cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2by06j
title: CCDC 2172827: Experimental Crystal Structure Determination

description: Related Article: Austen Moss, Youngwoo Jang, Jacob Arvidson, Vladimir N. Nesterov, Francis D'Souza, Hong Wang|2022|Chemical Science|13|9880|doi:10.1039/D2SC03238D
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.cc2by07k
title: CCDC 2172828: Experimental Crystal Structure Determination

description: Related Article: Austen Moss, Youngwoo Jang, Jacob Arvidson, Vladimir N. Nesterov, Francis D'Souza, Hong Wang|2022|Chemical Science|13|9880|doi:10.1039/D2SC03238D
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.cc2by08l
title: CCDC 2172829: Experimental Crystal Structure Determination

description: Related Article: Austen Moss, Youngwoo Jang, Jacob Arvidson, Vladimir N. Nesterov, Francis D'Souza, Hong Wang|2022|Chemical Science|13|9880|doi:10.1039/D2SC03238D
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.cc1mlx48
title: CCDC 1507720: Experimental Crystal Structure Determination

description: Related Article: Xiao-Lei Li, Lang Zhao, Jianfeng Wu, Wei Shi, Niklas Struch, Arne Lützen, Annie K. Powell, Peng Cheng, Jinkui Tang|2022|Chemical Science|13|10048|doi:10.1039/D2SC03156F
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.cc1nkp41
title: CCDC 1536333: Experimental Crystal Structure Determination

description: Related Article: Xiao-Lei Li, Lang Zhao, Jianfeng Wu, Wei Shi, Niklas Struch, Arne Lützen, Annie K. Powell, Peng Cheng, Jinkui Tang|2022|Chemical Science|13|10048|doi:10.1039/D2SC03156F
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: 10.6084/m9.figshare.20753596
title: Allen brain mask histology weights

description: Allen brain mask histology weights <br>
DOI: 10.6084/m9.figshare.20753596.v1
title: Allen brain mask histology weights

description: Allen brain mask histology weights <br>
DOI: 10.5281/zenodo.7008262
title: ReachHover User Study Dataset

description: Dataset for the user study described in the ReachHover paper.
DOI: 10.5281/zenodo.7008260
title: ReachHover User Study Dataset

