cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8405296
title: META DATA FOR ENSEMBLE PROJECT

description: ### META DATA FOR ENSEMBLE PROJECT ### This dataset contains raw field measurements and laboratory results of field surveys and experiments performed in the ENSEMBLE project. <br> For more information on the ENSEMBLE Project and related publications please see: https://www.epfl.ch/labs/river/ensemble/ This dataset contains data and metadata for field surveys conducted in June and August 2019 (ENSEMBLE_2019_Campaign) and data and metadata on a global-change microbiology experiment performed during summer 2020 (ENSEMBLE_2020_Experiment). ### ENSEMBLE Field Campaign ###<br> - Sensor data contains a continuous record of key environmental parameter (Temperature, Electrical Conductivity, pH, Turbidity) measured at the the three field sites (Val Sorey, Valroseg and Otemma)<br> - BA contains flow cytometric measurements of bacterial abundance (in cells g-1 dry sediment).<br> - BP contains measurements of bacterial carbon production measured as 3H Leucine incorporation.<br> - Chla-1 and Chla-2 contain data on the spectrophotometric measurement of chlorophyl-a (after extraction in EtOH).<br> - DOC contains data on the concentration of Dissolved Organic Carbon.<br> - EEA contains data on the measurements of Extracellular Enzyme Activities.<br> - EPS contains data on the quantification of Extracellular Polymeric Substances measured as carbohydrate equivalents (Dubois assay).<br> - Field Data contains metadata (ID, coordinates, sampling time, etc) for the field sites.<br> - NUT contains measurements of main water nutrients (NO3, NO2, NH4, and SRP)<br> - SI contains data on water stable isotopes<br> - Water Metadata contains measurements of key environmental data for a subset of sites. <br> ### ENSEMBLE Experiment ### - The folder Pictures contains drone images and close-up pictures of the field experiment setup.<br> - CHLA_BA contains measurements of chl-a concentration and bacterial cell counts<br> - metadata contains measurements of key environmental parameter for each treatment over the course of the experiment<br> - NUT contains measurement of main water nutrients (NO3, NO2, NH4, and SRP)<br>
DOI: 10.21410/7e4/r6nloh
title: China Internet Regulations 1987-2022

description: This database contains 473 laws, policies, and administrative decisions that the Chinese government has adopted from 1987 to 2022. It also informs of the year-on-year adoption of the content regulation in this period.
DOI: 10.21410/7e4/pwq6wf
title: Geographical distribution of the top 100 internet firms 2013-2022

description: This database contains the geographical distribution of the top 100 internet firms in China from 2013 to 2022. It informs of the number of top 100 internet firms that each province and provincial-level municipality have created.
DOI: 10.5519/qd.gf3e6his
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (16 records)
DOI: 10.6084/m9.figshare.24241336
title: RP dataset files

description: Raw and processed alignments and corresponding phylogenetic trees for RP dataset.
DOI: 10.6084/m9.figshare.24241332
title: NM dataset files

description: Raw and processed alignments and corresponding phylogenetic trees for NM dataset.
DOI: 10.5519/qd.iu46198h
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)
DOI: 10.6084/m9.figshare.24241378
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.6084/m9.figshare.24241378.v1
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.6084/m9.figshare.24241399
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.6084/m9.figshare.24241399.v1
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.6084/m9.figshare.24241408
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.6084/m9.figshare.24241408.v1
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.6084/m9.figshare.24241411
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.6084/m9.figshare.24241411.v1
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology
DOI: 10.5281/zenodo.8375161
title: DISPLACE Corpus 2023: Dev Set

description: The DISPLACE challenge entailed a first of kind task to perform speaker and language diarization on the same data, as the data contains multi-speaker social conversations in multi-lingual code-mixed speech. In multi-lingual communities, social conversations frequently involve code-mixed and code-switched speech. In such cases, various speech processing systems need to perform the speaker and language segmentation before any downstream task. The current speaker diarization systems are not equipped to handle multi-lingual conversations, while the language recognition systems may not be able to handle the same talker speaking in multiple languages within the same recording. With this motivation, the DISPLACE challenge addressed Speaker Diarization (SD) in multi-lingual settings and Language Diarization (LD) in multi-speaker settings, using the same underlying dataset. For this challenge, a natural multi-lingual, multi-speaker conversational dataset was distributed. More details about the dataset can be found at https://www.isca-speech.org/archive/pdfs/interspeech_2023/baghel23_interspeech.pdf <br>
DOI: 10.5281/zenodo.8303784
title: DISPLACE Corpus 2023: Dev Set

