cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6393681
title: Clean-Clean ER Datasets with FastText embeddings

description: Please see version 5 for more datasets. This one contains only a print-out of the DBPedia dataset in CSV format.
Each line corresponds to a different entity profile and has the following structure (where n is number of attributes):
numerical_id , uri , n ,  aname_0 , aval_0 , aname_1 , aval_1 ,...aname_n , aval_n  
That is, the separator is "space,space".
Special thanks to moemode for providing the dataset.
DOI: 10.5281/zenodo.6393682
title: Clean-Clean ER Datasets with FastText embeddings

description: This is a collection of 11 real-world datasets for Clean-Clean ER. Every dataset contains the aggregate attribute values per entity (i.e., a concatenation of all individual attribute values) as well as the value for the most frequent and distinctive attribute. In both cases, the corresponding pre-trained fastText embeddings are available, too. The datasets are the following: D1: Restaurants D2: Abt-Buy D3: Amazon-Google Products D4: DBLP-ACM D5: IMDB-TMDB D6: IMDB-TVDB D7: TMDB-TVDB D8: Amazon-Walmart D9: DBLP-Scholar D10: Movies For more details, please refer to ContinuousFilteringBenchmark and to the corresponding technical report. The 11th datasets comprises two versions of the DBPedia English Infoboxes. Please refer to this publication for more details.
DOI: 10.5281/zenodo.6400475
title: Clean-Clean ER Datasets with FastText embeddings

description: This is a collection of 11 real-world datasets for Clean-Clean ER. Every dataset contains the aggregate attribute values per entity (i.e., a concatenation of all individual attribute values) as well as the value for the most frequent and distinctive attribute. In both cases, the corresponding pre-trained fastText embeddings are available, too. The datasets are the following: D1: Restaurants D2: Abt-Buy D3: Amazon-Google Products D4: DBLP-ACM D5: IMDB-TMDB D6: IMDB-TVDB D7: TMDB-TVDB D8: Amazon-Walmart D9: DBLP-Scholar D10: Movies For more details, please refer to ContinuousFilteringBenchmark and to the corresponding technical report. The 11th datasets comprises two versions of the DBPedia English Infoboxes. Please refer to this publication for more details.
DOI: 10.5281/zenodo.6950980
title: Clean-Clean ER Datasets with FastText embeddings

description: This is a collection of 11 real-world datasets for Clean-Clean ER. Every dataset contains the aggregate attribute values per entity (i.e., a concatenation of all individual attribute values) as well as the value for the most frequent and distinctive attribute. In both cases, the corresponding pre-trained fastText embeddings are available, too. The datasets are the following: D1: Restaurants D2: Abt-Buy D3: Amazon-Google Products D4: DBLP-ACM D5: IMDB-TMDB D6: IMDB-TVDB D7: TMDB-TVDB D8: Amazon-Walmart D9: DBLP-Scholar D10: Movies For more details, please refer to ContinuousFilteringBenchmark and to the corresponding technical report. The 11th datasets comprises two versions of the DBPedia English Infoboxes. Please refer to this publication for more details. We have also added 7 synthetic datasets for Dirty ER of increasing size, which are ideal for scalability analyses. These datasets, which contain demographic information, are the following (their names indicate their size): D10K D50K D100K D200K D300K D1M D2M At the moment, the repository contains the original datasets in JSO format. The datasets in CSV format along with their fastText embeddings are currently available here.
DOI: 10.5281/zenodo.7460624
title: Clean-Clean ER Datasets with FastText embeddings

description: This is a collection of 11 real-world datasets for Clean-Clean ER. Every dataset contains the aggregate attribute values per entity (i.e., a concatenation of all individual attribute values) as well as the value for the most frequent and distinctive attribute. In both cases, the corresponding pre-trained fastText embeddings are available, too. The datasets are the following: D1: Restaurants D2: Abt-Buy D3: Amazon-Google Products D4: DBLP-ACM D5: IMDB-TMDB D6: IMDB-TVDB D7: TMDB-TVDB D8: Amazon-Walmart D9: DBLP-Scholar D10: Movies For more details, please refer to ContinuousFilteringBenchmark and to the corresponding technical report. The 11th datasets comprises two versions of the DBPedia English Infoboxes. Please refer to this publication for more details. We have also added 7 synthetic datasets for Dirty ER of increasing size, which are ideal for scalability analyses. These datasets, which contain demographic information, are the following (their names indicate their size): D10K D50K D100K D200K D300K D1M D2M At the moment, the repository contains the original datasets in JSO format. The datasets in CSV format along with their fastText embeddings are currently available here.
DOI: 10.5281/zenodo.8433873
title: Clean-Clean ER Datasets with FastText embeddings

