cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8074853
title: A seven-Earth-radius helium-burning star inside a 20.5-min detached binary

description: 2024-02-19 updated for including the full MESA inlists of evolutionary models.

MESA_inlist.zip - the full MESA inlists of evolutionary models (including evolutions of single stars and binaries).

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This record contains the data of spectra, photometries and models used for the paper A seven-Earth-radius helium-burning star inside a 20.5-min detached binary, click here

J0526_fchart.png - the finder chart of J0526;

J0526_Keck_[0-5].spec  (in Keck_ori_spectra.zip)  - Keck I/LRIS spectra of J0526;

J0526_GTC_[0-6].spec  (in GTC_ori_spectra.zip)  - GTC/OSIRIS spectra of J0526;

J0526_GTC_syn_[0-6].spec  (in GTC_spectra_models.zip) - GTC/OSIRIS spectra and their best-fit TLUSTY/Synspec spectra;

J0526_RV_bary_corrected.dat - the barycenter-corrected radial velocities derived from LRIS and OSIRIS spectra;

J0526_SED.dat - the photometric fluxes in the broad-band SED and corresponding synthetic fluxes from the best-fit model spectrum;

ZTF_J0526_[r,g].dat (in light_curves.zip) - ZTF r-/g-band light cuvrs of J0526, the raw data were provided by NASA/IPAC Infrared Science Archive (https://irsa.ipac.caltech.edu );

Lijiang_J0526_[r,g].dat (in light_curves.zip) - LJT r-/g-band light cuvrs of J0526;

TMTS_J0526_L.dat (in light_curves.zip) - LJT L-band light cuvrs of J0526;

CHeB_c[0.32,0.33,0.34]+H1e-6.track (in MESA_models.zip) - the evolutionary tracks for core helium-burning stars with He-core mass of [0.32,0.33,0.34] Msun and H-envelope mass of 1e-6 Msun;

ZAHeMS_H0_sequence.dat  (in MESA_models.zip) - the zero-age He-star sequence (without H envelope);

ZAHeMS_H1e-6_sequence.dat  (in MESA_models.zip) - the zero-age He-star sequence (with H-envelope mass of 1e-6 M_sun );

binary_0.33+0.735.dat  (in MESA_models.zip) - the binary evolutionary track for a core helium-burning star (Mcore=0.33 M_sun, Menv=1e-6 M_sun ) and a CO WD (M=0.735 M_sun);

binary_0.36+0.735.dat  (in MESA_models.zip) - the binary evolutionary track for a core helium-burning star (Mcore=0.36 M_sun, Menv=1e-6 M_sun ) and a CO WD (M=0.735 M_sun);

J0526_[SED, RV, lc]_fit_corner.png - the corner plots for [SED, RV, light curve] MCMC fit.
DOI: 10.26179/cmn9-et04
title: Stitched and georeferenced aerial photos of the Casey Upper Melt Lake Feb 01, 1979
DOI: 10.6084/m9.figshare.25242574
title: Supplementary Material for: Combining surgery, radiotherapy and topical chemotherapy to prevent primary orbital exenteration for atypical caruncular melanoma: a case report

description: Introduction
This case report demonstrates the possibility of successful eye and vision-sparing therapy for caruncular melanoma. 

Case presentation
We present an atypical presentation of a caruncular melanoma. After excisional biopsy, residual flat conjunctival melanosis resolved using topical chemotherapy (5-fluorouracil), which was well tolerated. Relapse of the melanoma was treated with external beam radiotherapy, but the tumor grew despite treatment. Eighteen months after complete excision of the relapsed melanoma, the patient remains tumor-free while the eye and its function remain preserved. 

Conclusion
This case report suggests that aggressive eye-sparing therapy for caruncular melanoma combining surgery, adjuvant topical chemotherapy and external beam radiotherapy, can be an alternative for primary orbital exenteration.
DOI: 10.6084/m9.figshare.25242574.v1
title: Supplementary Material for: Combining surgery, radiotherapy and topical chemotherapy to prevent primary orbital exenteration for atypical caruncular melanoma: a case report

description: Introduction
This case report demonstrates the possibility of successful eye and vision-sparing therapy for caruncular melanoma. 

Case presentation
We present an atypical presentation of a caruncular melanoma. After excisional biopsy, residual flat conjunctival melanosis resolved using topical chemotherapy (5-fluorouracil), which was well tolerated. Relapse of the melanoma was treated with external beam radiotherapy, but the tumor grew despite treatment. Eighteen months after complete excision of the relapsed melanoma, the patient remains tumor-free while the eye and its function remain preserved. 

