cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/d49ct4fypr
title: Modeling the consequences of asymmetric scrutiny of stock assessment outputs

description: Code for exploring the consequences of selectively re-doing stock assessments as a function of the management consequences of the output of the first pass of the assessment.
DOI: 10.17632/7jhnzrmddz
title: Air-Sense ADS Data

description: The dataset of 2000 cotton leaves on the main stem of the plant were collected from cotton field of MNS-University of Agriculture, Multan. All images were captured using a 16-mega pixel camera in RGB(Red, Green, Blue) format during daylight between 12:00 pm to 2:00 pm in June, July, and August 2023. All images were labelled as ‘healthy’ or ‘injured’ by agricultural experts. The dataset is accompanied by a text file containing data of chlorophyll content of leaf taken from SPAD-502 meter. The process of re-cording chlorophyll content of the lead using SPAD-502 meter is shown in Figure 2. Chlorophyll content of leaf was recorded at three distinct locations: the top leaf, the centre leaf, and the bottom leaf of the main cot-ton plant stem.
DOI: 10.17632/yk6dyys48w
title: Tubulin (TUBA4A) ALS Mutations

description: Molecular Dynamic Simulation based predicted structures of various tubulin-alpha chain (TUBA4A) mutations reported in familial ALS.
DOI: 10.17632/jvf9jdgkt2
title: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties; Niekamp et al.; part 2 of 5

description: This project is described in: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties (2023) and can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/2023.10.26.564217v2

Here, we provide the experimental data together with the custom python code and ImageJ macros used for data analysis. Details on how the code works are given in the script itself.

This is part 2 of 5.

Part 1: Data for Figure 1, 2, and 3
Part 2: Data for Figure 4, 5, 6, and 7
Part 3: Data for Figure S1, S2, S3, S4, and S5
Part 4: Data for Figure S6
Part 5: Data for Figure S7
DOI: 10.17632/xdrkfb7trw
title: Questions for a Market Survey on BIM and Lighting Control

description: Digitalisation and new working methods are changing work processes in the construction industry and opening up new opportunities for those involved in the planning, execution and operation of buildings. A market survey was conducted to gain a better insight into developments in this area. The focus is on Building Information Modelling (BIM), Computer-Aided Facility Management (CAFM) and user-centred lighting control systems (daylight and/or artificial light). The aim of the evaluation, in conjunction with a literature review, is to identify requirements for greater integration of building control systems, particularly for lighting, in BIM in order to create more transparency, functionality, user-centredness and better operability. This should also improve energy efficiency and comfort in the building. The catalogue of questions on which the market survey was based is presented here. The results and further details on the survey can be found in this article: Link will be added shortly.
DOI: 10.17632/7jhvbsxfdc
title: Data for: Community energy markets with battery energy storage systems: a general modeling with applications

description: Input data of generation and consumption of a period of 24 hours of 365 days and battery storage system information. Part of the data was extracted from the related link.
DOI: 10.17632/stdmys22gk
title: Survey of the Level of Preparedness and Fears of Anaesthesia Staff regarding the Management of COVID-19 patients in ABUTH Zaria-Nigeria

description: This is a survey of the level of preparedness and fears of anaesthesia department staff regarding the management of COVID-19 patients in a low resource tertiary hospital in Zaria, Nigeria. During the period April 11-18, 2020 structured questionnaires were sent out by WhatsApp to physician anaesthetists, nurse anaesthetists, intensive care nurses, biomedical engineers and health assistants in ABUTH Zaria and those who consented filled and returned the questionnaires. Data obtained include age, gender, marital status, professional role, level of preparedness, availability of working materials/equipment, fear of COVID-19, level of stress, stigmatization and the willingness or otherwise to volunteer in the management of COVID-19 patients. Information collected were analyzed using SPSS version 21. The level of significance was set at p&lt;0.05.
DOI: 10.17632/d4js3h6zsx
title: Data for: Online and handwritten homework in Calculus for STEM majors