description: This is a collection of 11 real-world datasets for Clean-Clean ER. Every dataset contains the aggregate attribute values per entity (i.e., a concatenation of all individual attribute values) as well as the value for the most frequent and distinctive attribute. In both cases, the corresponding pre-trained fastText embeddings are available, too. The datasets are the following: D1: Restaurants D2: Abt-Buy D3: Amazon-Google Products D4: DBLP-ACM D5: IMDB-TMDB D6: IMDB-TVDB D7: TMDB-TVDB D8: Amazon-Walmart D9: DBLP-Scholar D10: Movies For more details, please refer to ContinuousFilteringBenchmark and to the corresponding technical report. The 11th datasets comprises two versions of the DBPedia English Infoboxes. Please refer to this publication for more details. We have also added 7 synthetic datasets for Dirty ER of increasing size, which are ideal for scalability analyses. These datasets, which contain demographic information, are the following (their names indicate their size): D10K D50K D100K D200K D300K D1M D2M At the moment, the repository contains the original datasets in JSO format. The datasets in CSV format along with their fastText embeddings are currently available here.
DOI: 10.5281/zenodo.10059061
title: TrackFM Datasets
DOI: 10.5281/zenodo.10059172
title: AGIMUS EAB workshop
DOI: 10.5281/zenodo.10059173
title: AGIMUS EAB workshop
DOI: 10.5281/zenodo.10059189
title: Gradients 3 KM1906 UCYN-A2&A3 nifH Gene Abundances

description: This dataset is a quantification of the UCYN-A2&A3 nifH gene using digital droplet Polymerase Chain Reaction (ddPCR). Samples were collected during the April 2019 KM1906/Gradients 3 cruise that traveled from Hawaii to the North Pacific and back.
DOI: 10.5281/zenodo.10059188
title: Gradients 3 KM1906 UCYN-A2&A3 nifH Gene Abundances

description: This dataset is a quantification of the UCYN-A2&A3 nifH gene using digital droplet Polymerase Chain Reaction (ddPCR). Samples were collected during the April 2019 KM1906/Gradients 3 cruise that traveled from Hawaii to the North Pacific and back.
DOI: 10.5281/zenodo.10059107
title: Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations.

description: In the present study, we describe the results from a computational microscopy perspective (also known as molecular dynamics simulation) at the atomistic resolution for the two gonadotropin hormone receptors, the follicle-stimulant hormone receptor and the luteinizing/chorionic gonadotropin hormone receptor, which are essential for reproduction in humans.
DOI: 10.5281/zenodo.10059194
title: AGIMUS Interviews with experts in AI ethics
DOI: 10.5281/zenodo.10059193
title: AGIMUS Interviews with experts in AI ethics
DOI: 10.5281/zenodo.10059212
title: Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations.

description: In the present study, we describe the results from a computational microscopy perspective (also known as molecular dynamics simulation) at the atomistic resolution for the two gonadotropin hormone receptors, the follicle-stimulant hormone receptor and the luteinizing/chorionic gonadotropin hormone receptor, which are essential for reproduction in humans.
DOI: 10.5281/zenodo.10059106
title: Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations.

description: In the present study, we describe the results from a computational microscopy perspective (also known as molecular dynamics simulation) at the atomistic resolution for the two gonadotropin hormone receptors, the follicle-stimulant hormone receptor and the luteinizing/chorionic gonadotropin hormone receptor, which are essential for reproduction in humans.
DOI: 10.7910/dvn/tjbnug
title: Replication Data for: "Política externa no governo Bolsonaro (2019-2021): disputas discursivas e rupturas institucionais nas políticas de gênero"

description: Data from the article "Política externa no governo Bolsonaro (2019-2021): disputas discursivas e rupturas institucionais nas políticas de gênero", by Fabiane Simioni and Gabriela Kyrillos, published in Revista DADOS, v. 67, n. 4 de 2024.
DOI: 10.6084/m9.figshare.24470059
title: <b><i>Alcyonidium kuklinskii</i></b><b> sp. nov., a new species of Antarctic ctenostome bryozoan with a key to all Antarctic species of the genus</b>

