cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24129012.v1
title: Code used to train models and produce input files for "<b>Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning</b>" (Riedling et al. 2023).

description: The Jupyter notebooks fungal_only_trained_model and NP_prediction_bacterial_and_fungal contain the code necessary to train the models. The Jupyter notebooks titled Running_predictions contain the code to make predictions on SM bioactivity from antiSMASH and RGI input. The Jupyter notebooks require the additional Python scripts so keep all scripts in the same directory as the notebooks. The scripts to extract the features from the antiSMASH and RGI files are included in the notebooks.
DOI: 10.5281/zenodo.10234492
title: Fine-scale map reveals highly variable recombination rates associated with genomic features in the Eurasian blackcap

description: This project estimates historical recombination rates of the Eurasian blackcap (Sylvia atricapilla) and the garden warbler (Sylvia borin) using a linkage disequilibrium-based approach. The population-scaled recombination rates were inferred using 'pyrho' considering the population's demographic information and mutation rates. The mutation rate was taken from a closely related species (flycatcher, Ficedula albicollis).

Here we report the recombination rate per site per generation (r, output of pyrho) for all the chromosomes along the genome for both species. 

Blackcap dataset file: WholeGenome_chromosomes_blackap_n38_Pen20_W50.gz

Garden warbler dataset file: WholeGenome_chromosomes_SylBor_n10_Pen20W50.gz

 

Additionally, we annotated the blackcap genome to evaluate the association between recombination rates and specific annotation features (e.g., genes, transcription start sites, promoters, 5'UTR, and 3'UTR regions). The annotation of the genome was based on RNA-seq and Iso-Seq data. It can be found with the name bSylAtr1.1.gff.
DOI: 10.5281/zenodo.7813727
title: Fine-scale map reveals highly variable recombination rates associated with genomic features in the Eurasian blackcap

description: This project estimates historical recombination rates of the Eurasian blackcap (Sylvia atricapilla) and the garden warbler (Sylvia borin) using a linkage disequilibrium-based approach. The population-scaled recombination rates were inferred using 'pyrho' considering the population's demographic information and mutation rates. The mutation rate was taken from a closely related species (flycatcher, Ficedula albicollis).

Here we report the recombination rate per site per generation (r, output of pyrho) for all the chromosomes along the genome for both species. 

Blackcap dataset file: WholeGenome_chromosomes_blackap_n38_Pen20_W50.gz

Garden warbler dataset file: WholeGenome_chromosomes_SylBor_n10_Pen20W50.gz

 

Additionally, we annotated the blackcap genome to evaluate the association between recombination rates and specific annotation features (e.g., genes, transcription start sites, promoters, 5'UTR, and 3'UTR regions). The annotation of the genome was based on RNA-seq and Iso-Seq data. It can be found with the name bSylAtr1.1.gff.
DOI: 10.5281/zenodo.7813728
title: Fine-scale map reveals highly variable recombination rates associated with genomic features in the European blackcap

description: In this project, we estimated historical recombination rates for the European blackcap (<em>Sylvia atricapilla</em>) and the garden warbler (<em>Sylvia borin)</em> using a linkage disequilibrium-based approach. The population-scaled recombination rates were inferred using a program called pyrho, which considers the population's demographic information and mutation rates. The mutation rate was taken from a close species (flycatcher, <em>Ficedula albicollis</em>). Here we report the recombination rate per site per generation (r, output of pyrho) for all the chromosomes along the genome for both species. Blackcap dataset file: WholeGenome_chromosomes_blackap_n38_Pen20_W50.gz Garden warbler dataset file: WholeGenome_chromosomes_SylBor_n10_Pen20W50.gz Additionally, we annotated the genome of the blackcap to evaluate the association of recombination rates with specific annotation features (e.g., genes, transcription start sites, promoters, 5'UTR, and 3'UTR regions). The annotation of the genome was based on RNA-seq and Iso-Seq data. It can be found with the name: bSylAtr1.1.gff
DOI: 10.6084/m9.figshare.24814071
title: National Occurrences of Chengshi Kaihuang (Excluding Private Social Media Posts)