Conclusion
This case report suggests that aggressive eye-sparing therapy for caruncular melanoma combining surgery, adjuvant topical chemotherapy and external beam radiotherapy, can be an alternative for primary orbital exenteration.
DOI: 10.5281/zenodo.10558673
title: The Apollo 17 Regolith:  Induced Thermoluminescence Evidence for Formation by a Single Event ~100 Million Years Ago and Possibly the Presence of Tycho Material

description: This data archive contains all thermoluminescence glow curves for this study.

The 'glowcurve_database.csv' file contains all glow curves, which are named by the data of aquisition, with the comma seperating the number of the glow curve for that date. For each glow curve, we provide the following four columns: (1) timestamp_sec, which is the epoch timestamp of data point; (2) elapsed_sec, which is the time in seconds since starting to record the data; (3) temp_c, which is the sample temprature in degree celsius; and (4) intensity_cps, which is the intensity of the loght measured in counts per second.

The 'glowcurve_metadata.csv' file provides the information about the sample and the instrumental settings for which the glow curve was obtained. The metadata headers are: (1) Filename, which is the glowcurve number; (2) sample, which is the sample number: (3) pan, which is the number of the glow curve pan used for this sample; (4) mass, which is the mass of the sample weighed into the pan; (5) measurement, which specifies if this is natural TL, or induced TL (exposure to 90Sr beta radiation for specified amount of time, usually 3 minutes), or black body measurement; (6) the PMT scale (i.e., 'gain') of the photomultiplier tube in scientific expression. Glow curve measurments at different scale or gain settings need to be corrected for glow curve intensity camparison (i.e.. multiply by scale or normalize to one scale setting). 

The 'glowcurve_printout_metadata_pdf' provides pdf files of all glow curves, which contains a printout along with the metadata. Therefore, the pdf files do not provide any additional or new information beyond 'glowcurve_database.csv' and 'glowcurve_metadata.csv' files.

We also provide indivual glow curve files in the 'glowcurves_individual.zip' archive.
DOI: 10.5281/zenodo.10558674
title: The Apollo 17 Regolith:  Induced Thermoluminescence Evidence for Formation by a Single Event ~100 Million Years Ago and Possibly the Presence of Tycho Material

description: This data archive contains all thermoluminescence glow curves for this study.

The 'glowcurve_database.csv' file contains all glow curves, which are named by the data of aquisition, with the comma seperating the number of the glow curve for that date. For each glow curve, we provide the following four columns: (1) timestamp_sec, which is the epoch timestamp of data point; (2) elapsed_sec, which is the time in seconds since starting to record the data; (3) temp_c, which is the sample temprature in degree celsius; and (4) intensity_cps, which is the intensity of the loght measured in counts per second.

The 'glowcurve_metadata.csv' file provides the information about the sample and the instrumental settings for which the glow curve was obtained. The metadata headers are: (1) Filename, which is the glowcurve number; (2) sample, which is the sample number: (3) pan, which is the number of the glow curve pan used for this sample; (4) mass, which is the mass of the sample weighed into the pan; (5) measurement, which specifies if this is natural TL, or induced TL (exposure to 90Sr beta radiation for specified amount of time, usually 3 minutes), or black body measurement; (6) the PMT scale (i.e., 'gain') of the photomultiplier tube in scientific expression. Glow curve measurments at different scale or gain settings need to be corrected for glow curve intensity camparison (i.e.. multiply by scale or normalize to one scale setting). 

The 'glowcurve_printout_metadata_pdf' provides pdf files of all glow curves, which contains a printout along with the metadata. Therefore, the pdf files do not provide any additional or new information beyond 'glowcurve_database.csv' and 'glowcurve_metadata.csv' files.

We also provide indivual glow curve files in the 'glowcurves_individual.zip' archive.
DOI: 10.6084/m9.figshare.25242583.v1
title: Supplementary Material for: Thrombomodulin gene mutation and associated predisposing factors in familial collapsing glomerulopathy

description: Introduction: Collapsing glomerulopathy (CG) is a rare glomerular disease and its familial form is even rarer. CG and non-collapsing forms of focal segmental glomerulosclerosis (FSGS) may both be caused by pathogenic variants in the same genes, but there is less information on genetics of the former disease. We hypothesized that different hits (viral infection and genetic variants) may be involved in the development of a familial CG here described.
Methods: Renal and etiological routine evaluation, PVB19 serology, genetic tests including whole exome analysis and dosage of serum thrombomodulin (THBD) were performed in two siblings with CG, one healthy sister and their mother.
Results: The THBD gene variant p.A43T in homozygosity was identified in the proband and her affected brother, both with CG. The same mutation was identified in their mother in heterozygosity. Thrombomodulin levels were elevated in the serum of both affected siblings. They also had PVB19 positive serology and the G1 high-risk apolipoprotein L1 (APOL1) alelles in homozygosity. Their healthy sister had no PVB19 positive serology and no THBD nor APOL1 gene variants.
Conclusion: In this case of familial CG, THBD and APOL1 gene variants, and a previous PVB19 infection may be associated with the development of CG in a multi-hit process. In addition, the p.A43T THBD variant, identified in the affected siblings, has never been previously described in homozygosis, pointing to a likely autosomal recessive CG trait caused by this gene mutation.
DOI: 10.5281/zenodo.3762961
title: K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations

description: ABSTRACT: Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of naturalistic affective interaction data. Most existing emotion datasets do not support studying idiosyncratic emotions arising in the wild as they were collected in constrained environments. Therefore, studying emotions in the context of social interactions requires a novel dataset, and K-EmoCon is such a multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. Distinct from previous datasets, it includes emotion annotations from all three available perspectives: self, debate partner, and external observers. Raters annotated emotional displays at intervals of every 5 seconds while viewing the debate footage, in terms of arousal-valence and 18 additional categorical emotions. The resulting K-EmoCon is the first publicly available emotion dataset accommodating the multiperspective assessment of emotions during social interactions.


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| Changelog (last updated: Jul 7, 2020) |
+---------------------------------------+

* Version 1.0.0 (Jul 7, 2020):
    - Updated emotion_annotations.tar.gz:
        - Updated aggregated external annotations to support the reproduction of technical validation results.

* Version 0.2.0 (May 11, 2020):
    - Added data_quality_tables.tar.gz
    - Updated emotion_annotations.tar.gz
        - Newly added aggregated external annotations.
    - Updated metadata.tar.gz:
        - Added a new column to data_availability.csv to show the availability of aggregated external annotations.

* Version 0.1.0 (Apr 25, 2020):
    - Added debate_audios.tar.gz
    - Added debate_recordings.tar.gz
    - Added e4_data.tar.gz
    - Added emotion_annotations.tar.gz (self, partner, external)
    - Added metadata.tar.gz
    - Added neurosky_polar_data.tar.gz


 
description: When applying for the dataset, please fill in the user agreement form as well (https://forms.gle/qmK8TcEDtw56Nqev5). 

Please read the terms of usage carefully and don't hesitate to contact us if you have any questions.

Note that you need to use the same email address/mention the user id that you used in your Zenodo request when filling in the user agreement form. 

Also, please answer in detail to the question "For what purpose do you intend to use this data?" 

We may not accept your request if we find your justification insufficient upon the review.

After filling in the user agreement form, please do not forget to submit your request on Zenodo so that we can share the dataset.
DOI: 10.5281/zenodo.6851297
title: K-EmoPhone, A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

description: ABSTRACT: With the popularization of low-cost mobile and wearable sensors, prior studies have utilized such sensors to track and analyze people's mental well-being, productivity, and behavioral patterns. However, there still is a lack of open datasets collected in-the-wild contexts with affective and cognitive state labels such as emotion, stress, and attention, which would limit the advances of research in affective computing and human-computer interaction. This work presents K-EmoPhone, an in-the-wild multi-modal dataset collected from 77 university students for seven days. This dataset contains (i) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices; (ii) context and interaction data collected from individuals' smartphones; and (iii) 5,582 self-reported affect states, such as emotion, stress, attention, and disturbance, acquired by the experience sampling method. We anticipate that the presented dataset will contribute to the advancement of affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.



	
		
			
			
Last update: Apr. 12, 2023

			
-----------------------------

			
* Version 1.1.2 (Jun. 3, 2023)

			

				
Published the dataset at Scientific Data Journal.
				
Updated end-user license agreement.
			

			
* Version 1.1.1 (Apr. 12, 2023)

			

				
Updated file description and abstract.
			

			
* Version 1.1.0 (Feb. 5, 2023)

			

				
Updated the quality of the sensor data information. 
				
Deleted three participants (P27, P59, P65) due to the low quality issue.
			

			
* Version 1.0.0 (Aug. 3, 2022)

			

				
Added P##.zip files, where each P## means the separate participant.
				
Added SubjData.zip file, which includes individual characteristics information and labels.
description: 1. This dataset is currently available at Scientific Data, Nature. The primary encoding of data files is "utf-8", but "cp949" may be needed due to Korean characters.

2. When applying for the dataset, please fill in the user agreement form as well (http://tiny.cc/kemo-eula). 

Please read the terms of usage carefully and don't hesitate to contact us if you have any questions.

