cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18494896
title: Data from: An autoinhibitory feedback mechanism preserves intestinal stem cell maintenance and fate commitment

description: This dataset contains the Seurat objects necessary to recreate all figures from the publication. 

The files included are as follows:



Ctrl_NotchKO_integrated_scent: The primary integrated Seurat object containing Control and Notch KO samples. It includes the final high-resolution cell type annotations.

RNAi_experiments_integrated: The integrated Seurat object containing all RNAi perturbation experiments (Control, Notch RNAi, CphUp and NotchRNAi+CphRNAi). It includes the final high-resolution cell type annotations.

Ctrl_NotchKO_progenitors_integrated_meld: A subset of the Control/Notch KO dataset restricted specifically to progenitor populations (ISCs, EBs, and EEPs). It contains MELD likelihood scores used to quantify the probability of cells belonging to the perturbed condition.

seuratKO_slingshot_object_integrated: A Seurat object containing trajectory inference results generated using Slingshot and Condiments. It includes specific dimensional reductions and lineage curves used to visualize the lineages.

differential_expression_results.zip: Contains the differential gene expression results using either the pseudobulk with edgeR approach or Seurat FindMarkers function with test.use=“MAST“ .

gene_sets.rds: contains the gene sets that were used to run GSEA and ORA.

plotSmoothers_data.rds: Pre-computed smoothed expression data extracted from the tradeSeq GAM model for all genes. Contains fitted expression values along pseudotime trajectories for each lineage and condition, used to visualize gene expression dynamics along differentiation trajectories without re-computing the smoothers each time.

tradeSeq_fit_condiments_strict.rds: A fitted tradeSeq generalized additive model (GAM) object for trajectory-based differential expression analysis. The model was fit with 6 knots on the Slingshot trajectory data, incorporating condition information (Control vs Notch KO) to enable testing for differential gene expression along pseudotime between conditions.

tradeSeq_condition_test_res_strict.rds: Results from the tradeSeq conditionTest function, containing statistical test results for genes differentially expressed between Control and Notch KO conditions along each of the four lineages. Includes p-values and adjusted p-values (FDR) for identifying genes with condition-specific expression dynamics during differentiation.

slingshot_individual_fit.rds: Condition-specific Slingshot trajectory objects fitted separately for Control and Notch KO using the conditments package.
DOI: 10.5281/zenodo.17716158
title: Data from: An autoinhibitory feedback mechanism preserves intestinal stem cell maintenance and fate commitment

description: This dataset contains the Seurat objects necessary to recreate all figures from the publication. 

The files included are as follows:



Ctrl_NotchKO_integrated_scent: The primary integrated Seurat object containing Control and Notch KO samples. It includes the final high-resolution cell type annotations.

RNAi_experiments_integrated: The integrated Seurat object containing all RNAi perturbation experiments (Control, Notch RNAi, CphUp and NotchRNAi+CphRNAi). It includes the final high-resolution cell type annotations.

Ctrl_NotchKO_progenitors_integrated_meld: A subset of the Control/Notch KO dataset restricted specifically to progenitor populations (ISCs, EBs, and EEPs). It contains MELD likelihood scores used to quantify the probability of cells belonging to the perturbed condition.

seuratKO_slingshot_object_integrated: A Seurat object containing trajectory inference results generated using Slingshot and Condiments. It includes specific dimensional reductions and lineage curves used to visualize the lineages.

differential_expression_results.zip: Contains the differential gene expression results using either the pseudobulk with edgeR approach or Seurat FindMarkers function with test.use=“MAST“ .

gene_sets.rds: contains the gene sets that were used to run GSEA and ORA.

plotSmoothers_data.rds: Pre-computed smoothed expression data extracted from the tradeSeq GAM model for all genes. Contains fitted expression values along pseudotime trajectories for each lineage and condition, used to visualize gene expression dynamics along differentiation trajectories without re-computing the smoothers each time.

tradeSeq_fit_condiments_strict.rds: A fitted tradeSeq generalized additive model (GAM) object for trajectory-based differential expression analysis. The model was fit with 6 knots on the Slingshot trajectory data, incorporating condition information (Control vs Notch KO) to enable testing for differential gene expression along pseudotime between conditions.

