cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10515778
title: Artifacts: A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools

description: Context. Rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing software engineering in every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations are experiencing friction in the adoption of ML-based technologies, mainly due to the current shortage of ML professionals. In this context, Automated Machine Learning (AutoML) techniques have been presented as a promising solution to democratize ML adoption, even in the absence of specialized people.

Objective. Our research aims to provide an overview of the evidence on the benefits and limitations of using AutoML tools.

Method. We conducted a Multivocal Literature Review (MLR), which allowed us to identify 54 sources from the academic literature and 108 sources from the grey literature reporting on AutoML benefits and limitations. We extracted explicitly reported benefits and limitations from the papers and applied the thematic analysis method for synthesis.

Results. Overall, we identified 18 reported benefits and 25 limitations. Concerning the benefits, we highlight that AutoML tools can help streamline the core steps of ML workflows, namely data preparation, feature engineering, model construction, and hyperparameter tuning—with concrete benefits on model performance, efficiency, and scalability. In addition, AutoML empowers both novice and experienced data scientists, promoting ML accessibility. On the other hand, we highlight several limitations that may represent obstacles to the widespread adoption of AutoML. For instance, AutoML tools may introduce barriers to transparency and interoperability, exhibit limited flexibility for complex scenarios, and offer inconsistent coverage of the ML workflow.

Conclusions. The effectiveness of AutoML in facilitating the adoption of machine learning by users may vary depending on the specific tool and the context in which it is used. As of today, AutoML tools are used to increase human expertise rather than replace it, and, as such, they require skilled users.
DOI: 10.5281/zenodo.10515779
title: Artifacts: A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools

description: Context. Rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing software engineering in every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations are experiencing friction in the adoption of ML-based technologies, mainly due to the current shortage of ML professionals. In this context, Automated Machine Learning (AutoML) techniques have been presented as a promising solution to democratize ML adoption, even in the absence of specialized people.

Objective. Our research aims to provide an overview of the evidence on the benefits and limitations of using AutoML tools.

Method. We conducted a Multivocal Literature Review (MLR), which allowed us to identify 54 sources from the academic literature and 108 sources from the grey literature reporting on AutoML benefits and limitations. We extracted explicitly reported benefits and limitations from the papers and applied the thematic analysis method for synthesis.

Results. Overall, we identified 18 reported benefits and 25 limitations. Concerning the benefits, we highlight that AutoML tools can help streamline the core steps of ML workflows, namely data preparation, feature engineering, model construction, and hyperparameter tuning—with concrete benefits on model performance, efficiency, and scalability. In addition, AutoML empowers both novice and experienced data scientists, promoting ML accessibility. On the other hand, we highlight several limitations that may represent obstacles to the widespread adoption of AutoML. For instance, AutoML tools may introduce barriers to transparency and interoperability, exhibit limited flexibility for complex scenarios, and offer inconsistent coverage of the ML workflow.

Conclusions. The effectiveness of AutoML in facilitating the adoption of machine learning by users may vary depending on the specific tool and the context in which it is used. As of today, AutoML tools are used to increase human expertise rather than replace it, and, as such, they require skilled users.
DOI: 10.6084/m9.figshare.25003058
title: Tables 1-3 and raw data

description: This study reveals that latent infection with <i>T. gondii</i> is associated with a lower risk of accidents among motorcycle riders, while CMV and HSV infections did not significantly difference between case than the control groups. Since this is the first study that assessed the association of latent infections among motorcycle riders, our results should be interpreted with caution; hence, further studies with a greater sample size should be recommended in this regard.
DOI: 10.6084/m9.figshare.25003058.v1
title: Tables 1-3 and raw data

description: This study reveals that latent infection with <i>T. gondii</i> is associated with a lower risk of accidents among motorcycle riders, while CMV and HSV infections did not significantly difference between case than the control groups. Since this is the first study that assessed the association of latent infections among motorcycle riders, our results should be interpreted with caution; hence, further studies with a greater sample size should be recommended in this regard.
DOI: 10.15468/ebnp2u
title: 2013-2015 Marine Biological Association (MBA) Wembury Devon Crab Survey

description: Targeted search for species of crab conducted by the Marine Biological Association at Wembury Bay.
DOI: 10.6084/m9.figshare.24306013
title: mmDetection results

