heiDATA

Metrics

187,706 Downloads

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Subject: Computer and Information Science

21 to 30 of 84 Results

Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review Jan 4, 2020 - Medical Informatics Benning, Nils-Hendrik; Hagen, Niclas; Knaup, Petra, 2020, "Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review", https://doi.org/10.11588/data/JRVJGN, heiDATA, V1, UNF:6:f63Fk7qwB3+Tc3vOEqFdnA== [fileUNF] This dataset contains the results (publication references) of the systematic review "Current Use of Sensor-Based Measurements for Paraplegics", presented at MIE 2020, Geneva.
Selectional Preference Embeddings (EMNLP 2017) Jan 31, 2019 - AIPHES Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1 Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...
Real-World PP Attachment Disambiguation Dataset Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines, 2023, "Real-World PP Attachment Disambiguation Dataset", https://doi.org/10.11588/data/NB46XR, heiDATA, V1 This resource contains a German dataset for real-world PP attachment disambiguation. The creation, analysis and experiment results of the dataset are described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited"
Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data] Mar 7, 2022 - Theoretical Physics Li, Kunhe; Oiwa, Nestor Norio; Cordeiro, Claudette E.; Heermann, Dieter W., 2022, "Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data]", https://doi.org/10.11588/data/RDISCE, heiDATA, V1 The file contains the CTCF-DNA binding sites for complete genome of Homo sapiens (human), Mus musculus (mouse), Sus scrofa (pig), Capra hircus (goat), Aedes aegypti (dengue and yellow fever mosquito) and Drosophila melanogaster (fruit fly) using electronic nucleotide alignment. T...
Pre-trained POS tagging models for German social media Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1 Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...
PatTR: Patent Translation Resource Jun 16, 2014 - Statistical Natural Language Processing Group Wäschle, Katharina; Riezler, Stefan, 2014, "PatTR: Patent Translation Resource", https://doi.org/10.11588/data/10002, heiDATA, V3 PatTR is a sentence-parallel corpus extracted from the MAREC patent collection. The current version contains more than 22 million German-English and 18 million French-English parallel sentences collected from all patent text sections as well as 5 million German-French sentence pa...
OwnReality API-only web application Oct 26, 2020 - OwnReality. To Each His Own Reality Schepp, Moritz, 2020, "OwnReality API-only web application", https://doi.org/10.11588/data/KZHLS8, heiDATA, V1 This dataset contains the data platform for the research project "OwnReality. To Each His Own Reality". During the course of the project, data was gathered and entered into a database. In general, this platform allows the integration of that data into web based systems such as co...
Opinion role extractor Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1 System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text
Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset] Jul 27, 2021 - Institute of Pathology Mannheim Runz, Marlen; Weis, Cleo-Aron, 2021, "Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]", https://doi.org/10.11588/data/8LKEZF, heiDATA, V1 Here we provide the data sets supporting the experiments in our publication Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks, which were collected at the Institute of Pathology, Medical Faculty Mannheim, Heidelberg University....
Neural-Guided RANSAC for Estimating Epipolar Geometry [Data] Sep 7, 2020 - IWR Visual Learning Lab Brachmann, Eric, 2020, "Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]", https://doi.org/10.11588/data/PCGYET, heiDATA, V1 Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac

Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review

Jan 4, 2020 - Medical Informatics

Benning, Nils-Hendrik; Hagen, Niclas; Knaup, Petra, 2020, "Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review", https://doi.org/10.11588/data/JRVJGN, heiDATA, V1, UNF:6:f63Fk7qwB3+Tc3vOEqFdnA== [fileUNF]

This dataset contains the results (publication references) of the systematic review "Current Use of Sensor-Based Measurements for Paraplegics", presented at MIE 2020, Geneva.

Selectional Preference Embeddings (EMNLP 2017)

Jan 31, 2019 - AIPHES

Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1

Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...

Real-World PP Attachment Disambiguation Dataset

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines, 2023, "Real-World PP Attachment Disambiguation Dataset", https://doi.org/10.11588/data/NB46XR, heiDATA, V1

This resource contains a German dataset for real-world PP attachment disambiguation. The creation, analysis and experiment results of the dataset are described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited"

Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data]

Mar 7, 2022 - Theoretical Physics

Li, Kunhe; Oiwa, Nestor Norio; Cordeiro, Claudette E.; Heermann, Dieter W., 2022, "Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data]", https://doi.org/10.11588/data/RDISCE, heiDATA, V1

The file contains the CTCF-DNA binding sites for complete genome of Homo sapiens (human), Mus musculus (mouse), Sus scrofa (pig), Capra hircus (goat), Aedes aegypti (dengue and yellow fever mosquito) and Drosophila melanogaster (fruit fly) using electronic nucleotide alignment. T...

Pre-trained POS tagging models for German social media

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1

Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...

PatTR: Patent Translation Resource

Jun 16, 2014 - Statistical Natural Language Processing Group

Wäschle, Katharina; Riezler, Stefan, 2014, "PatTR: Patent Translation Resource", https://doi.org/10.11588/data/10002, heiDATA, V3

PatTR is a sentence-parallel corpus extracted from the MAREC patent collection. The current version contains more than 22 million German-English and 18 million French-English parallel sentences collected from all patent text sections as well as 5 million German-French sentence pa...

OwnReality API-only web application

Oct 26, 2020 - OwnReality. To Each His Own Reality

Schepp, Moritz, 2020, "OwnReality API-only web application", https://doi.org/10.11588/data/KZHLS8, heiDATA, V1

This dataset contains the data platform for the research project "OwnReality. To Each His Own Reality". During the course of the project, data was gathered and entered into a database. In general, this platform allows the integration of that data into web based systems such as co...

Opinion role extractor

Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1

System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text

Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]

Jul 27, 2021 - Institute of Pathology Mannheim

Runz, Marlen; Weis, Cleo-Aron, 2021, "Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]", https://doi.org/10.11588/data/8LKEZF, heiDATA, V1

Here we provide the data sets supporting the experiments in our publication Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks, which were collected at the Institute of Pathology, Medical Faculty Mannheim, Heidelberg University....

Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]

Sep 7, 2020 - IWR Visual Learning Lab

Brachmann, Eric, 2020, "Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]", https://doi.org/10.11588/data/PCGYET, heiDATA, V1

Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac

Add Data

Share Dataverse

Link Dataverse

Reset Modifications