Note that you need to use the same email address/mention the user id that you used in your Zenodo request when filling in the user agreement form.

Also, please answer in detail to the question "For what purpose do you intend to use this data?" 

We may not accept your request if we find your justification insufficient upon the review.

After filling in the user agreement form, please do not forget to submit your request on Zenodo so that we can share the dataset.
DOI: 10.5281/zenodo.7606611
title: K-EmoPhone, A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

description: ABSTRACT: With the popularization of low-cost mobile and wearable sensors, prior studies have utilized such sensors to track and analyze people's mental well-being, productivity, and behavioral patterns. However, there still is a lack of open datasets collected in-the-wild contexts with affective and cognitive state labels such as emotion, stress, and attention, which would limit the advances of research in affective computing and human-computer interaction. This work presents K-EmoPhone, an in-the-wild multi-modal dataset collected from 77 university students for seven days. This dataset contains (i) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices; (ii) context and interaction data collected from individuals' smartphones; and (iii) 5,582 self-reported affect states, such as emotion, stress, attention, and disturbance, acquired by the experience sampling method. We anticipate that the presented dataset will contribute to the advancement of affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.



	
		
			
			
Last update: Apr. 12, 2023

			
-----------------------------

			
* Version 1.1.2 (Jun. 3, 2023)

			

				
Published the dataset at Scientific Data Journal.
				
Updated end-user license agreement.
			

			
* Version 1.1.1 (Apr. 12, 2023)

			

				
Updated file description and abstract.
			

			
* Version 1.1.0 (Feb. 5, 2023)

			

				
Updated the quality of the sensor data information. 
				
Deleted three participants (P27, P59, P65) due to the low quality issue.
			

			
* Version 1.0.0 (Aug. 3, 2022)

			

				
Added P##.zip files, where each P## means the separate participant.
				
Added SubjData.zip file, which includes individual characteristics information and labels.
description: 1. This dataset is currently available at Scientific Data, Nature. The primary encoding of data files is "utf-8", but "cp949" may be needed due to Korean characters.

2. When applying for the dataset, please fill in the user agreement form as well (http://tiny.cc/kemo-eula). 

Please read the terms of usage carefully and don't hesitate to contact us if you have any questions.

Note that you need to use the same email address/mention the user id that you used in your Zenodo request when filling in the user agreement form.

Also, please answer in detail to the question "For what purpose do you intend to use this data?" 

We may not accept your request if we find your justification insufficient upon the review.

After filling in the user agreement form, please do not forget to submit your request on Zenodo so that we can share the dataset.
DOI: 10.5281/zenodo.10254061
title: Synapsin E-domain is essential for a-synuclein function

description: The cytosolic proteins synucleins and synapsins are thought to play cooperative roles in regulating synaptic vesicle (SV) recycling, but mechanistic insight is lacking. Here we identify the synapsin E-domain as an essential functional binding-partner of a-synuclein (a-syn). Synapsin E-domain allows a-syn functionality, binds to a-syn, and is necessary and sufficient for enabling effects of a-syn at the synapse. Together with previous studies implicating the E-domain in clustering SVs, our experiments advocate a cooperative role for these two proteins in maintaining physiologic SV clusters. 
description: Tabular Data set is associated to https://doi.org/10.7554/eLife.89687.1
DOI: 10.5281/zenodo.10254062
title: Synapsin E-domain is essential for a-synuclein function

description: The cytosolic proteins synucleins and synapsins are thought to play cooperative roles in regulating synaptic vesicle (SV) recycling, but mechanistic insight is lacking. Here we identify the synapsin E-domain as an essential functional binding-partner of a-synuclein (a-syn). Synapsin E-domain allows a-syn functionality, binds to a-syn, and is necessary and sufficient for enabling effects of a-syn at the synapse. Together with previous studies implicating the E-domain in clustering SVs, our experiments advocate a cooperative role for these two proteins in maintaining physiologic SV clusters. 
description: Tabular Data set is associated to https://doi.org/10.7554/eLife.89687.1
DOI: 10.5067/cygns-l3m32
title: CYGNSS Level 3 MRG Science Data Record Version 3.2
DOI: 10.6084/m9.figshare.25242628
title: New intelligent particle swarm optimization algorithm with extreme learning machine for forecasting Pattavia pineapple productivity: case study of Loei and Nong Khai provinces in Thailand

description: This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively.
DOI: 10.6084/m9.figshare.25242628.v1
title: New intelligent particle swarm optimization algorithm with extreme learning machine for forecasting Pattavia pineapple productivity: case study of Loei and Nong Khai provinces in Thailand

description: This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively.