cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/xg5f-b066
title: In-situ Process Monitoring Data of PECVD Plasma

description: The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process data are required, such as plasma, gases, equipment control state, and process result. Also it is important to data must be have the reproducibility of raw data is difficult to acquire due to the data security policy of the chip manufactures.
DOI: 10.17632/cssc4vn6wv.1
title: Homology and the evolution of vocal folds in the novel avian voice box

description: Data for "Homology and the evolution of vocal folds in the novel avian voice box" including RNA-sequencing and ATAC-sequencing of developing chick and mouse vocal folds.

ATAC-sequencing peaks for dissected HH39 chick vocal folds (VF) and dissected epithelium and mesenchyme from HH34 chick trachea (Tr) and tracheobronchial junction (TBJ).  Peaks were called for each replicate and a consensus peak set with a fixed center and standardized width of 200 bps was created using Genrich for input into chromVAR. Also included is the Chromvar analysis R dataset. For chromVAR analysis, we followed the default walkthrough (https://greenleaflab.github.io/chromVAR/articles/Introduction.html) using the galGal6 BSgenome package and the human_pwms_v1 position weight matrices to identify transcription factor binding sites using the matchMotifs command in the motifmatchr R package (v1.2.0). Peaks from the scaffold “chrUn_NW_020109859v1” were removed prior to correcting for GC bias and computing motif variability, as it does not appear in the galGal6 BSGenome file.

Also included are transcript frequencies for Lightseq data from mouse and chick vocal fold, tracheal mesenchyme and cartilage. Data analysis was performed using published Light-seq analysis code (Kishi et al., 2022) on the Harvard Medical School O2 cluster (Kernel 2.10.0) with Python (v3.7.5), PyTable (v3.6.1), samtools (v1.12), pysam (v0.17.0), numpy (v1.21.4), pandas (v1.3.4), Biopython (v1.79), and scikit-bio (v0.5.6). Briefly, Barcode, UMI, and cDNA sequences were extracted from Read 1 using UMI-tools (v1.1.1). cDNA sequence was then mapped to either the chick (GG6a) or mouse (M27) genomes. Reads were assigned to genes using FeatureCounts using fractional read counting and the GTF annotation ‘gene’ (-M --fraction -g gene_id -t gene) and deduplicated (per gene) with UMI-tools dedup. Reads were parsed out by barcode sequences using a custom python script adapted from Kishi et al., 2022. Barcode to sample information is included in the sample_info spreadsheet.
DOI: 10.17632/4brcwhmvyk.3
title: Metadata for Automated plant species identification from the stomata images using deep neural network

description: Our study has been conducted at the Sundarbans Mangrove Forest and Ratargul Fresh Water Swamp Forest. Both study sites have distinct ecological characteristics. While the Shundarbans Mangrove has a complex network of tidal waterways and is regularly inundated by saline water, the Ratargul is linked with the Gowain River and faces seasonal flooding with the fresh water from the Gowain River during the rainy season, which is generally from May to early October. There is a stomata dataset of 11 species collected from these two study sites: 9 species collected from the Sundarban mangrove forest  and 2 species collected from the Ratargul Swamp Forest in Bangladesh. These are the species worth mentioning in the two forests. Hence, we used this dataset for automated species identification.

Sundarban mangrove species-
1. Aglaia Cucullata 
2. Bruguiera gymnorrhiza 
3. Bruguiera sexangula 
4. Cerbera manghas 
5. Ceriops decandra 
6. Excoecaria agallocha 
7. Heritiera fomes 
8. Sonneratia apetala
9. Xylocarpus moluccensis 

Ratargul swampforest species-

10. Barringtoni aacutangula
11. Pongamia pinnata
For more please read metadata file "Stomata ISO19139 Metadata.xml"
DOI: 10.17632/4brcwhmvyk.1
title: Automated plant species identification from the stomata images using deep neural network: A representative study of mangrove and freshwater swamp forest tree species of Bangladesh

description: Our study has been conducted at the Sundarbans Mangrove Forest and Ratargul Fresh Water Swamp Forest. Both study sites have distinct ecological characteristics. While the Shundarbans Mangrove has a complex network of tidal waterways and is regularly inundated by saline water, the Ratargul is linked with the Gowain River and faces seasonal flooding with the fresh water from the Gowain River during the rainy season, which is generally from May to early October. There is a stomata dataset of 11 species collected from these two study sites: 9 species collected from the Sundarban mangrove forest  and 2 species collected from the Ratargul Swamp Forest in Bangladesh. These are the species worth mentioning in the two forests. Hence, we used this dataset for automated species identification.