description: Anonymized data on student results.
DOI: 10.17632/gc2vpjj2f4
title: Data for: Linking genetic variation with epigenetic profiles in Sjögren’s syndrome
DOI: 10.17632/bskmkxpjty
title: LINF_050008300

description: MCM domain-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/gxbf8zf324
title: BRO_Ditte – Dimme – Migging_Road

description: BRO_Ditte – Dimme – Migging_Road
DOI: 10.17632/8ndbmzwy8t
title: Raw dataset of 18 landmarks coordinates of Anopheles cruzii from São Paulo, Brazil

description: 18 landmarks coordinates in TPS format for 509 Anopheles cruzii individuals from São Paulo, Brazil
DOI: 10.17632/k659jr9gvv
title: Transcriptional dysregulation in postnatal glutamatergic progenitor contributes to closure of the cortical neurogenic period

description: Glutamatergic progenitors persist in the forebrain long after the period of corticogenesis. We performed fate-mapping of pre- and postnatal glutamatergic progenitors to investigate their lineage relationship and neuronal output. Our results show that postnatal glutamatergic progenitors do not accumulate during embryonic development, but are produced by embryonic radial glial cells that persist after birth in the dorsal subventricular zone. Whereas most undergo delayed cell death, some of these progenitors give rise to cortical neurons that express markers of late born neurons, but ectopically migrate to deep cortical layers where they survive for at least several weeks. Single cell RNA-sequencing reveal a downregulation of transcriptional programs, specifically those involved in metabolism, differentiation and migration, which parallels the gradual decline in cortical neurogenesis observed in vivo. Rescuing experiments show that postnatal progenitors remain permissive to genetic and pharmacological manipulation, suggesting that they could be recruited for regenerative purposes. Our study is the first to provide an in depth characterization of postnatal glutamatergic progenitors and identifies novel potential therapeutic targets for promoting brain repair
DOI: 10.17632/7ckngf5c5c
title: Correlation Between Coordination and Quality of Life among Post Covid Patients

description: COVID disease (Coronavirus) is an infectious disease caused by the SARS-CoV-2 (severe acute respiratory syndrome Coronavirus 2) virus. It is profoundly contagious and quickly spread, as it tends to be communicated by respiratory drops and contact. It's common side effects is fever, headache, serious cerebral pain, myalgia and fatigue and the incubation period for Coronavirus contamination is ordinarily 1-14 days and could reach out up to 24 days. Real time RT-PCR is considered the standered confirmatory test for COVID-19. It can affect multiple organ systems, including the central nervous system (CNS) and the brain. The virus can enter the CNS through various mechanisms, including direct invasion, crossing the blood-brain barrier, or indirectly through inflammation and immune responses.
The main purpose of this preliminary study is to determine feasibility of coordination study to assess the effect of post COVID complication on coordination and quality of life among post COVID patients.
The aim of conducting the study as to assess the correlation between coordination and quality of life among post COVID patient.
In this study we have used EQ5D5L to assess the quality of life and Comprehensive coordination scale (CCS) to assess the coordination.
DOI: 10.17632/7725svpmy4
title: Deconstructed beetles: green composite materials with potential for passive cooling due to high near-infrared reflectivity -  Data and Code

description: This data allowed us to describe the optical properties and underlying structures that produce very high near-infrared reflectance in three species of green scarab beetles Xylonichus eucalypti, Anoplognathus prasinus and Paraschizognathus olivaceus. Although the chiral structures in scarabs have been well described in previous literature, we have limited knowledge of the natural mechanisms to manipulate near-infrared light (NIR; 700 – 1700 nm). In addition, previous studies in light manipulation by the beetle elytra have been focused mainly on the cuticle. Thus, our aim was to explore if other elements in the elytra can contribute to their characteristic visible and NIR reflectance. 