tradeSeq_condition_test_res_strict.rds: Results from the tradeSeq conditionTest function, containing statistical test results for genes differentially expressed between Control and Notch KO conditions along each of the four lineages. Includes p-values and adjusted p-values (FDR) for identifying genes with condition-specific expression dynamics during differentiation.

slingshot_individual_fit.rds: Condition-specific Slingshot trajectory objects fitted separately for Control and Notch KO using the conditments package.
DOI: 10.15468/dl.xhtj4g
title: Occurrence Download

description: A dataset containing 7456 species occurrences available in GBIF matching the query:
{
 "RecordedBy" : [
 "is R.M. Harley"
 ]
}

 The dataset includes 7456 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013938-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p4jdnq
title: Occurrence Download

description: A dataset containing 310 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tapena thwaitesi"
 ]
}

 The dataset includes 310 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013941-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w62xda
title: Occurrence Download

description: A dataset containing 151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Kubitzkia mezii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 151 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013943-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/sghnjs
title: Modelling Carbon and Groundwater in Peatlands

description: Data to reproduce the modelling in the paper "Modelling Carbon and Groundwater in Peatlands using AlphaEarth Embeddings ". Data will be made available once article is accepted,
DOI: 10.15468/dl.z6vvcq
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Newsteadia floccosa",
 "Year 1907-2025"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013942-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/31048
title: Chandra X-ray Observatory ObsId 31048

description: How Large Stellar Flares Influence the Chemistry of Protoplanetary Disks
DOI: 10.25824/redu/ihxy6l
title: Replication data for: "Low-cost apparatus for determining the band gap energy in photoresistors"

description: Raw data and Python scripts used for producing the figures presented in the research article "Low-cost apparatus for determining the band gap energy in photoresistors" by Vitor R. Coluci, Y.A. Meyer, H.H.M. Dos Santos, T.S.S. Ximenes, and Ivan de Oliveira (Universidade Estadual de Campinas)
DOI: 10.15468/dl.kjwa5w
title: Occurrence Download

description: A dataset containing 5826 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sarda sarda"
 ]
}

 The dataset includes 5826 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013946-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.984883
title: Ozone measurements from station Payerne (2024-02)

description: Location: Arosa, Switzerland; Distance from radiation site: 200 km
DOI: 10.1594/pangaea.984884
title: Ozone measurements from station Payerne (2024-01)

description: Location: Arosa, Switzerland; Distance from radiation site: 200 km
DOI: 10.1594/pangaea.984885
title: Radiosonde measurements from station Payerne (2024-02)
DOI: 10.15468/dl.prqhpr
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mentzelia chrysantha"
 ]
}

 The dataset includes 66 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007881-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.984886
title: Other measurements at 10 m from station Payerne (2024-02)
DOI: 10.1594/pangaea.984887
title: Other measurements at 10 m from station Payerne (2024-01)
DOI: 10.1594/pangaea.984888
title: Radiosonde measurements from station Payerne (2024-01)
DOI: 10.15468/dl.futxgc
title: Occurrence Download

description: A dataset containing 1275318 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trifolium repens",
 "Year 1975-2024"
 ]
}

 The dataset includes 1275318 records from 4065 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007804-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/uwj7nm74
title: SRBench

description: A large benchmarking effort that includes a dataset repository curated for SR, as well as a benchmarking library designed to allow researchers to easily contribute methods.
DOI: 10.57702/2d46h6mw
title: Shen fMRI dataset

description: fMRI dataset for model training: The fMRI data originates from Shen et al. (2019). This Shen fMRI dataset recorded human brain fMRI signals from three subjects while they focused on the center of the screen viewing natural images from ImageNet.
DOI: 10.57702/vf2ybnsy
title: Synthetic Retinotopic Mapping Dataset

description: The dataset used in the paper is a synthetic dataset for testing the proposed Topological Receptive Field (tRF) model.
DOI: 10.57702/lbawszk2
title: NYU

description: The dataset used for testing the HandVoxNet approach for 3D hand shape and pose estimation from a single depth map.
DOI: 10.57702/kl4rk6x4
title: PHOCNet

description: The dataset used for word spotting in handwritten documents.
DOI: 10.57702/0v7fwyda
title: ToyADMOS