Sundarban mangrove species-
1. Aglaia Cucullata 
2. Bruguiera gymnorrhiza 
3. Bruguiera sexangula 
4. Cerbera manghas 
5. Ceriops decandra 
6. Excoecaria agallocha 
7. Heritiera fomes 
8. Sonneratia apetala
9. Xylocarpus moluccensis 

Ratargul swampforest species-

10. Barringtoni aacutangula
11. Pongamia pinnata
DOI: 10.17632/4brcwhmvyk.2
title: Metadata for Automated plant species identification from the stomata images using deep neural network

description: Our study has been conducted at the Sundarbans Mangrove Forest and Ratargul Fresh Water Swamp Forest. Both study sites have distinct ecological characteristics. While the Shundarbans Mangrove has a complex network of tidal waterways and is regularly inundated by saline water, the Ratargul is linked with the Gowain River and faces seasonal flooding with the fresh water from the Gowain River during the rainy season, which is generally from May to early October. There is a stomata dataset of 11 species collected from these two study sites: 9 species collected from the Sundarban mangrove forest  and 2 species collected from the Ratargul Swamp Forest in Bangladesh. These are the species worth mentioning in the two forests. Hence, we used this dataset for automated species identification.

Sundarban mangrove species-
1. Aglaia Cucullata 
2. Bruguiera gymnorrhiza 
3. Bruguiera sexangula 
4. Cerbera manghas 
5. Ceriops decandra 
6. Excoecaria agallocha 
7. Heritiera fomes 
8. Sonneratia apetala
9. Xylocarpus moluccensis 

Ratargul swampforest species-

10. Barringtoni aacutangula
11. Pongamia pinnata
For more please read metadata file "Stomata ISO19139 Metadata.xml"
DOI: 10.17632/n2wm36mrmw.1
title: Unprocessed and uncropped images for "How Pol α-primase is targeted to replisomes to prime eukaryotic DNA replication" 

description: Unprocessed and uncropped images for "How Pol α-primase is targeted to replisomes to prime eukaryotic DNA replication"
DOI: 10.17632/mp2rhtgtw6.1
title: THE INCREASING UPTAKE OF ORPHAN DRUGS IN THE WHO ESSENTIAL MEDICINES LIST

description: Supplement of the manuscript entitled THE INCREASING UPTAKE OF ORPHAN DRUGS IN THE WHO ESSENTIAL MEDICINES LIST currently submitted to the WHO Bulletin
DOI: 10.17632/kdgmfb8k55.1
title: Phylogenomics shows that novel tapeworm-like traits of haplozoan parasites evolved from within the Peridiniales (Dinoflagellata)

description: Transcriptome assemblies of Haplozoon axiothellae and Haplozoon pugnus.

https://www.sciencedirect.com/science/article/abs/pii/S1055790323001598
https://doi.org/10.1016/j.ympev.2023.107859
DOI: 10.17632/tvvbfp4yr9.1
title: Supplementary datasets

description: Supplementary datasets related to this study are available in the online supplementary data.
Particle size or geochemical data involved in the article can be referred to this study; Cai et al., 2013;  Xu et al., 2016; Zhong et al., 2017; Cao et al., 2019; Wang et al., 2022.
DOI: 10.17632/tvvbfp4yr9.2
title: Supplementary datasets

description: Supplementary datasets related to this study are available in the online supplementary data.
Particle size or geochemical data involved in the article can be referred to this study; Cai et al., 2013;  Xu et al., 2016; Zhong et al., 2017; Cao et al., 2019; Wang et al., 2022.
DOI: 10.17632/78py4t2bc5.1
title: Immersive virtual reality learning space for effective collaboration