description: National Occurrences of Chengshi Kaihuang (Excluding Private Social Media Posts)
DOI: 10.6084/m9.figshare.24813888
title: Land Use History of Detected CK Sites

description: Land Use History of Detected CK Sites
DOI: 10.5281/zenodo.10145323
title: FOCAL Dataset: Ford-OLIVES Collaboration on Active Learning

description: In this dataset, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting. Annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. A practical motivation for active learning research is to minimize annotation-cost by selectively labeling informative samples that will maximize performance within a given budget constraint. However, previous work in video active learning lacks real-time annotation labels for accurately assessing cost minimization and instead operates under the assumption that annotation-cost scales linearly with the amount of data to annotate. This assumption does not take into account a variety of real-world confounding factors that contribute to  a nonlinear cost such as the effect of an assistive labeling tool and the variety of interactions within a scene such as occluded objects, weather, and motion of objects. FOCAL addresses this discrepancy by providing real annotation-cost labels for 126 video sequences across 69 unique city scenes with a variety of weather, lighting, and seasonal conditions. These videos have a wide range of interactions that are at the intersection of infrastructure-assisted autonomy and autonomous vehicle communities. We show through a statistical analysis of the FOCAL dataset that cost is more correlated with a variety of factors beyond just the length of a video sequence. We also introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data in order to achieve a better trade-off between annotation-cost and performance while also reducing floating point operations (FLOPS) overhead by at least 77.67%. We show how these approaches better reflect how annotations on videos are done in practice through a sequence selection framework. We further demonstrate the advantage of these approaches by introducing two performance-cost metrics and show that the best conformal active learning method is cheaper than the best traditional active learning method by 113 hours.  
This work took place at the OLIVES Lab @ Georgia Tech. 
The codebase associated with this work can be found at this Github.
Please refer to our lab-wide github for more information regarding the code associated with our other papers.
description: The data presented here is a processed version of the full FOCAL dataset in YOLO object detection format.
 

The associated github for this dataset can be located at: https://github.com/olivesgatech/FOCAL_Dataset.
 

The images and labels in Yolo format are located in the file focal_yolo.tar.gz.
 

Information regarding the annotation cost of every sequence in the dataset is located in the associated excel file.
 

Every image has the format:   nameofsequence _ nameofimage.jpg
 

The folder called split ids contains files that delineate which sequences belonged to the train, test, and val sets throughout the creation of the paper.
DOI: 10.5281/zenodo.10145325
title: FOCAL Dataset: Ford-OLIVES Collaboration on Active Learning

description: In this dataset, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting. Annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. A practical motivation for active learning research is to minimize annotation-cost by selectively labeling informative samples that will maximize performance within a given budget constraint. However, previous work in video active learning lacks real-time annotation labels for accurately assessing cost minimization and instead operates under the assumption that annotation-cost scales linearly with the amount of data to annotate. This assumption does not take into account a variety of real-world confounding factors that contribute to  a nonlinear cost such as the effect of an assistive labeling tool and the variety of interactions within a scene such as occluded objects, weather, and motion of objects. FOCAL addresses this discrepancy by providing real annotation-cost labels for 126 video sequences across 69 unique city scenes with a variety of weather, lighting, and seasonal conditions. These videos have a wide range of interactions that are at the intersection of infrastructure-assisted autonomy and autonomous vehicle communities. We show through a statistical analysis of the FOCAL dataset that cost is more correlated with a variety of factors beyond just the length of a video sequence. We also introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data in order to achieve a better trade-off between annotation-cost and performance while also reducing floating point operations (FLOPS) overhead by at least 77.67%. We show how these approaches better reflect how annotations on videos are done in practice through a sequence selection framework. We further demonstrate the advantage of these approaches by introducing two performance-cost metrics and show that the best conformal active learning method is cheaper than the best traditional active learning method by 113 hours.  
This work took place at the OLIVES Lab @ Georgia Tech. 
The codebase associated with this work can be found at this Github.
Please refer to our lab-wide github for more information regarding the code associated with our other papers.
description: The data presented here is a processed version of the full FOCAL dataset in YOLO object detection format.
 