This dataset contains different types of data including reflectance and transmittance spectra, morphological data of the shape and dimension of the nanostructures that reflect light in the beetle elytron,  and results of computational models simulating different scenarios to understand how these structures interact with light. Thus, we are attaching a readme file inside the folder "Data" explaining the data collection and usage for each file. 

Our Highlights: 
- The studied beetle species have similar high NIR reflectance and green appearance. 
- The elytra of these species contains bilayered composite materials, where a structural component reflects light and an overlaying thin layer filters only the reflectance in the visible spectrum. We showed that the transmittance of the outermost component of the composite materials is similar in the three species.
- We characterized the shape of the nanostructures that produce broadband reflectance in Xylonichus eucalypti.
- We compared different simulated scenarios where the nanostructures varied in separation and size and were illuminated with different polarizations and angle of incidence.
DOI: 10.17632/fdhn6n72sb
title: Data for: Three-effect tubular solar desalination system with vacuum operation under actual weather conditions

description: 1. Temperature of every channel (including 3-stage water in troughs, circular outer surface, circulating water and ambient); 2. Yield rate; 3. Volume rate of circulating water; 4. Electric conductivity of raw water, etc.
DOI: 10.17632/2v2mnbzykt
title: Suello et al. 2023, STOTEN, Data

description: Article Title: Rising sea levels help mitigate climate warming through increased carbon sequestration in salt marsh sediments
DOI: 10.17632/pt4bjfrjkg
title: SUPPLEMENTARY MATERIAL

description: Risk of Major Adverse Cardiovascular Events in Patients With Moderate‑to‑Severe Atopic Dermatitis: A United States Population‑Based Study
DOI: 10.17632/fsdkzzhw2w
title: Dairy farmer succession planning

description: These files represent the collection of data on the intention of rural producers to adopt family succession planning in milk production.
The data was collected from 160 dairy farmers in the state of Paraná - Brazil.
DOI: 10.17632/kzgdp5khky
title: Application of Motion Image Skeleton Recognition Algorithm Based on Convolutional Neural Network in Rehabilitation Training

description: This paper aims to explore the application effect of Convolutional Neural Network (CNN) algorithm in motion image skeleton recognition to improve the accuracy of motion skeleton recognition and the efficiency of rehabilitation training. Here, in response to the robustness and insensitivity to changes in lighting conditions of human motion image skeleton data, the first step is to depict human motion images in real Three-Dimensional (3D) space by abstracting the human body into several joint points connected to bones to obtain 3D skeleton data. Second, the channel attention module is proposed, which is combined with the spatial graph convolution module and the time graph convolution module in the graph CNN. The 3D skeleton data samples of diverse motion images are learned to obtain good action expression ability. A motion image skeleton recognition model based on multi-attention Spatial Temporal Graph Convolutional Network is constructed. Finally, the constructed model is applied to the tracking and analysis of rehabilitation training. The results show that the model algorithm can track the patient's motor state accurately in rehabilitation training. The effectiveness of local and global information reaches more than 90%, which is obviously better than the spatiotemporal graph convolution algorithm. The model algorithm effectively reduces the problem of muscle injury caused by exercise posture error during manual intervention in traditional rehabilitation training. Therefore, the model algorithm can be widely used in rehabilitation training and practice, which provides new ideas and exploration directions for the medical industry to improve the efficiency and quality of rehabilitation training.
DOI: 10.17632/56g8r54mjg
title: LINF_060010000

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/99dbbsx3ff
title: Supplementary Figure 1

description: Trends in surgery for DFSP, 2004-2017
DOI: 10.17632/nx8tz6gcw2
title: T/R/V KAGOSHIMA-MARU KG1615 M-LINE DATA SET