description: ToyADMOS: A dataset of miniature-machine operating sounds for anomalous sound detection
DOI: 10.57702/b0c4mru5
title: Microsoft voxelized upper bodies-a voxelized point cloud dataset

description: Microsoft voxelized upper bodies-a voxelized point cloud dataset.
DOI: 10.15468/dl.y92zzq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76176 24.03346, 88.68487399999999 24.027883, 88.6099 24.011291, 88.538702 23.984096, 88.47304699999999 23.946975, 88.41455999999999 23.900849, 88.36468499999999 23.846863, 88.324645 23.786355, 88.295419 23.720822, 88.27771 23.651884, 88.271939 23.581243, 88.278228 23.510639, 88.296403 23.441809, 88.32600100000001 23.376444, 88.36627799999999 23.316147, 88.416236 23.262396, 88.47463999999999 23.216504, 88.540057 23.179595, 88.610884 23.152568, 88.685391 23.136084, 88.76176 23.130544, 88.838129 23.136084, 88.91263600000001 23.152568, 88.983463 23.179595, 89.04888 23.216504, 89.10728400000001 23.262396, 89.157242 23.316147, 89.197519 23.376444, 89.22711700000001 23.441809, 89.24529200000001 23.510639, 89.251581 23.581243, 89.24581000000001 23.651884, 89.228101 23.720822, 89.198875 23.786355, 89.158835 23.846863, 89.10896 23.900849, 89.050473 23.946975, 88.984818 23.984096, 88.91361999999999 24.011291, 88.838646 24.027883, 88.76176 24.03346))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080158-251120083545085/datasets/export for details.
DOI: 10.57702/kynwb73w
title: Treasure Mountain

description: The dataset used in the paper is the Treasure Mountain environment, which is a 2D environment that requires the agent to navigate through a mountain to find a treasure.
DOI: 10.57702/om7b04vc
title: FreeNeRF

description: The FreeNeRF dataset is used for few-shot novel view synthesis.
DOI: 10.57702/n2vhae1n
title: TUM VI Benchmark

description: The TUM VI benchmark datasets are a collection of visual-inertial odometry datasets.
DOI: 10.57702/2q3kn82k
title: Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets

description: Skin cancer is one of the most common types of cancer in the world. Different computer-aided diagnosis systems have been proposed to tackle skin lesion diagnosis, most of them based in deep convolutional neural networks. However, recent advances in computer vision achieved state-of-art results in many tasks, notably Transformer-based networks. We explore and evaluate advances in computer vision architectures, training methods and multimodal feature fusion for skin lesion diagnosis task.
DOI: 10.57702/xt0penas
title: PanNuke

description: PanNuke dataset consists of 6078 H&E tiles, each of size 256x256, covering 19 different tissue types. It was designed for nuclei instance classification and segmentation and contains annotations of five types of nuclei: neoplastic, inflammatory, connective/soft tissue, dead, and healthy epithelial.
DOI: 10.57702/os9hrrbk
title: A Wideband Distributed Massive MIMO Channel Sounder

description: A channel sounder for distributed massive MIMO based on the NI USRP X410.
DOI: 10.57702/zxzax5ph
title: Malaria cell image classification

description: Malaria cell image classification dataset
DOI: 10.57702/c5l09i1m
title: Bivariate Gaussian likelihood example

description: The dataset used in the paper is a bivariate Gaussian likelihood example with uncorrelated priors.
DOI: 10.57702/f1iif4tu
title: Child Growth Monitor Dataset

description: A dataset of depth images collected from children under 5 years of age using a smartphone, used for height estimation.
DOI: 10.57702/03ub823e
title: Flickr8k Audio Captions Corpus

description: Flickr8k Audio Captions Corpus is a large-scale dataset of audio and text pairs.
DOI: 10.57702/h3vv27ca
title: One-Shot Generation of Near-Optimal Topology through Theory-Driven Machine Learning

description: The proposed learning mechanism uses a compliance minimization problem as a running example to learn a one-shot solution generator for topology optimization.
DOI: 10.57702/ewblwitm
title: MICCAI grand challenge on 6-month infant brain MRI segmentation

description: 10 images are provided as training data, 13 images are used to evaluate the method in the challenge. For all patients, T1- and T2-weighted images are provided.
DOI: 10.57702/gbkm75tj
title: ICL-NUIM dataset