The associated github for this dataset can be located at: https://github.com/olivesgatech/FOCAL_Dataset.
 

The images and labels in Yolo format are located in the file focal_yolo.tar.gz.
 

Information regarding the annotation cost of every sequence in the dataset is located in the associated excel file.
 

Every image has the format:   nameofsequence _ nameofimage.jpg
 

The folder called split ids contains files that delineate which sequences belonged to the train, test, and val sets throughout the creation of the paper.
DOI: 10.5281/zenodo.10396131
title: Dataset Región de Aysén

description: Actualizado con detalle de si medios son de propiedad municipal / no municipal
DOI: 10.5281/zenodo.10396132
title: Dataset Región de Aysén
DOI: 10.5281/zenodo.10396290
title: Dataset Región de Aysén

description: Actualizado con detalle de si medios son de propiedad municipal / no municipal
DOI: 10.5517/ccdc.csd.cc2fdfkv
title: CCDC 2246277: Experimental Crystal Structure Determination

description: Related Article: Chun-Hua Liu, Liangji Chen, Hao Zhang, Yunbin Li, Hongyu Lin, Lu Li, Junjie Wu, Chulong Liu, Zi-Ming Ye, Shengchang Xiang, Banglin Chen, Zhangjing Zhang|2023|Chem (Cell Press)|9|3532|doi:10.1016/j.chempr.2023.07.016
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fdfpz
title: CCDC 2246281: Experimental Crystal Structure Determination

description: Related Article: Chun-Hua Liu, Liangji Chen, Hao Zhang, Yunbin Li, Hongyu Lin, Lu Li, Junjie Wu, Chulong Liu, Zi-Ming Ye, Shengchang Xiang, Banglin Chen, Zhangjing Zhang|2023|Chem (Cell Press)|9|3532|doi:10.1016/j.chempr.2023.07.016
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fdhly
title: CCDC 2246340: Experimental Crystal Structure Determination

description: Related Article: Chun-Hua Liu, Liangji Chen, Hao Zhang, Yunbin Li, Hongyu Lin, Lu Li, Junjie Wu, Chulong Liu, Zi-Ming Ye, Shengchang Xiang, Banglin Chen, Zhangjing Zhang|2023|Chem (Cell Press)|9|3532|doi:10.1016/j.chempr.2023.07.016
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.10396327
title: nothocercus_nigrocapillus_zanfona_01.fasta.gz

description: .
DOI: 10.5281/zenodo.10396328
title: nothocercus_nigrocapillus_zanfona_01.fasta.gz

description: .
DOI: 10.24432/c5ss4g
title: RealWaste
DOI: 10.34740/kaggle/dsv/7218762
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.6084/m9.figshare.24846483
title: Data sets and software for: Mechanics of the foot and ankle joints during running using a multi-segment foot model compared with a single-segment model

description: Software and data for the following manuscript.<b>Title</b>: Mechanics of the foot and ankle joints during running using a multi-segment foot model compared with a single-segment model<b>Authors</b>: Justin C. Wager and John H. Challis<b>Affiliation Justin: </b>Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, USA.<b>Affiliation John: </b>Biomechanics Laboratory, Pennsylvania State University, University Park, USA<b>Journal:</b><b> </b>PLoS One.<b>Abstract</b>: The primary purpose of this study was to compare the ankle joint mechanics, during the stance phase of running, computed with a multi-segment foot model (MULTI; three segments) with a traditional single segment foot model (SINGLE). Traditional ankle joint models define all bones between the ankle and metatarsophalangeal joints as a single rigid segment (SINGLE). However, this contrasts with the more complex structure and mobility of the human foot, recent studies of walking using more multiple-segment models of the human foot have highlighted the errors arising in ankle kinematics and kinetics by using an oversimplified model of the foot. This study sought to compare whether ankle joint kinematics and kinetics during running are similar when using a single segment foot model (SINGLE) and a multi-segment foot model (MULTI). Seven participants ran at 3.1 m/s while the positions of markers on the shank and foot were tracked and ground reaction forces were measured. Ankle joint kinematics, resultant joint moments, joint work, and instantaneous joint power were determined using both the SINGLE and MULTI models. Differences between the two models across the entire stance phase were tested using statistical parametric mapping. During the stance phase, MULTI produced ankle joint angles that were typically closer to neutral and angular velocities that were reduced compared with SINGLE. Instantaneous joint power (p&lt;0.001) and joint work (p&lt;0.001) during late stance were also reduced in MULTI compared with SINGLE demonstrating the importance of foot model topology in analyses of the ankle joint during running. This study has highlighted that considering the foot as a rigid segment from ankle to MTP joint produces poor estimates of the ankle joint kinematics and kinetics, which has important implications for understanding the role of the ankle joint in running.
DOI: 10.6084/m9.figshare.24846429
title: MPDSR-Bangladesh data new