description: Dataset for the user study described in the ReachHover paper.
DOI: 10.5519/qd.zqi8bi4q
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25574/54254
title: Chandra X-ray Observatory ObsId 54254
DOI: 10.25574/54260
title: Chandra X-ray Observatory ObsId 54260
DOI: 10.25574/54267
title: Chandra X-ray Observatory ObsId 54267
DOI: 10.25574/54274
title: Chandra X-ray Observatory ObsId 54274
DOI: 10.25574/54276
title: Chandra X-ray Observatory ObsId 54276
DOI: 10.25574/54281
title: Chandra X-ray Observatory ObsId 54281
DOI: 10.25574/54288
title: Chandra X-ray Observatory ObsId 54288
DOI: 10.25574/54294
title: Chandra X-ray Observatory ObsId 54294
DOI: 10.25574/54301
title: Chandra X-ray Observatory ObsId 54301
DOI: 10.25574/54308
title: Chandra X-ray Observatory ObsId 54308
DOI: 10.25574/54316
title: Chandra X-ray Observatory ObsId 54316
DOI: 10.25574/54323
title: Chandra X-ray Observatory ObsId 54323
DOI: 10.25574/54329
title: Chandra X-ray Observatory ObsId 54329
DOI: 10.25574/54335
title: Chandra X-ray Observatory ObsId 54335
DOI: 10.25574/54342
title: Chandra X-ray Observatory ObsId 54342
DOI: 10.25574/54350
title: Chandra X-ray Observatory ObsId 54350
DOI: 10.25574/54366
title: Chandra X-ray Observatory ObsId 54366
DOI: 10.25574/54372
title: Chandra X-ray Observatory ObsId 54372
DOI: 10.25574/54380
title: Chandra X-ray Observatory ObsId 54380
DOI: 10.25574/54385
title: Chandra X-ray Observatory ObsId 54385
DOI: 10.25574/54393
title: Chandra X-ray Observatory ObsId 54393
DOI: 10.25574/54399
title: Chandra X-ray Observatory ObsId 54399
DOI: 10.25574/54405
title: Chandra X-ray Observatory ObsId 54405
DOI: 10.25574/54411
title: Chandra X-ray Observatory ObsId 54411
DOI: 10.25574/54418
title: Chandra X-ray Observatory ObsId 54418
DOI: 10.25574/54424
title: Chandra X-ray Observatory ObsId 54424
DOI: 10.25574/54431
title: Chandra X-ray Observatory ObsId 54431
DOI: 10.25574/54436
title: Chandra X-ray Observatory ObsId 54436
DOI: 10.25574/54441
title: Chandra X-ray Observatory ObsId 54441
DOI: 10.25574/54450
title: Chandra X-ray Observatory ObsId 54450
DOI: 10.25574/54457
title: Chandra X-ray Observatory ObsId 54457
DOI: 10.25574/54463
title: Chandra X-ray Observatory ObsId 54463
DOI: 10.25574/54465
title: Chandra X-ray Observatory ObsId 54465
DOI: 10.25574/54472
title: Chandra X-ray Observatory ObsId 54472
DOI: 10.25574/54482
title: Chandra X-ray Observatory ObsId 54482
DOI: 10.25574/54492
title: Chandra X-ray Observatory ObsId 54492
DOI: 10.25574/54496
title: Chandra X-ray Observatory ObsId 54496
DOI: 10.5066/p9uztypv
title: Proximal and distal factors associated with the decline in secondary invertebrate prey production in the Colorado River, Glen Canyon, Arizona.

description: Using a bioenergetic model, demographic data for the Rainbow Trout (Oncorhynchus mykiss) population were compiled and used to estimate total prey consumption in the Colorado River, Glen Canyon , AZ. Additionally, other data including invertebrate diet, drift, and benthic measurements were used to make generalized estimates of daily production rates for the most common benthic invertebrate taxa. The primary objectives were to test a set of hypotheses regarding proximate and distal drivers that were regualating secondary production of invertebrate prey in Glen Canyon. These production estimates represent an estimate of aggregate prey items that include Chironomidae and Simulium arcticum [complex]), as well as amphipods (Gammarus lacustris); [...]
DOI: 10.17632/kpxdzkgs3j.1
title: A Golden Set of Problem, Solution, Advantages Senteces of the Patents

description: This data contains two different dataset:

(1) Golden Set is dataset of sentences tagged as (A) technical problem; (B) solution to the problem; and (C) advantageous effect of the invention. The dataset is based on a selectively extracted collection from the United States Patent and Trademark Office (USPTO) curated by Chikkamath, R., Parmar, V. R., Hewel, C., & Endres, M. (2021). Patent Sentiment Analysis to Highlight Patent Paragraphs. arXiv preprint arXiv:2111.09741. The full text of a patent is composed of three main parts: abstract, claims and description. The five IP offices (IP5) formalize a common application format to standardize the written style of the patent description. The common application format also includes the sections related with the concepts of our interest, i.e., technical problems, solutions to the problem, and advantageous effects of the invention. USPTO provides to the public the full text of the patent for advancing the state-of-the-art in innovation. The full text of a patent is saved in a nested eXtensible Markup Language (XML) formatted file. The XML format enables to distinguish the patent text in the abstract, claims and description part. It allows us to distinguish from the different sections composing the description established by the IP5 common application format, i.e., the background information, the summary, the embodiment, the description of the drawings, the technical fields, and other sections. Chikkamath et al. (2021) use the USPTO data and in particular the XML files of the patent full text for creating a new dataset. The dataset contains a collection of patent texts (150,000 samples) referred to (A) technical problems; (B) solutions to the problem; and (C) advantageous effects of the invention. We use this data for building our golden set.