description: The ICL-NUIM dataset used for testing the CodedVO method, containing two scenes: (of-krt2) and (lr-krt2).
DOI: 10.57702/l27n00kn
title: INbreast dataset

description: Mammogram classiﬁcation is directly related to computer-aided di-agnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate.
DOI: 10.57702/1q8ywfn4
title: Traffic ST Signal Dataset

description: The dataset is a collection of 345 traffic ST signals, each representing a traffic state.
DOI: 10.57702/le82m3ma
title: Airbnb price listing data

description: Airbnb is a company providing an online marketplace for rental apartments. In this example, our goal is to construct a model to predict the listing price of apartments with all available information.
DOI: 10.57702/9rfcmle7
title: Efficient and Scalable Chinese Vector Font Generation via Component Composition

description: Chinese vector font generation is challenging due to the complex structure and huge amount of Chinese characters. Recent advances remain limited to generating a small set of characters with simple structure. This work proposes a novel framework for efficient and scalable Chinese vector font generation via component composition.
DOI: 10.57702/iic6w36p
title: ISRUC-S3 Dataset

description: ISRUC-S3 dataset contains data from a sleep stage classification system, including EEG signals.
DOI: 10.57702/shgyn9jj
title: VCSEL reliability data

description: VCSEL reliability data derived from accelerated aging tests conducted under different operating conditions.
DOI: 10.57702/f9ikfx6n
title: CASIA-MFSD Database

description: Face authentication is being increasingly used in our daily life for tasks such as phone unlocking, access authentication and face-payment owing to its convenience and high–efﬁciency.
DOI: 10.57702/nevc9rke
title: IC19-ReCTS

description: The IC19-ReCTS dataset is a robust reading challenge on reading Chinese text on signboard.
DOI: 10.57702/549w7atp
title: ORL5

description: The dataset used in the paper is ORL5, which consists of 10 different images of 40 distinct subjects taken at different times, varying the lightning, facial expression, and facial details.
DOI: 10.57702/sh54jiqt
title: Google Aerial Photo to Maps dataset

description: The dataset used for unsupervised image-to-image translation, consisting of 3292 pairs of aerial photos and corresponding maps.
DOI: 10.57702/80wuv0h1
title: Botnet

description: The dataset used in this paper is the Botnet dataset, a collection of network traffic data.
DOI: 10.57702/3kbjtmse
title: UK-PODS-ALIGN

description: This work showcases a cost-effective method for generating training data for speech processing tasks. The dataset UK-PODS-ALIGN is a dataset that features modern conversational Ukrainian language.
DOI: 10.57702/kjptkcj4
title: Learning to Evaluate Image Captioning

description: Evaluation metrics for image captioning face two challenges. Firstly, commonly used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not correlate well with human judgments. Secondly, each metric has well known blind spots to pathological caption constructions, and rule-based metrics lack provisions to repair such blind spots once identified.
DOI: 10.57702/h4oq41h9
title: Peptide Structure Prediction

description: Peptides are short chains of amino acids, referred to as mini-proteins, formed through dehydration.
DOI: 10.57702/8qryqwot
title: RAGSummData

description: The dataset used in the paper is a collection of dialogues and prompts for training a model to perform retrieval-augmented generation (RAG) based summarization. The dataset is designed to test the robustness of large language models (LLMs) in various scenarios.
DOI: 10.57702/pjsxt9qv
title: MS MARCO and DL-Typo

description: Two datasets used in the paper: MS MARCO and DL-Typo.
DOI: 10.57702/chrbqvzk
title: Financial dataset

description: Real-world financial dataset used in experiments, sourced from reputable financial databases and APIs like Yahoo Finance and Alpaca News API.
DOI: 10.57702/8490o9ay
title: NTU-120 minus 60

description: A large-scale labeled RGB-Depth dataset for action recognition, containing 942 training and 132 validation videos for 51 action classes.
DOI: 10.57702/w9zcphkn
title: VCR

description: The dataset used in the paper for visual commonsense reasoning.
DOI: 10.15156/bio/sh0155738.10fu
title: SH0155738.10FU

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/sh0155739.10fu
title: SH0155739.10FU

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/sh0155740.10fu
title: SH0155740.10FU

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.