description: An evaluation study (N = 95) was conducted to explore its useful design considerations and potential influencing factors for collaboration experience. The IVR learning space was enabled by the simultaneous localization and mapping- (SLAM)-based inside-out tracking technique and was informed by four essential design considerations for collaborative learning pedagogy, namely, the role script, learning task, collaboration mechanism, and communication design.
DOI: 10.17632/78py4t2bc5.2
title: Immersive virtual reality learning space for effective collaboration

description: An evaluation study (N = 95) was conducted to explore its useful design considerations and potential influencing factors for collaboration experience. The IVR learning space was enabled by the simultaneous localization and mapping- (SLAM)-based inside-out tracking technique and was informed by four essential design considerations for collaborative learning pedagogy, namely, the role script, learning task, collaboration mechanism, and communication design.
DOI: 10.17632/hgr37pv9pp.1
title: Modern Cd Dataset

description: Data underlying figures in "Refining the roles of productivity, redox, and remineralization on the cadmium isotope composition of marine sediments"
DOI: 10.17632/9wnjpmckx9.1
title: Data on improving optimum Adsorption Corrosion Inhibition of AISI 4140 Steel by induce Acidify Carica papaya Leaf Extract

description: Raw data from RSM and ANN design data
DOI: 10.17632/j2x4h9ddcj.1
title: Syncytiotrophoblast Membrane Extracellular Vesicles (STB-EVs) Derived Proteomic Biomarkers of Preeclampsia-Supplemental Data

description: The file contains the full list of differentially expressed proteins and results of functional enrichment analysis ( Gene Ontology; Biological Processes (GO:BP), Gene Ontology; Molecular Functions (GO:MF), Gene Ontology; Cellular Components (GO:CC) and KEGG Pathways)  of placenta, medium/large syncytiotrophoblast membrane extracellular vesicles (m/lSTB-EVs) and small syncytiotrophoblast membrane extracellular vesicles (sSTB-EVs) in preeclampsia (PE) compared to normal (NP)
DOI: 10.17632/6s5fkd3z7t.1
title: Syncytiotrophoblast Membrane Extracellular Vesicles (STB-EVs) Derived Transcriptomic Biomarkers of Preeclampsia-Supplemental Data

description: The folder includes the complete list of differentially expressed genes and functional enrichment analysis (Gene Ontology; Biological Processes (GO:BP), Molecular Functions (GO:MF), Cellular Components (GO:CC), SPIA, and KEGG Pathways) of placenta, medium/large syncytiotrophoblast membrane extracellular vesicles (m/lSTB-EVs), and small STB-EVs in preeclampsia.
DOI: 10.6084/m9.figshare.23805348
title: mp_pickle.zip

description: The dataset used for generating dense vector representation
DOI: 10.6084/m9.figshare.23805348.v1
title: mp_pickle.zip

description: The dataset used for generating dense vector representation
DOI: 10.5281/zenodo.8033673
title: Auxiliary Datasets for Speaker Disambiguation in Quotebank

description: This data repository contains the auxiliary datasets necessary for enriching and preprocessing Quotebank, a large dataset of unique, speaker-attributed quotations. The scripts utilizing the data can be found in the quotebank-toolkit GitHub repository. The datasets are stored in <strong>data.zip </strong>and are described below: <strong>quotebank_disambiguation_mapping_quote.parquet</strong><br> Provides the `quoteID`-&gt; `speakerQID` mapping for each quotation, disambiguating ambiguous speaker names and linking them to their respective Wikidata items. The schema of the dataset is as follows: <pre><code> |-- quoteID: primary key of the quotation (format: "YYYY-MM-DD-{increasing int:06d}") |-- speaker: Wikidata ID corresponding to the speaker of the quotation</code></pre> The mapping is created using heuristics described in the following paper: Marko Čuljak, Andreas Spitz, Robert West, and Akhil Arora<br> "Strong Heuristics for Named Entity Linking"<br> <em>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop</em><br> 10.18653/v1/2022.naacl-srw.30 <strong>self_quotations_filtered.parquet</strong><br> Contains the identifiers of the quotations identified as not being self-attributed. The schema of the dataset is as follows: <pre><code> |-- quoteID: primary key of the quotation (format: "YYYY-MM-DD-{increasing int:06d}") </code></pre> <strong>speaker_attributes.parquet</strong><br> Contains attributes of all the speakers appearing in Quotebank extracted from Wikidata. The schema of the dataset is as follows. <pre><code> |-- id: Wikidata item QID of the speaker, primary key |-- aliases: list of speaker's aliases |-- date_of_birth: list of possible speaker's dates of birth |-- nationality: list of speaker's nationalities |-- gender: list of speaker's previous or current genders |-- lastrevid: ID of the last revision of the speaker's item |-- ethnic_group: list of ethnic groups the speaker belongs to |-- US_congress_bio_ID: identifier for the speaker in the Biographical Directory of the United States Congress |-- occupation: list of speaker's occupations |-- party: list of parties the speaker is/was affiliated to |-- academic_degree: list of academic degrees obtained by the speaker |-- label: Wikidata label of the speaker |-- candidacy: list of the speaker's candidacies in political elections |-- type: type of the Wikidata entry (value is `item` for all the speakers) |-- religion: previous/current religious affiliations of the speaker </code></pre> Using the `id` field corresponding to the Wikidata QID of a speaker, this dataset can be easily joined with disambiguated Quotebank obtained by running the `cleanup_disambiguate.py` script available in the aforementioned quotebank-toolkit repository.
DOI: 10.5281/zenodo.8033672
title: Auxiliary Datasets for Speaker Disambiguation in Quotebank

