cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2024-01.3011009/2502856
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011010/2502857
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.chybae
title: Occurrence Download

description: A dataset containing 559 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Thalassarche chlororhynchos (Gmelin, 1789))",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 559 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003691-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3011013/2502858
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011014/2502859
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011023/2502860
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011024/2502861
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011027/2502862
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011031/2502863
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011037/2502864
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011040/2502865
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011041/2502866
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011042/2502867
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011043/2502868
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011044/2502869
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011045/2502870
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011048/2502871
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011050/2502872
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011052/2502873
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011053/2502874
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.vx7se9
title: Occurrence Download

description: A dataset containing 6766 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Hirundo semirufa Sundevall, 1850)",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 6766 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003693-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3011054/2502875
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011055/2502876
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011056/2502877
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011064/2502878
title: Data for EMSL Project 51287 from January 2024
DOI: 10.5281/zenodo.8080170
title: TRAPT: A multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data

description: It is challenging to identify regulatory transcriptional regulators (TRs), which control the expression of gene sets via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale multi-omics epigenomic data enables the representation of the complex epigenomic patterns of control of the regulatory elements and the regulators. In this study, we propose Transcription Regulator Activity Prediction Tool (TRAPT), a multi-modality deep learning framework that can predict regulatory TRs in a queried gene set by using large-scale multi-omics epigenomic data that include information on histone modifications, ATAC-seq, and TR-ChIP-seq. We designed a two-stage knowledge-based model of distillation to learn the embedded nonlinear representations of upstream and downstream regulatory potentials, and merged multi-modality epigenomic features from the TRs and the queried gene sets to infer regulator activity. The results of experiments on 570 TR-related datasets showed that TRAPT outperformed state-of-the-art methods in predicting the TRs, especially in terms of forecasting transcription co-factors and chromatin regulators. Moreover, we successfully identified key TRs associated with diseases, genetic variations, cell-fate decisions, and tissues. Our method provided an innovative perspective on identifying TRs by using epigenomic data, and can help researchers better understand the mechanisms of regulation of gene expression.
DOI: 10.5281/zenodo.8080171
title: TRAPT: A multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data

description: It is challenging to identify regulatory transcriptional regulators (TRs), which control the expression of gene sets via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale multi-omics epigenomic data enables the representation of the complex epigenomic patterns of control of the regulatory elements and the regulators. In this study, we propose Transcription Regulator Activity Prediction Tool (TRAPT), a multi-modality deep learning framework that can predict regulatory TRs in a queried gene set by using large-scale multi-omics epigenomic data that include information on histone modifications, ATAC-seq, and TR-ChIP-seq. We designed a two-stage knowledge-based model of distillation to learn the embedded nonlinear representations of upstream and downstream regulatory potentials, and merged multi-modality epigenomic features from the TRs and the queried gene sets to infer regulator activity. The results of experiments on 570 TR-related datasets showed that TRAPT outperformed state-of-the-art methods in predicting the TRs, especially in terms of forecasting transcription co-factors and chromatin regulators. Moreover, we successfully identified key TRs associated with diseases, genetic variations, cell-fate decisions, and tissues. Our method provided an innovative perspective on identifying TRs by using epigenomic data, and can help researchers better understand the mechanisms of regulation of gene expression.
DOI: 10.25582/data.2024-01.3011065/2502879
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011066/2502880
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011067/2502881
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011068/2502882
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011071/2502883
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011073/2502884
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011074/2502885
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011075/2502886
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011077/2502887
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.u44xbz
title: Occurrence Download

description: A dataset containing 79875 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Monitoreo pasivo de fauna silvestre con cámaras trampa en el Magdalena Medio, Santander y Cesar, Colombia - Proyecto FIBRAS"
 ]
}

 The dataset includes 79875 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003692-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3011078/2502888
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011079/2502889
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011080/2502890
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011082/2502891
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011084/2502892
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011086/2502893
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011090/2502894
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.pdkxd7
title: Occurrence Download

description: A dataset containing 3296 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Coturnix delegorguei Delegorgue, 1847)",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 3296 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003695-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3011094/2502895
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011095/2502896
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011096/2502897
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011097/2502898
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011099/2502899
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011103/2502900
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011106/2502901
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011107/2502902
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011109/2502903
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011110/2502904
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011111/2502905
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011113/2502906
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011114/2502907
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011115/2502908
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011117/2502909
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011120/2502910
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011121/2502911
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011123/2502912
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011129/2502913
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.qcn6q8
title: Occurrence Download

description: A dataset containing 2284538 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Odonata"
 ]
}