description: Containing subfolders: faster_rcnn_r50_fpn_adhd/ and faster_rcnn_r50_gfap/. Both of them contain the training configuration, loss curves, metrics and evaluations on each individual test set against the GT annotations. We share these results for reproducibility.
DOI: 10.6084/m9.figshare.24306034
title: ALDH1L1 stained MIRAX file to slide 21_03_21_100_2017_12_ALDH1L1_1_1000_AC

description: ALDH1L1 stained MIRAX file to slide 21_03_21_100_2017_12_ALDH1L1_1_1000_AC
DOI: 10.6084/m9.figshare.24306034.v1
title: ALDH1L1 stained MIRAX file to slide 21_03_21_100_2017_12_ALDH1L1_1_1000_AC

description: ALDH1L1 stained MIRAX file to slide 21_03_21_100_2017_12_ALDH1L1_1_1000_AC
DOI: 10.6084/m9.figshare.24306037.v1
title: ALDH1L1 stained MIRAX file to slide 21_04_20_104_12_a_ALDH1L1_1_1000_AC

description: ALDH1L1 stained MIRAX file to slide 21_04_20_104_12_a_ALDH1L1_1_1000_AC
DOI: 10.6084/m9.figshare.24306049
title: GFAP stained MIRAX file to slide 21_05_11_100_2017_12_GFAP_1_500_mwcitr

description: GFAP stained MIRAX file to slide 21_05_11_100_2017_12_GFAP_1_500_mwcitr
DOI: 10.6084/m9.figshare.24306049.v1
title: GFAP stained MIRAX file to slide 21_05_11_100_2017_12_GFAP_1_500_mwcitr

description: GFAP stained MIRAX file to slide 21_05_11_100_2017_12_GFAP_1_500_mwcitr
DOI: 10.6084/m9.figshare.24306421
title: ALDH1L1 stained MIRAX file to slide 21_05_11_104_12_I_ALDH1L_1_1000_AC

description: 21_05_11_104_12_I_ALDH1L_1_1000_AC
DOI: 10.6084/m9.figshare.24306037
title: ALDH1L1 stained MIRAX file to slide 21_04_20_104_12_a_ALDH1L1_1_1000_AC

description: ALDH1L1 stained MIRAX file to slide 21_04_20_104_12_a_ALDH1L1_1_1000_AC
DOI: 10.6084/m9.figshare.24306421.v1
title: ALDH1L1 stained MIRAX file to slide 21_05_11_104_12_I_ALDH1L_1_1000_AC

description: 21_05_11_104_12_I_ALDH1L_1_1000_AC
DOI: 10.6084/m9.figshare.24314080
title: GFAP stained MIRAX file to slide 21_04_20_NP104_12_a_GFAP_1_500_mwcitr

description: GFAP stained MIRAX file to slide 21_04_20_NP104_12_a_GFAP_1_500_mwcitr
DOI: 10.6084/m9.figshare.24314080.v1
title: GFAP stained MIRAX file to slide 21_04_20_NP104_12_a_GFAP_1_500_mwcitr

description: GFAP stained MIRAX file to slide 21_04_20_NP104_12_a_GFAP_1_500_mwcitr
DOI: 10.6084/m9.figshare.24314083
title: ALDH1L1 stained MIRAX file to slide 21_05_11_100_2017_29_ALDH1L1_1_1000_AC

description: ALDH1L1 stained MIRAX file to slide 21_05_11_100_2017_29_ALDH1L1_1_1000_AC
DOI: 10.6084/m9.figshare.24306427
title: GFAP stained MIRAX file to slide 21_05_11_104_12_I_GFAP_1_500_mwcitr

description: GFAP stained MIRAX file to slide 21_05_11_104_12_I_GFAP_1_500_mwcitr
DOI: 10.6084/m9.figshare.24306427.v1
title: GFAP stained MIRAX file to slide 21_05_11_104_12_I_GFAP_1_500_mwcitr

description: GFAP stained MIRAX file to slide 21_05_11_104_12_I_GFAP_1_500_mwcitr
DOI: 10.6084/m9.figshare.24314083.v1
title: ALDH1L1 stained MIRAX file to slide 21_05_11_100_2017_29_ALDH1L1_1_1000_AC