description: Maternal and perinatal death surveillance and response in Bangladesh: A case study on measuring impact through health information systems
DOI: 10.6084/m9.figshare.24846429.v2
title: MPDSR-Bangladesh data new

description: Maternal and perinatal death surveillance and response in Bangladesh: A case study on measuring impact through health information systems
DOI: 10.6084/m9.figshare.24846429.v1
title: MPDSR-Bangladesh data

description: Maternal and perinatal death surveillance and response in Bangladesh: A case study on measuring impact through health information systems
DOI: 10.34740/kaggle/dsv/7218772
title: Top Spotify Songs in 73 Countries (Daily Updated)
DOI: 10.34740/kaggle/dsv/7218776
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.5281/zenodo.7330445
title: Baboon approach behaviour data - Gorongosa National Park, Mozambique and Gombe National Park, Tanzania

description: See README file for more complete information

Overview: This dataset provides presence/absence data for each of 299 observations from Gorongosa National Park and 73 observations from Gombe National Park. Each observation is one "proximity event", where an adult/subadult baboon approached another adult/subadult baboon from outside 5 m to within 2 m. Data was first collected using BORIS (Friard and Gamba, 2016) following an ethogram (DOI:10.5281/zenodo.7314291), then cleaned using Python version 3.8.5 and R version 4.0.5 to format as a presence/absence matrix. 

Study species: Papio ursinus griseipes (chacma baboon) and Papio anubis (olive baboon) where specified

Dates of data collection: Videos in Gorongosa National Park were filmed 2018-10 to 2018-11 and 2019-07 to 2019-11. Videos in Gombe Stream national Park were recorded 2021-1 to 2021-5 and 2021-8 to 2021-9.

Geographic location of data collection: Gorongosa National Park, Mozambique and Gombe Stream National Park, Tanzania as specified by observationVideo coding software: BORIS (Friard and Gamba, 2016)

Recommended citation in BibTex form:

@ELECTRONIC{Muschinski2022b,  author = {Muschinski, Jana and Carvalho, Susana},  year = {2023},  title = {Baboon approach behaviour data - {Gorongosa National Park}, {Mozambique} and {Gombe National Park}, {Tanzania}},  doi = {10.5281/zenodo.10339023},  owner = {Paleo-Primate Project, Gorongosa National Park},  organization = {Paleo-Primate Project, Gorongosa National Park}} 
description: Organization: Paleo-Primate Project, Gorongosa National Park
DOI: 10.5281/zenodo.7330446
title: Chacma baboon approach behaviour data, Gorongosa National Park, Mozambique