description: <b>Abstract:</b>Recent surveys of Antarctic waters in the Terra Nova Bay revealed numerous bryozoan species including ctenostome bryozoans. Whereas cheilostome bryozoans are well-studied in these latitudes, ctenostomes remain highly neglected. Large ctenostomes are easily addressed by their lack of calcified skeletons, but this lack also renders them difficult and tedious to identify. As a result, histology and reconstructions of internal soft tissues is required to classify this group of bryozoans. Thanks to the availability of new specimens from Terra Nova Bay, a detailed analysis of growth form, gut morphology and tentacle number of two colonies, initially ascribed to the ctenostome bryozoan genus <i>Alcyonidum </i>Lamouroux, 1813, turned out to be a new species, <i>Alcyonidium kukliskii</i> sp. nov., which we described in this study. These specimens were also barcoded (COI) and sequences compared to available ones. Together with the new species described here, a total of ten species of <i>Alcyonidium</i> is known for the Southern Ocean, accounting for one eight of the entire genus diversity. All Southern Ocean species appear to be endemic. In order to speed the identification of the Antarctic <i>Alcyonidium</i> species we provide an identification key and a distribution map of all type species. In brief, colony morphology, zooidal size and in particular tentacle number represent the most suitable characters for identifying species within this genus.
DOI: 10.6084/m9.figshare.24470059.v1
title: <b><i>Alcyonidium kuklinskii</i></b><b> sp. nov., a new species of Antarctic ctenostome bryozoan with a key to all Antarctic species of the genus</b>

description: <b>Abstract:</b>Recent surveys of Antarctic waters in the Terra Nova Bay revealed numerous bryozoan species including ctenostome bryozoans. Whereas cheilostome bryozoans are well-studied in these latitudes, ctenostomes remain highly neglected. Large ctenostomes are easily addressed by their lack of calcified skeletons, but this lack also renders them difficult and tedious to identify. As a result, histology and reconstructions of internal soft tissues is required to classify this group of bryozoans. Thanks to the availability of new specimens from Terra Nova Bay, a detailed analysis of growth form, gut morphology and tentacle number of two colonies, initially ascribed to the ctenostome bryozoan genus <i>Alcyonidum </i>Lamouroux, 1813, turned out to be a new species, <i>Alcyonidium kukliskii</i> sp. nov., which we described in this study. These specimens were also barcoded (COI) and sequences compared to available ones. Together with the new species described here, a total of ten species of <i>Alcyonidium</i> is known for the Southern Ocean, accounting for one eight of the entire genus diversity. All Southern Ocean species appear to be endemic. In order to speed the identification of the Antarctic <i>Alcyonidium</i> species we provide an identification key and a distribution map of all type species. In brief, colony morphology, zooidal size and in particular tentacle number represent the most suitable characters for identifying species within this genus.
DOI: 10.21227/8kfb-b104
title: CAlving Fronts and where to Find thEm: a benchmark dataset and methodology for automatic glacier calving front extraction from sar imagery

description: The temporal variability in calving front positions of marine-terminating glaciers permits inference on the frontal ablation. Frontal ablation, the sum of the calving rate and the melt rate at the terminus, significantly contributes to the mass balance of glaciers. Therefore, the glacier area has been declared as an Essential Climate Variable product by the World Meteorological Organization. The presented dataset provides the necessary information for training deep learning techniques to automate the process of calving front delineation. The dataset includes Synthetic Aperture Radar (SAR) images of seven glaciers distributed around the globe. Five of them are located in Antarctica: Crane, Dinsmoore-Bombardier-Edgeworth, Mapple, Jorum and the Sjörgen-Inlet Glacier. The remaining glaciers are the Jakobshavn Isbrae Glacier in Greenland and the Columbia Glacier in Alaska. Several images were taken for each glacier, forming a time series. The time series lie in the time span between 1995 and 2020. The images have different spatial resolutions, as they were captured by different satellites. The satellites used are Sentinel-1, TerraSAR-X, TanDEM-X, ENVISAT, European Remote Sensing Satellite 1&2, ALOS PALSAR, and RADARSAT-1. Along with the SAR images, two types of labels are provided so that deep learning techniques can be trained in a supervised manner. One label provides the position of the calving front. The other label shows the position of different landscape regions comprising glacier, rock outcrop, ocean including ice-melange, and an area where no information is available consisting of SAR shadows, layover regions, and areas outside the swath. The two labels allow different approaches to calving front delineation, as the calving front can be extracted from landscape region predictions during post-processing. As additional information for post-processing, the dataset includes bounding boxes for the dynamic calving front for each image. This bounding box excludes nearly static calving fronts also visible in the images, which are not of interest but would still be predicted as calving fronts by deep learning techniques. Hence, all front predictions outside this bounding box can be excluded during post-processing. To ensure the generalizability of the trained deep learning techniques to new unseen glaciers, the dataset is split into a training and an out-of-sample test set. The latter shall only be used to test the performance of the trained front delineation algorithm after all hyperparameters are optimized. The test set comprises the time series of Mapple and Columbia. More information on the dataset and how to use it can be found in the related paper.
DOI: 10.6084/m9.figshare.24470044.v1
title: Spatio-temporal variability of sea surface temperature on the Portuguese coast

description: Monitoring ocean surface temperature is critical in detecting potential climatic changes and characterizing ocean temperature behavior. Understanding the climatological trends and anomalies of sea surface temperatures is essential to comprehend the long-term effects of climate change on marine ecosystems and coastal regions. The collected dataset contains data from seven buoys of Ondograph and Meteo-oceanography types anchored offshore and along the Portuguese coast to acquire ocean surface temperatures. The data gathered provides a considerable temporal window, with an average interval of 14 years per buoy, enabling the creation of data series and the implementation of data mining algorithms to develop decision support systems. Collecting data <i>in situ</i> makes it possible to validate simulated results obtained using approximation models. This allows for more accurate temperature readings and facilitates testing and correcting created models.
DOI: 10.6084/m9.figshare.24470113
title: Mushrooms

description: The images have been collected from the iNaturalist website.
DOI: 10.6084/m9.figshare.24470113.v1
title: Mushrooms

description: The images have been collected from the iNaturalist website.
DOI: 10.5281/zenodo.8025871
title: Data archive: CICT for single cell RNA-seq network inference

description: This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions" (Shojaee and Huang, Brief in Bioinform 2023. DOI: 10.1093/bib/bbad370). The CICT code is available at the GitHub repo (https://github.com/hlab1/scRNAseqWithCICT/).
The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021), SCENIC (Aibar et al., Nat Methods 2017), Inferelator 3.0 (Gibbs et al., Bioinformatics 2022), and CellOracle (Kamimoto et al., Nature 2023). The output directory names are (subdirectories within each dataset):
* CICT_ewMIshrink_RFmaxdepth10_RFntrees20/: CICT for simulated data* CICT_v2/: CICT for experimental data* CELLORACLEDB/: CellOracle for experimental data* DEEPDRIM72_ewMIshrink_RFmaxdepth10_RFntrees20/: DEEPDRIM for simulated data* DEEPDRIM72_v2/: DEEPDRIM for experimental data* INFERELATOR38_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-Prior for simulated data* INFERELATOR38_v2/: Inferelator-Prior for experimental data* INFERELATOR34_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-NoPrior for experimental data* INFERELATOR34_v2/: Inferelator-NoPrior for experimental data* GENIE3/: GENIE3* GRNBOOST2/: GRNBOST2* LEAP/: LEAP* PIDC/: PIDC* PPCOR/: PPCOR* SCENICDB/: SCENIC for experimental data* SCNS/: SCNS* SCODE/: SCODE* SCRIBE/: SCRIBE* SINCERITIES/: SINCERITIES* SINGE/: SINGE* RANDOM/: RANDOM
The methods were benchmarked against two kinds of scRNA-seq datasets:* Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts with shape parameter k=6.5 and rate parameter q=10, 30, 50, 70, 80. * Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, with three types of ground truth networks.    * Evaluation levels:        * L0: 500 highly varying genes plus TFs        * L1: 1000 highly varying genes plus TFs        * L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1.    * Types of ground truths:        * Cell-type-specific ChIP-seq ground truth (L0, L1, L2)        * Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns)        * Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof)
The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For complete details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).
DOI: 10.5281/zenodo.8025872
title: Data archive: CICT for single cell RNA-seq network inference

description: This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data using Causal Inference with Composition of Transactions" (Shojaee and Huang, 2023). The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021) and Inferelator 3.0 (Gibbs et al., Bioinformatics 2022). The output directory names are: CICT_v2/: CICT DEEPDRIM72_v2/: DEEPDRIM INFERELATOR38_v2/: Inferelator-Prior INFERELATOR34_v2/: Inferelator-NoPrior GENIE3/: GENIE3 GRNBOOST2/: GRNBOST2 LEAP/: LEAP PIDC/: PIDC PPCOR/: PPCOR SCNS/: SCNS SCODE/: SCODE SCRIBE/: SCRIBE SINCERITIES/: SINCERITIES SINGE/: SINGE RANDOM/: RANDOM The methods were benchmarked against two kinds of scRNA-seq datasets. Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts. Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, and three types of ground truth networks: Evaluation levels: L0: 500 highly varying genes plus TFs L1: 1000 highly varying genes plus TFs L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1. Types of ground truths: Cell-type-specific ChIP-seq ground truth (L0, L1, L2) Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns) Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof) The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).