(2) Test data is a database 400 random patent grants and patent applications downoladed from USPTO. We use this data for evaluating a transformer-based language models developed for extracting problems, solutions and advantages on a real case use in an open ended domain.
DOI: 10.17632/kpxdzkgs3j.2
title: A Golden Set of Problem, Solution, Advantages Senteces of the Patents

description: This data contains two different dataset:

(1) Golden Set is dataset of sentences tagged as (A) technical problem; (B) solution to the problem; and (C) advantageous effect of the invention. The dataset is based on a selectively extracted collection from the United States Patent and Trademark Office (USPTO) curated by Chikkamath, R., Parmar, V. R., Hewel, C., & Endres, M. (2021). The dataset is available upon request.

(2) Test data is a database 400 random patent grants and patent applications downoladed from USPTO. We use this data for evaluating a transformer-based language models developed for extracting problems, solutions and advantages on a real case use in an open ended domain.
DOI: 10.17632/2sstw869xj.1
title: National governance indicators and speed of response to COVID-19 in LMICs

description: Dataset for the paper: Exploring the relationship between national governance indicators and speed of initial government response to COVID-19 in low- and middle-income countries.
DOI: 10.17632/xpnrvp9k6w.1
title: IoT Smart Door Lock with Wireless Key Sharing for Tourism Applications

description: IoT Smart Door Lock with Wireless Key Sharing for Tourism Applications Videos
DOI: 10.17632/xpnrvp9k6w.2
title: IoT Smart Door Lock with Wireless Key Sharing for Tourism Applications

description: IoT Smart Door Lock with Wireless Key Sharing for Tourism Applications Videos
DOI: 10.17632/y9ctcn7p89.1
title: Vietnamese translation of same-sex marriage scale

description: This data presents the Attitudes of vietnamese students toward same-sex marriage. The data obtained from 280 students from four universities in Ho Chi Minh City, Vietnam. The participants filled out the questionnaire individually
DOI: 10.17632/dz7r84dcbp.1
title: Dataset of Covid-19 cases in Jakarta, Indonesia

description: This is covid-19 daily cases data with additional information such as climatology, public transport users, congestion, air quality, and movement restriction (MR) from 10 March 2020 until 25 November 2020 in Jakarta, Indonesia. The data is collected from various sources. The data acquisition and preparation procedures are explained in an article named "A Covid-19 Positive Case Prediction and People Movement Restriction Classification".
DOI: 10.17632/227d24hxnr.1
title: HP-Table S1

description: The isotope data in two catchments on the CLP
DOI: 10.17632/wr29vt6z7j.2
title: Enhanced degradation of fomesafen by a rhizobial strain Sinorhizobium sp. W16 in symbiotic association with soybean: Datasets and Supplementary Materials

description: Associated publication: Chen, W., Gao, Y., Shi, G., Li, J., Fan, G., Yang, C., Wang, B., Tong, F., Li, Y. (2022) "Enhanced degradation of fomesafen by a rhizobial strain Sinorhizobium sp. W16 in symbiotic association with soybean."

Fomesafen, a broad-spectrum herbicide with a long duration, is frequently applied in legume farmlands, causing severe damage to crop growth and the local environment. Rhizobia can develop key enzyme systems to degrade organic pollutants and form a symbiotic relationship with legumes to enhance the degradation of fomesafen. However, there are few relevant studies examining this phenomenon. In this study, a fomesafen-degrading rhizobium, Sinorhizobium sp. W16, was isolated from a soybean nodule for the first time. The metabolic study of fomesafen in its pure culture revealed a novel biodegradation pathway, which was initiated by cleavage of the S‒N bond, followed by dechlorination and cleavage of the ether bond. Strain W16 efficiently degraded fomesafen by 69% and 38% under fomesafen concentrations of 5 mg L-1 and 50 mg L-1 over 7 days, respectively. The pot experiment results demonstrated that strain W16 colonizes the soybean rhizosphere successfully and eliminates the inhibition of fomesafen on nitrogen fixation by stimulating the indigenous nitrogen-fixing microbes in the rhizosphere. These developed symbiotic relationships promote fomesafen degradation rates by 29.17%-57.87%. This study provides valuable information about a novel fomesafen-degrading rhizobium with great potential for promoting the bioremediation of herbicide-contaminated farmland soil due to the development of symbiotic systems with legumes.