description: CRUISE NAME: KG1615 SHIP: T/R/V KAGOSHIMA-MARU TRANSECT NAME: M-LINE OBSERVATION DATE: 16-Nov-2016 Time information in the data is UTC+9. DATA 1) Station Information FILE NAME: STATION_INFO_KG1615_M.mat FORMAT: Matlab's MAT-file 2) Microstructure data FILE NAME: TURBULENCE_KG1615_M.mat FORMAT: Matlab's MAT-file 3) Nitrate data FILE NAME: NITRATE_KG1615_M.mat FORMAT: Matlab's MAT-file 4) Nitrate sensor calibration FILE NAME: NITRATE_CALIBRATION_KG1615_M.mat FORMAT: Matlab's MAT-file see NITRATE_CALIBRATION_KG1615_M.png for the calibration result. 5) PAR data FILE NAME: PAR_KG1615_M.mat FORMAT: Matlab's MAT-file mean Kd = -0.048 [m^{-1}] see Kd_VARIATION_KG1615_M.png for the histogram of Kd. 6) ADCP data FILE NAME: ADCP_KG1615_M.mat FORMAT: Matlab's MAT-file 7) Echo Sounder data FILE NAME: ECHO_SOUNDER_KG1615_M.mat FORMAT: Matlab's MAT-file
DOI: 10.17632/c249p8gdzd
title: Data for: Connecting MHC-I-binding motifs with HLA alleles via deep learning

description: This dataset contains the research data supporting the study, “connecting MHC-I-binding motifs with HLA alleles via deep learning”.
1. MHCI_res182_seq.json: the peptide-binding cleft sequence of each MHC-I allele extracted from  the IPD-IMGT/HLA database (version 3.41.0)
2. MHCI_res182_onehot.npy: the one-hot encoding of the peptide-binding cleft sequence of MHC-I allele
3. dataframe.tar.gz: this folder contains training, validation, and benchmark datasets
    [the common columns]
        1. sequence: the peptide sequence
        2. peptide_length: the length of peptide sequences
        3. mhc: the MHC-I allele
        4. meas: binding affinity for assay data
        5. value: the value (between 0 and 1) converted from the binding affinity
        6. bind: the label of binding
        7. source: data source (assay, MS, random decoy, or peptide decoy)
    1. train_hit.csv
        1. data: measurements extracted from IEDB for the training process
        2. columns
            1. the common columns
            2. MHCfovea: the prediction score of MHCfovea (used for ScoreCAM analysis)
    2. train_decoy_{1-90}.csv
        1. data: artificial decoy peptides for the training process; the data number of each file is almost equal to the number of eluted peptides
        2. columns
            1. the common columns
    3. valid.csv
        1. data: measurements extracted from IEDB and decoy peptides for validation
        2. columns
            1. the common columns
            2. batch_size_{16, 32, 64}_and_learning_rate_{0.00100, 0.00010, 0.00001}: for optimizing hyperparameters of batch size and learning rate
            3. DE_{1, 5, 10, 15, 30}_and_{30, 60, 90}: for optimizing the D-E ratio of the training and downsized dataset; the first number is the D-E ratio of the downsized dataset and the second number is the D-E ratio of the training dataset
    4. benchmark.csv
        1. data: eluted peptides extracted from IEDB and decoy peptides for the testing process
        2. columns
            1. the common columns
            2. NetMHCpan4.1, MHCflurry2.0, MixMHCpred2.1, MHCfovea: the prediction score of each predictor
4. allele_expansion.tar.gz: this folder contains data for the allele expansion
    1. peptides.csv: peptides used for allele expansion
    2. output/{MHC-I group}
        1. allele.json: alleles of the MHC-I group
        2. motif.npy: binding motifs for each allele
        3. prediction.npy: prediction score for each allele (the order is the same as that of peptides.csv)
5. MHCfovea-v1.0.0.zip: the source code of MHCfovea
DOI: 10.15468/dl.5xendv
title: Occurrence Download

description: A dataset containing 1272618 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus robur L."
 ]
}

 The dataset includes 1272618 records from 3569 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001555-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1069194.10fu
title: SH1069194.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1069198.10fu
title: SH1069198.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1069205.10fu
title: SH1069205.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1069208.10fu
title: SH1069208.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1069212.10fu
title: SH1069212.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.