description: The DISPLACE challenge entailed a first of kind task to perform speaker and language diarization on the same data, as the data contains multi-speaker social conversations in multi-lingual code-mixed speech. In multi-lingual communities, social conversations frequently involve code-mixed and code-switched speech. In such cases, various speech processing systems need to perform the speaker and language segmentation before any downstream task. The current speaker diarization systems are not equipped to handle multi-lingual conversations, while the language recognition systems may not be able to handle the same talker speaking in multiple languages within the same recording. With this motivation, the DISPLACE challenge addressed Speaker Diarization (SD) in multi-lingual settings and Language Diarization (LD) in multi-speaker settings, using the same underlying dataset. For this challenge, a natural multi-lingual, multi-speaker conversational dataset was distributed. More details about the dataset can be found at https://www.isca-speech.org/archive/pdfs/interspeech_2023/baghel23_interspeech.pdf <br>
DOI: 10.4121/14394578
title: Data underlying the publication: Product inhibition and pH affect stoichiometry and kinetics of chain elongating microbial communities in sequencing batch bioreactors - 16S data

description: 16S rRNA sequences from chain elongating microbial communities and raw data of end-of-cycle measurements and cumulative gas production over time, as presented in the article 'Product inhibition and pH affect stoichiometry and kinetics of chain elongating microbial communities in sequencing batch bioreactors '
DOI: 10.4121/14394578.v1
title: Data underlying the publication: Product inhibition and pH affect stoichiometry and kinetics of chain elongating microbial communities in sequencing batch bioreactors - 16S data

description: 16S rRNA sequences from chain elongating microbial communities and raw data of end-of-cycle measurements and cumulative gas production over time, as presented in the article 'Product inhibition and pH affect stoichiometry and kinetics of chain elongating microbial communities in sequencing batch bioreactors '
DOI: 10.6084/m9.figshare.24241402
title: Baker2023_Halophiles_Supplementary_Data

description: The MAGs reported in this study have been deposited in GenBank under BioProject number PRJNA901412. All raw data underlying phylogenomic analyses (raw and processed alignments and corresponding phylogenetic trees) and all predicted proteomes have been deposited into this Figshare.
DOI: 10.6084/m9.figshare.24241402.v1
title: Baker2023_Halophiles_Supplementary_Data

description: The MAGs reported in this study have been deposited in GenBank under BioProject number PRJNA901412. All raw data underlying phylogenomic analyses (raw and processed alignments and corresponding phylogenetic trees) and all predicted proteomes have been deposited into this Figshare.
DOI: 10.4121/afd6f608-469e-48f9-977d-875b45840d39
title: Process Discovery Contest 2023

description: This data set contains the data set as was used for the Process Discovery Contest of 2023 (PDC 2023).The data set contains 384 training logs, 96 corresponding test logs and base logs, 96 correspondingground truth logs, and 96 models. The logs are all stored using the IEEE XES file format (see either https://www.xes-standard.org/ or https://ieeexplore.ieee.org/document/7740858), while the models areworkflow nets (a subclass of Petri nets) stored in the PNML fileformat (seehttps://www.iso.org/obp/ui/#iso:std:iso-iec:15909:-2:ed-1:v1:en).
DOI: 10.4121/afd6f608-469e-48f9-977d-875b45840d39.v1
title: Process Discovery Contest 2023

description: This data set contains the data set as was used for the Process Discovery Contest of 2023 (PDC 2023).The data set contains 384 training logs, 96 corresponding test logs and base logs, 96 correspondingground truth logs, and 96 models. The logs are all stored using the IEEE XES file format (see either https://www.xes-standard.org/ or https://ieeexplore.ieee.org/document/7740858), while the models areworkflow nets (a subclass of Petri nets) stored in the PNML fileformat (seehttps://www.iso.org/obp/ui/#iso:std:iso-iec:15909:-2:ed-1:v1:en).
DOI: 10.6084/m9.figshare.24241429
title: Supplementary Material for: Proteomics to metabolomics: A new insight into the pathogenesis of hypertensive nephropathy

description: Abstract Background: Hypertensive nephropathy (HN) is a high burden disorder and a leading cause of end-stage renal disorder. In spite of huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to provide a comprehensive insight towards this complex disorder. Methods: Protein expression profiles of kidney tubule and cortex sub-compartments were retrieved from the PRIDE database and the quality of the datasets were assessed using principal component analysis (PCA) and hierarchical clustering. Differentially expressed proteins (DEPs) were detected and their attributed metabolites were enriched and their interactions were assessed in multi-layer networks. Moreover, considering the DEPs and the predicted metabolites, key biomedical phenomena with a leading role in HN pathogenesis were proposed. Results: Amino acid and purine metabolisms are the most prominent alteration in kidney cortex whereas dysregulation of energy hemostasis is a key pathogenic mechanism in tubule. Besides, actin cytoskeleton disorganization is an enriched pathway in both anatomical areas. Conclusion: The proteomics profiles of kidney sub-compartments were analyzed using a top-down approach to infer the main pathogenic processes. The constructed holistic map of HN can be exploited to propose novel therapeutic strategies. Keywords: Hypertensive nephropathy, Proteomics, Metabolomics, Systems biology