Contact information: Wei Chen, Jiangsu Academy of Agricultural Sciences, Nanjing, China, wchen@jaas.ac.cn

Dates of data collection: 2019/06 - 2021/07
DOI: 10.17632/t4gjkfnxnh.1
title: Wavelet-based fuzzy clustering of interval time series - Supplementary Material

description: Supplementary material for the manuscript "Wavelet-based fuzzy clustering of interval time series". This includes additional figures and tables referred to in the manuscript as well as details of scripts and data files used for the simulation studies and the application. All scripts are in .R format and data files are is in EXCEL (. xlsx) format.
DOI: 10.17632/t4gjkfnxnh.2
title: Wavelet-based fuzzy clustering of interval time series -  Supplementary Material

description: Supplementary material for the manuscript  "Wavelet-based fuzzy clustering of interval time series".  This includes additional figures and tables referred to in the manuscript as well as details of scripts and data files used for the simulation studies and the application. All scripts are in .R format and data files are is in EXCEL (. xlsx) format.
DOI: 10.17632/t4gjkfnxnh.3
title: Wavelet-based fuzzy clustering of interval time series -  Supplementary Material

description: Supplementary material for the manuscript  "Wavelet-based fuzzy clustering of interval time series".  This includes additional figures and tables referred to in the manuscript as well as details of scripts and data files used for the simulation studies and the application. All scripts are in .R format and data files are is in EXCEL (. xlsx) format.
DOI: 10.7284/146517
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise KM2113 using a Hawaii UHDAS instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146529
title: Anemometer data collected during research cruise KM2113 using a RMYoung 85106 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146530
title: Anemometer data collected during research cruise KM2113 using a RMYoung 85106 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146522
title: Barometer data collected during research cruise KM2113 using a Vaisala PTB220 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146518
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise KM2113 using a SeaBird SBE-911+ instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146521
title: Fluorometer data collected during research cruise KM2113 using a WETLabs WETStar instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146519
title: Gravimeter data collected during research cruise KM2113 using a Bell BGM-3 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146520
title: Gyrocompass data collected during research cruise KM2113 using a Sperry NAVIGAT X MK 1 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/fpf2jv378d.1
title: Image data set for AI-assisted reliability assessment for gravure offset printing system

description: In total, 299 images of printed lines were collected using the in-house roll-based gravure offset printing system.  Then they were labeled for overall printing quality classification and local printing defect detection tasks. For overall printing quality classification, these images were divided into two classes: 225 with satisfactory quality and 74 with defects. For local printing defect detection, all 74 images with distinctive local defects were chosen and labeled following the YOLO object detection format. During the training process, these images were split for training and validation as follows: 75/25 % for the overall printing quality classification model and 80/20 % for the local printing defect detection model respectively.
DOI: 10.7284/146531
title: INS (Inertial Navigation System) data collected during research cruise KM2113 using a Applanix POS/MV-320 V4 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/fpf2jv378d.2
title: Image data set for AI-assisted reliability assessment for gravure offset printing system