description: This data repository contains the auxiliary datasets necessary for enriching and preprocessing Quotebank, a large dataset of unique, speaker-attributed quotations. The scripts utilizing the data can be found in the quotebank-toolkit GitHub repository. The datasets are stored in <strong>data.zip </strong>and are described below: <strong>quotebank_disambiguation_mapping_quote.parquet</strong><br> Provides the `quoteID`-&gt; `speakerQID` mapping for each quotation, disambiguating ambiguous speaker names and linking them to their respective Wikidata items. The schema of the dataset is as follows: <pre><code> |-- quoteID: primary key of the quotation (format: "YYYY-MM-DD-{increasing int:06d}") |-- speaker: Wikidata ID corresponding to the speaker of the quotation</code></pre> The mapping is created using heuristics described in the following paper: Marko Čuljak, Andreas Spitz, Robert West, and Akhil Arora<br> "Strong Heuristics for Named Entity Linking"<br> <em>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop</em><br> 10.18653/v1/2022.naacl-srw.30 <strong>self_quotations_filtered.parquet</strong><br> Contains the identifiers of the quotations identified as not being self-attributed. The schema of the dataset is as follows: <pre><code> |-- quoteID: primary key of the quotation (format: "YYYY-MM-DD-{increasing int:06d}") </code></pre> <strong>speaker_attributes.parquet</strong><br> Contains attributes of all the speakers appearing in Quotebank extracted from Wikidata. The schema of the dataset is as follows. <pre><code> |-- id: Wikidata item QID of the speaker, primary key |-- aliases: list of speaker's aliases |-- date_of_birth: list of possible speaker's dates of birth |-- nationality: list of speaker's nationalities |-- gender: list of speaker's previous or current genders |-- lastrevid: ID of the last revision of the speaker's item |-- ethnic_group: list of ethnic groups the speaker belongs to |-- US_congress_bio_ID: identifier for the speaker in the Biographical Directory of the United States Congress |-- occupation: list of speaker's occupations |-- party: list of parties the speaker is/was affiliated to |-- academic_degree: list of academic degrees obtained by the speaker |-- label: Wikidata label of the speaker |-- candidacy: list of the speaker's candidacies in political elections |-- type: type of the Wikidata entry (value is `item` for all the speakers) |-- religion: previous/current religious affiliations of the speaker </code></pre> Using the `id` field corresponding to the Wikidata QID of a speaker, this dataset can be easily joined with disambiguated Quotebank obtained by running the `cleanup_disambiguate.py` script available in the aforementioned quotebank-toolkit repository.
DOI: 10.17632/zb2smkbtb2.1
title: Supplementary Data for MBE-D-23-00144R1