description: ALDH1L1 stained MIRAX file to slide 21_05_11_100_2017_29_ALDH1L1_1_1000_AC
DOI: 10.6084/m9.figshare.24314086
title: ALDH1L1 stained Aperio file to slide ID26698

description: ALDH1L1 stained Aperio file to slide ID26698
DOI: 10.6084/m9.figshare.24314089
title: ALDH1L1 stained Aperio file to slide ID26705

description: ALDH1L1 stained Aperio file to slide ID26705
DOI: 10.6084/m9.figshare.24314383
title: ALDH1L1 stained Aperio image to slide ID26710

description: ALDH1L1 stained Aperio image to slide ID26710
DOI: 10.6084/m9.figshare.24314086.v1
title: ALDH1L1 stained Aperio file to slide ID26698

description: ALDH1L1 stained Aperio file to slide ID26698
DOI: 10.6084/m9.figshare.24314089.v1
title: ALDH1L1 stained Aperio file to slide ID26705

description: ALDH1L1 stained Aperio file to slide ID26705
DOI: 10.6084/m9.figshare.24314092
title: ALDH1L1 stained Aperio file to slide ID26707

description: ALDH1L1 stained Aperio file to slide ID26707
DOI: 10.6084/m9.figshare.24314092.v1
title: ALDH1L1 stained Aperio file to slide ID26707

description: ALDH1L1 stained Aperio file to slide ID26707
DOI: 10.6084/m9.figshare.24314383.v1
title: ALDH1L1 stained Aperio image to slide ID26710

description: ALDH1L1 stained Aperio image to slide ID26710
DOI: 10.6084/m9.figshare.24314386.v1
title: GFAP stained Aperio image to slide ID26769

description: GFAP stained Aperio image to slide ID26769
DOI: 10.6084/m9.figshare.24314500
title: GFAP stained Aperio file to slide ID26774

description: GFAP stained Aperio file to slide ID26774
DOI: 10.6084/m9.figshare.24314500.v1
title: GFAP stained Aperio file to slide ID26774

description: GFAP stained Aperio file to slide ID26774
DOI: 10.6084/m9.figshare.24314386
title: GFAP stained Aperio image to slide ID26769

description: GFAP stained Aperio image to slide ID26769
DOI: 10.6084/m9.figshare.24314503
title: GFAP stained Aperio file to slide ID26780

description: GFAP stained Aperio file to slide ID26780
DOI: 10.6084/m9.figshare.24314503.v1
title: GFAP stained Aperio file to slide ID26780

description: GFAP stained Aperio file to slide ID26780
DOI: 10.6084/m9.figshare.24314506
title: GFAP stained Aperio file to slide ID44082

description: GFAP stained Aperio file to slide ID44082
DOI: 10.6084/m9.figshare.24314506.v1
title: GFAP stained Aperio file to slide ID44082

description: GFAP stained Aperio file to slide ID44082
DOI: 10.6084/m9.figshare.24314509
title: GFAP stained Aperio file to slide ID44084

description: GFAP stained Aperio file to slide ID44084
DOI: 10.6084/m9.figshare.24314509.v1
title: GFAP stained Aperio file to slide ID44084

description: GFAP stained Aperio file to slide ID44084
DOI: 10.6084/m9.figshare.24428167
title: ALDH1L1 stained patches with annotations

description: The folder contains the train/ and individual test splits: test_05019_cohort_1/, test_05019_cohort_2/ and test_03557/ sub folders with the extracted patches from the ALDH1L1 stained tissue sections. Each of them have corresponding annotations with the exact same name. 

test_*_cohort.json: consensus annotations corresponding to each subset
test_*_*_*.json: individual junior, mid-level (medior) or expert test set annotations for the specified subset
train.json: the train set expert annotations
DOI: 10.6084/m9.figshare.24428170
title: GFAP stained patches with annotations

description: The folder contains the train/ and individual test splits: test_05019_cohort_1/, test_05019_cohort_2/ and test_03557/ sub folders. Each of them have corresponding annotations with the exact same name. 

test_*_consensus.json: consensus annotations corresponding to each subset
test_*_*_*.json: individual junior, mid-level (medior) or expert test set annotations for the specified subset
train.json: the train set expert annotations
DOI: 10.34740/kaggle/dsv/7413552
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/7413558
title: Worldwide Internet users as % of populationti
DOI: 10.5281/zenodo.8254381
title: empty_60SDS_Na_DESAmber_TIP4P_293K_10fsSF_replica1

description: empty_60SDS_Na_DESAmber_TIP4P_293K_10fsSF_replica1
DOI: 10.5281/zenodo.8254382
title: empty_60SDS_Na_DESAmber_TIP4P_293K_10fsSF_replica1

description: empty_60SDS_Na_DESAmber_TIP4P_293K_10fsSF_replica1
DOI: 10.34740/kaggle/dsv/7413593
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.8254392
title: empty_60SDS_Na_DESAmber_TIP4P_307K_replica2_from_whole_charmm

description: empty_60SDS_Na_DESAmber_TIP4P_307K_replica2_from_whole_charmm
DOI: 10.5281/zenodo.8254393
title: empty_60SDS_Na_DESAmber_TIP4P_307K_replica2_from_whole_charmm

description: empty_60SDS_Na_DESAmber_TIP4P_307K_replica2_from_whole_charmm
DOI: 10.6084/m9.figshare.25003151
title: Supplementary Material for: Identification and functional study of enhancers of EYA1, the causative gene of branchio-oto-renal syndrome

description: Introduction: Branchio-oto-renal syndrome (BOR syndrome) is a rare genetic disorder with an incidence of 1 in 40,000, affecting the development of multiple organs, including the branchio, ear and kidney. It is responsible for 2% of childhood deafness. Currently, variants in the coding regions of the main causative genes, such as EYA1, SIX1, and SIX5, explain only half of the disease’s etiology. Therefore, there is a need to explore the non-coding regions, which constitute the majority of the genome, especially the regulatory regions, as potential new causative factors. Method: In this study, we focused on the EYA1 gene, which accounts for over 40% of BOR syndrome cases, and conducted a screening of candidate enhancers within a 250 kb region upstream and downstream of the gene using comparative genomics. We characterized the enhancer activities of these candidates in zebrafish using the Tol2 transposon system. Results: Our findings revealed that out of the 11 conserved non-coding elements (CNEs) examined, four exhibited enhancer activity. Notably, CNE16.39 and CNE16.45 displayed tissue-specific enhancer activity in the ear. CNE16.39required the full-length 206 bp sequence for inner-ear-specific expression, while the core functional region of CNE16.45 was identified as 136 bp. Confocal microscopy results demonstrated that both CNE16.39 and CNE16.45 drove the expression of GFP in the sensory region of the crista of the inner ear in zebrafish, consistent with the expression pattern of eya1. Conclusion: This study contributes to the understanding of the regulatory network governing EYA1 expression and offers new insights to further clarify the pathogenic role of EYA1 in BOR syndrome.
DOI: 10.6084/m9.figshare.25003151.v1
title: Supplementary Material for: Identification and functional study of enhancers of EYA1, the causative gene of branchio-oto-renal syndrome

description: Introduction: Branchio-oto-renal syndrome (BOR syndrome) is a rare genetic disorder with an incidence of 1 in 40,000, affecting the development of multiple organs, including the branchio, ear and kidney. It is responsible for 2% of childhood deafness. Currently, variants in the coding regions of the main causative genes, such as EYA1, SIX1, and SIX5, explain only half of the disease’s etiology. Therefore, there is a need to explore the non-coding regions, which constitute the majority of the genome, especially the regulatory regions, as potential new causative factors. Method: In this study, we focused on the EYA1 gene, which accounts for over 40% of BOR syndrome cases, and conducted a screening of candidate enhancers within a 250 kb region upstream and downstream of the gene using comparative genomics. We characterized the enhancer activities of these candidates in zebrafish using the Tol2 transposon system. Results: Our findings revealed that out of the 11 conserved non-coding elements (CNEs) examined, four exhibited enhancer activity. Notably, CNE16.39 and CNE16.45 displayed tissue-specific enhancer activity in the ear. CNE16.39required the full-length 206 bp sequence for inner-ear-specific expression, while the core functional region of CNE16.45 was identified as 136 bp. Confocal microscopy results demonstrated that both CNE16.39 and CNE16.45 drove the expression of GFP in the sensory region of the crista of the inner ear in zebrafish, consistent with the expression pattern of eya1. Conclusion: This study contributes to the understanding of the regulatory network governing EYA1 expression and offers new insights to further clarify the pathogenic role of EYA1 in BOR syndrome.
DOI: 10.5281/zenodo.10491408
title: pNEUMA dataset

description: pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. A unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was not available until now, that researchers from different disciplines around the globe can use to develop and test their own models.

 

How are the .csv files organized?

For each .csv file the following apply:





each row represents the data of a single vehicle

the first 10 columns in the 1st row include the columns’ names

the first 4 columns include information about the trajectory like the unique trackID, the type of vehicle, the distance traveled in meters and the average speed of the vehicle in km/h

the last 6 columns are then repeated every 6 columns based on the time frequency. For example, column_5 contains the latitude of the vehicle at time column_10, and column­­­_11 contains the latitude of the vehicle at time column_16.

Speed is in km/h, Longitudinal and Lateral Acceleration in m/sec2 and time in seconds.



 

For more details about the pNEUMA dataset, please check our website at https://open-traffic.epfl.ch
DOI: 10.5281/zenodo.10491409
title: pNEUMA dataset

description: pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. A unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was not available until now, that researchers from different disciplines around the globe can use to develop and test their own models.

 

How are the .csv files organized?

For each .csv file the following apply:





each row represents the data of a single vehicle

the first 10 columns in the 1st row include the columns’ names

the first 4 columns include information about the trajectory like the unique trackID, the type of vehicle, the distance traveled in meters and the average speed of the vehicle in km/h

the last 6 columns are then repeated every 6 columns based on the time frequency. For example, column_5 contains the latitude of the vehicle at time column_10, and column­­­_11 contains the latitude of the vehicle at time column_16.

Speed is in km/h, Longitudinal and Lateral Acceleration in m/sec2 and time in seconds.



 

For more details about the pNEUMA dataset, please check our website at https://open-traffic.epfl.ch
DOI: 10.6084/m9.figshare.24787356
title: Supplementary Material for: Twelve-months outcomes after metabolic and bariatric surgery among youths participating in a structured preparation and follow-up program – Results of the Youth with Extreme obesity Study (YES)

description: Introduction: While invasive and associated with risks, metabolic and bariatric surgery (MBS) can promote sustained weight loss and substantial health benefits in youths with extreme obesity.The path toward informed decision making for or against MBS is poorly characterized and postoperative follow-up to assess risks and benefits is inconsistent. In youths with extreme obesity we aimed to evaluate decision making toward MBS, as well as MBS outcomes and adherence with follow-up and recommendations in the setting of a structured pre- and post- MBS.
Methods: Participants were recruited in the setting of the multicenter “Youth with Extreme Obesity Study” (YES). YES is a cohort study in adolescents and young adults aged 14–24 years with obesity (BMI≥30.0 kg/m2) who were recruited at four medical centers and one job center in Germany between 2012 and 2018. Participants at two medical centers with BMI≥35kg/m2, aged 14-24 years, and interested in pursuing MBS were included in the subproject 3 “Safety and effectiveness of weight loss surgery in adolescents with severe obesity within a structured pre- and post-surgery treatment program – an observational study” that comprised a 2-months pre- and 12-months post-MBS program. 
Results: Twenty-eight of 169 youths (17%) with BMI≥35kg/m2 were interested in MBS. Twenty-six fulfilled published eligibility criteria for MBS and participated in the structured pre- MBS preparation program. Of these, 9 participants (2 female) decided against, and 17 (n=11 females) decided for MBS (sleeve gastrectomy). The 12-months follow-up rate was high (16/17 [94%]) and all participants achieved significant weight reduction (ΔBMI:-16.1±5.6 kg/m2). Eleven of 16 participants (69%) reported taking the prescribed dietary supplements in the first year after MBS, but only five of them (31%) did so daily. In contrast to the high 12-month retention rate, follow-up after completion of the structured program was low at 24-months (9/16 [56%]) and at 36-months (5/15 [36%]) respectively. 
Conclusion: Participants demonstrated active decision making for or against MBS and high adherence with the structured pre- and 12 months post- MBS program, but participation was low thereafter. These findings endorse the need for longer-term structured post-MBS programs to capture long-term outcomes and provide adequate care in this vulnerable group at the transition to adulthood.