description: In total, 299 images of printed lines were collected using the in-house roll-based gravure offset printing system.  Then they were labeled for overall printing quality classification and local printing defect detection tasks. For overall printing quality classification, these images were divided into two classes: 225 with satisfactory quality and 74 with defects. For local printing defect detection, all 74 images with distinctive local defects were chosen and labeled following the YOLO object detection format. During the training process, these images were split for training and validation as follows: 75/25 % for the overall printing quality classification model and 80/20 % for the local printing defect detection model respectively.
DOI: 10.7284/146516
title: Meteorological Station data collected during research cruise KM2113 using a Hawaii MET-System instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/sjrxbzwm7m.1
title: Image data set for AI-aided printed line smearing analysis of the roll-to-roll screen printing process for printed electronics

description: A total of 20 images were collected with cross-directional (CD) printed lines of various line widths and smearing areas using the in-house roll-to-roll screen printing system. Then these images were labeled pixel-wise into three classes: smearing, printed line, and background, and labels were saved as one channel 8-bit images with corresponding intensity 1, 2, and 3. This data set was used to train a U-Net-like deep convolutional neural network (DCNN) to detect continuous printed line smearing defects.
DOI: 10.17632/sjrxbzwm7m.2
title: Image data set for AI-aided printed line smearing analysis of the roll-to-roll screen printing process for printed electronics

description: A total of 20 images were collected with cross-directional (CD) printed lines of various line widths and smearing areas using the in-house roll-to-roll screen printing system. Then these images were labeled pixel-wise into three classes: smearing, printed line, and background, and labels were saved as one channel 8-bit images with corresponding intensity 1, 2, and 3. This data set was used to train a U-Net-like deep convolutional neural network (DCNN) to detect continuous printed line smearing defects.
DOI: 10.17632/sjrxbzwm7m.3
title: Image data set for AI-aided printed line smearing analysis of the roll-to-roll screen printing process for printed electronics

description: A total of 20 images were collected with cross-directional (CD) printed lines of various line widths and smearing areas using the in-house roll-to-roll screen printing system. Then these images were labeled pixel-wise into three classes: smearing, printed line, and background, and labels were saved as one channel 8-bit images with corresponding intensity 1, 2, and 3. This data set was used to train a U-Net-like deep convolutional neural network (DCNN) to detect continuous printed line smearing defects.
DOI: 10.7284/146523
title: Meteorological Station data collected during research cruise KM2113 using a Hawaii MET-System instrument system onboard the platform RV Kilo Moana

description: Processed data created by vessel technician during a Field_expedition
DOI: 10.7284/146525
title: Multibeam Echosounder data collected during research cruise KM2113 using a Kongsberg EM122 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146527
title: Multibeam Echosounder data collected during research cruise KM2113 using a Kongsberg EM710 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/146528
title: pCO2 (Partial Pressure of Carbon Dioxide) data collected during research cruise KM2113 using a GeneralOceanics 8050 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/858wmkxykz.1
title: First year report_Honglin Xu

description: These videos are contraction function analysis of cardiac organoids. Cardiac organoids of 2500 μm diameter exhibiting spontaneous contraction(Myvideo_0). Transient calcium flux of cardiac organoids using 4 X (Myvideo_1) and 20 X (Myvideo_2) microscope.
DOI: 10.7284/146513
title: SSV (Sound Speed Velocimeter) data collected during research cruise KM2113 using a AML SV&P instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/zfjzsmn262.1
title: Supplementary Materials: Skin cancer prevalence and risk among foreign-born people living in the US

description: Supplementary Materials: Demographic Characteristics (Table 1) and Skin Cancer Prevalence and Risk Factors with Differing Markers of Acculturation (Table 2)
DOI: 10.7284/146514
title: TSG (Thermosalinograph) data collected during research cruise KM2113 using a SeaBird SBE-45 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/137640
title: Anemometer data collected during research cruise SKQ201610S using a RMYoung 85004 instrument system onboard the platform RV Sikuliaq

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/137641
title: Anemometer data collected during research cruise SKQ201610S using a RMYoung 85004 instrument system onboard the platform RV Sikuliaq

description: Processed data created by vessel technician during a Field_expedition