description: Supplementary data to accompany the manuscript Metabolic Engineering of Synechococcus elongatus 7942 for Enhanced Sucrose Biosynthesis by Bo Wang, Cristal Zuniga, Michael T. Guarnieri, Karsten Zengler, Michael Betenbaugh, and Jamey D. Young
DOI: 10.17632/s2nykz7kds.1
title: Modified red light 5-aminolevulinic acid photodynamic therapy versus low dose isotretinoin therapy for moderate to severe acne vulgaris: A prospective, randomized, multicenter study

description: Supplemental materials of the article of Journal of the American Academy of Dermatology: "Modified red light 5-aminolevulinic acid photodynamic therapy versus low dose isotretinoin therapy for moderate to severe acne vulgaris: A prospective, randomized, multicenter study".
Supplementary figure 1. A) The overall effective rate of comedones, papules, pustules, cysts, and nodules in one-month post-therapy. B) The IGA scores in the two groups at baseline and in one-month post-therapy. C) Time to achieve 50% lesion improvement (T50) in two groups. ns no significant difference, **P<0.01, ***P<0.001.
Supplementary figure 2. Typical pictures of the cases in the M-PDT and the ISO groups before and after the therapy. The lesions in the M-PDT group showed clear improvements by week-4 and the improvement was sustained until 6-mon post-therapy. Better skin texture was also observed. The ISO group showed a transient exacerbation at week-4, followed by marked improvement at 1-mon post-therapy, which was sustained until 6-mon post-therapy.
DOI: 10.17632/pn7htgv4dv.1
title: Development of a species-level field diagnostic kit for Nipaecoccus viridis (Newstead) (Hemiptera: Pseudococcidae), an invasive and regulatory pest in the United States 

description: One of the main limitations in developing a species-level field diagnostic kit for Nipaecoccus viridis is the potential safety hazards associated with KOH. KOH is corrosive and causes damage to the skin, eyes, and mucous membranes. It remains unknow whether the potassium in the 10% KOH solution or the change in the pH that turned N. viridis hemolymph green. If the change in hemolymph color was caused by the pH and not associated with potassium, then finding a safe alternative to KOH that can change the pH could resolve the situation. Therefore, we tested different solutions, along with KOH, covering the whole pH spectrum to 1) figure out whether it is the potassium in 10% KOH or its pH that turns hemolymph green; 2) explore a relatively safe alternative to 10% KOH in laboratory and field experiments, and 3) develop a species-level field diagnostic kit for N. viridis.
DOI: 10.17632/nrs3jfwcf8.1
title: Data for: Mechanism of metamorphic fluid expulsion from ductile contact aureole: Insights from numerical modeling of a growing mid-crustal magma chamber

description: The dataset comprises output files for "Mechanism of metamorphic fluid expulsion from ductile contact aureole: Insights from numerical modeling of a growing mid-crustal magma chamber". It includes data for the complete evolution of the pluton and fluid propagation process for models of continuous growth, episodic growth at every 100 years, and episodic growth at every 1000 years. Additionally, data for three individual nodes located in the inner, middle and outer parts of the center and edge aureole are provided.
DOI: 10.17632/pn9zbwhftd.1
title: SVRdata

description: 243 sets of data were selected as samples for the process-composition-performance model of die casting aluminum alloy. The dataset was randomly divided into two groups: the first group of 200 samples was used for training, and the second group of 43 samples was used as a test set to verify accuracy. In the process of SVM training, small data sets are randomly divided and combined several times in order to more accurately evaluate the generalization performance of the model. Machine learning models predict yield strength and tensile strength with more than 96% accuracy using small data sets.
DOI: 10.17632/5dzzb5bc54.1
title: Speech Challenges

description: Speech challenges can significantly impact a person's ability to communicate effectively, affecting academic performance, social interactions, and overall quality of life. Early identification and intervention are crucial in addressing these challenges, as speech therapy and other interventions can help individuals improve their speech skills and enhance their communication abilities. Speech-language pathologists play a vital role in evaluating and treating speech challenges, tailoring interventions to meet the specific needs of each individual to promote successful communication and language development.
DOI: 10.17632/26gr3zctk9.1
title: Dataset_Universe_JESilva21

description: Datasets related to J.E. Silva et al., 2021. Universe.
DOI: 10.17632/26gr3zctk9.2
title: Dataset_Universe_PMachado23

description: Datasets related to P.Machado et al., 2023. Universe.