 The dataset includes 2284538 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003689-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3011130/2502914
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011133/2502915
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011134/2502916
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011135/2502917
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011136/2502918
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.ghy4en
title: Occurrence Download

description: A dataset containing 40745 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Polyboroides typus A.Smith, 1829)",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 40745 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003696-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3011139/2502919
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011140/2502920
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011144/2502921
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011147/2502922
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011148/2502923
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011152/2502924
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011154/2502925
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011155/2502926
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011157/2502927
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011158/2502928
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011159/2502929
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011160/2502930
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011162/2502931
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011163/2502932
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011165/2502933
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011168/2502934
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011174/2502935
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011181/2502936
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011182/2502937
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011187/2502938
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011188/2502939
title: Data for EMSL Project 51287 from January 2024
DOI: 10.6084/m9.figshare.28236686
title: data.xlsx

description: Appropriate soil physical properties and nutrient availability are crucial for crop growth, development and yield. However, the interaction mechanisms between soil nutrients and physical properties, and their specific pathways of crop yield influence, remain inconclusive. We investigated the soil physicochemical properties and their mechanisms driving crop yields in Northeast China, focusing on six ecological zones: the Greater and Lesser Xing'an Mountains (XAL), Sanjiang Plain (SJP), Songnen Plain (SNP), Northwest Semi-arid Zone (WSA), Changbai Mountain Low Hills (CBM), and Sanjiang Plain (LHP). Soil samples from rice and maize fields collected in 2018 were analyzed. Rice yields were higher in central and eastern regions; maize yields were higher in central and southeastern regions. Soil bulk density and pH were elevated in the northwest semi-arid region; organic matter and nitrogen, phosphorus, and potassium contents were the highest in the Songnen Plain. pH has a negative regulatory effect on maize yield, the content of nitrogen, phosphorus, potassium and organic matter and bulk density have a positive effect on rice yield. Bulk has a negative regulatory effect on maizeyield, while the content of nitrogen, phosphorus, potassium and organic matter has a normal regulatory on maizeyield.
DOI: 10.6084/m9.figshare.28236686.v1
title: data.xlsx

description: Appropriate soil physical properties and nutrient availability are crucial for crop growth, development and yield. However, the interaction mechanisms between soil nutrients and physical properties, and their specific pathways of crop yield influence, remain inconclusive. We investigated the soil physicochemical properties and their mechanisms driving crop yields in Northeast China, focusing on six ecological zones: the Greater and Lesser Xing'an Mountains (XAL), Sanjiang Plain (SJP), Songnen Plain (SNP), Northwest Semi-arid Zone (WSA), Changbai Mountain Low Hills (CBM), and Sanjiang Plain (LHP). Soil samples from rice and maize fields collected in 2018 were analyzed. Rice yields were higher in central and eastern regions; maize yields were higher in central and southeastern regions. Soil bulk density and pH were elevated in the northwest semi-arid region; organic matter and nitrogen, phosphorus, and potassium contents were the highest in the Songnen Plain. pH has a negative regulatory effect on maize yield, the content of nitrogen, phosphorus, potassium and organic matter and bulk density have a positive effect on rice yield. Bulk has a negative regulatory effect on maizeyield, while the content of nitrogen, phosphorus, potassium and organic matter has a normal regulatory on maizeyield.
DOI: 10.25582/data.2024-01.3011190/2502940
title: Data for EMSL Project 51287 from January 2024
DOI: 10.15468/dl.hcq999
title: Occurrence Download

description: A dataset containing 3153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Cercotrichas signata (Sundevall, 1850))",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 3153 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003698-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2024-01.3011191/2502941
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011194/2502942
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011203/2502943
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011205/2502944
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011208/2502945
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011209/2502946
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011215/2502947
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011216/2502948
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011218/2502949
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011224/2502950
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011225/2502951
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011227/2502952
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011230/2502953
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011234/2502954
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011235/2502955
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011236/2502956
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011237/2502957
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011238/2502958
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011239/2502959
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011240/2502960
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011241/2502961
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011251/2502962
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011258/2502963
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011259/2502964
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011277/2502965
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011278/2502966
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011282/2502967
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011284/2502968
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011291/2502969
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011301/2502970
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011302/2502971
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011312/2502972
title: Data for EMSL Project 51287 from January 2024
DOI: 10.6084/m9.figshare.28236635
title: Dataset

description: <b>Facial Expression Recognition Dataset</b>This dataset supports research on facial expression recognition using visible and infrared modalities. It includes data for various facial expressions from two publicly available datasets: VIRI (five expressions: angry, happy, neutral, sad, and surprised) and NVIE (three expressions: fear, disgust, and happy). The dataset has been processed and prepared for training and evaluation of machine learning models.The dataset is designed for use with deep learning frameworks like PyTorch and supports experiments in feature extraction, model evaluation, and early fusion approaches for visible and infrared modalities.For details on the methodology, preprocessing steps, and evaluation metrics, please refer to the linked GitHub repository: https://github.com/naseemmuhammadtahir/raw-data.This dataset facilitates reproducibility and exploration of advanced models for facial expression recognition tasks in diverse modalities.
DOI: 10.25582/data.2024-01.3011313/2502973
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3011314/2502974
title: Data for EMSL Project 51287 from January 2024