description: <em><strong>See README file for more complete information</strong></em> <strong>Overview: </strong>This dataset provides presence/absence data for each of 341 observations. Each observation is one "proximity event", where an adult/subadult baboon approached another adult/subadult baboon from outside 5 m to within 2 m. Data was first collected using BORIS (Friard and Gamba, 2016) following an ethogram (DOI:10.5281/zenodo.7314291), then cleaned using Python version 3.8.5 and R version 4.0.5 to format as a presence/absence matrix. <strong>Study species: </strong>Papio ursinus griseipes (chacma baboon) <strong>Dates of data collection:</strong> Videos were filmed 2018-10 to 2018-11 and 2019-07 to 2019-11. Coding of videos occurred between 2020-10 to 2021-08. <strong>Geographic location of data collection:</strong> Gorongosa National Park, Mozambique <strong>Video coding software: </strong>BORIS (Friard and Gamba, 2016) <strong>Recommended citation in BibTex form:</strong> @ELECTRONIC{Muschinski2022b,<br> author = {Muschinski, Jana and Carvalho, Susana},<br> year = {2022},<br> title = {Chacma baboon approach behaviour dataset, {G}orongosa {N}ational {P}ark, {M}ozambique},<br> doi = {10.5281/zenodo.7330446},<br> owner = {Paleo-Primate Project, Gorongosa National Park},<br> organization = {Paleo-Primate Project, Gorongosa National Park}<br> }<br>
description: Organization: Paleo-Primate Project, Gorongosa National Park
DOI: 10.34740/kaggle/dsv/7218790
title: Full TMDB Movies Dataset 2023 (930K Movies)
DOI: 10.5281/zenodo.10390389
title: Diplomamunka 2023-Menyhárd István nyomában
DOI: 10.5281/zenodo.10390388
title: Diplomamunka 2023-Menyhárd István nyomában
DOI: 10.5281/zenodo.10396352
title: myiothlypis_coronata_zanfona_1.fasta.gz

description: ,
DOI: 10.5281/zenodo.10396353
title: myiothlypis_coronata_zanfona_1.fasta.gz

description: ,
DOI: 10.5281/zenodo.10363506
title: Multimodal datataset of EEG, ECG and audio recordings of individual musicians playing emotional music

description: This dataset contains EEG, ECG and audio recordings of 11 individual musicians playing emotional music on their instrument.
DOI: 10.6084/m9.figshare.24846510
title: PaiNN dataset and model for LiGa(SeO<sub>3</sub>)<sub>2</sub>

description: Can be used to run MD with the NeuralForceField repository. Units for the dataset are: kcal/mol for energy, kcal/mol-Å for forces, Å for units of length.
DOI: 10.34740/kaggle/dsv/7218935
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.5281/zenodo.10396388
title: eueides_lybia_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396389
title: eueides_lybia_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.24846513
title: PaiNN dataset and model for LiGa(SeS<sub>3</sub>)<sub>2</sub>

description: Can be used to run MD with the NeuralForceField repository. Units for the dataset are: kcal/mol for energy, kcal/mol-Å for forces, Å for units of length.
DOI: 10.6084/m9.figshare.24846513.v1
title: PaiNN dataset and model for LiGa(SeS<sub>3</sub>)<sub>2</sub>

description: Can be used to run MD with the NeuralForceField repository. Units for the dataset are: kcal/mol for energy, kcal/mol-Å for forces, Å for units of length.
DOI: 10.5281/zenodo.8381367
title: Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling

description: Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".
DOI: 10.5281/zenodo.8381368
title: Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling

description: Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".
DOI: 10.5281/zenodo.10396399
title: octopus_jollyorum_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396400
title: octopus_jollyorum_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396314
title: RSS-NET baseline results (Part 7)
DOI: 10.5281/zenodo.10396315
title: RSS-NET baseline results (Part 7)
DOI: 10.34740/kaggle/dsv/7219043
title: Partidos Futbol Argentino 18/19-2023 (no 2021)
DOI: 10.6084/m9.figshare.23703276.v1
title: Physical fitness and adolescents' attention

description: Dataset of the paper "Physical Fitness Is Related to Concentration Performance in Adolescents"
DOI: 10.34740/kaggle/dsv/7219144
title: Public Opinion Russia Ukraine War (Updated Daily)
DOI: 10.6084/m9.figshare.23703276
title: Physical fitness and adolescents' attention

description: Dataset of the paper "Physical Fitness Is Related to Concentration Performance in Adolescents"