forest inventory - Result table No. 897203