cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7352057
title: Microelectrode register (MER) data from Deep Brain Stimulation (DBS) surgery in Parkinson's disease patients

description: MER data consist of brain signal in different depths when DBS surgery is being done. In each depth, a data file is created, with different duration depending on the depth, and up to three channels. Data come from 14 patients (9 males and 5 females), they are anonymized and labelled from P1 to P14. They correspond to patients in age 65.1 +- 5.6 years. Data are organized in STN-IN and STN-OUT (different depths in each folder), subthalamus-in, and subthalamus-out since the STN area is the target area when implanting a DBS. Classification in STN-IN and STN-OUT was made by the neurophysiologists and surgeons. Data were recorded for left and right lobes, 8 patients in left and right lobe, 1 patient in right lobe, and 5 patients in left lobe. The format is mat file (MATLAB file) Data sampling frequency is 12kHz. No filtering or data processing was made, they are directly obtained from the MER acquisition system.
DOI: 10.48511/vhxj-7p97
title: Topping Creek Stage-Flow

description: Data includes water level as determined from pressure transducer in logger sensor and temperature; Flow will be measured periodically to determine Rating Curve.
DOI: 10.6084/m9.figshare.5813877.v5
title: Carbocupration of heterosubstituted alkynes

description: Inputs and outputs
DOI: 10.6084/m9.figshare.21611136
title: Additional file 5 of Behavioral differences among domestic cats in the response to cat-attracting plants and their volatile compounds reveal a potential distinct mechanism of action for actinidine

description: Additional file 5. This file contains the raw data from the plant, single compound, and habituation / dishabituation experiments, as well as the BORIS analyses (plants and single compounds).
DOI: 10.6084/m9.figshare.21611136.v1
title: Additional file 5 of Behavioral differences among domestic cats in the response to cat-attracting plants and their volatile compounds reveal a potential distinct mechanism of action for actinidine

description: Additional file 5. This file contains the raw data from the plant, single compound, and habituation / dishabituation experiments, as well as the BORIS analyses (plants and single compounds).
DOI: 10.6084/m9.figshare.21611130
title: Additional file 4 of Behavioral differences among domestic cats in the response to cat-attracting plants and their volatile compounds reveal a potential distinct mechanism of action for actinidine

description: Additional file 4. DNA barcoding sequence of cox1 from Pseudasphondylia matatabi, and sequences of matK, rbcL, and psbA – trnH from Actinidia and Lonicera species.
DOI: 10.6084/m9.figshare.21611130.v2
title: Additional file 4 of Behavioral differences among domestic cats in the response to cat-attracting plants and their volatile compounds reveal a potential distinct mechanism of action for actinidine

description: Additional file 4. DNA barcoding sequence of cox1 from Pseudasphondylia matatabi, and sequences of matK, rbcL, and psbA – trnH from Actinidia and Lonicera species.
DOI: 10.6084/m9.figshare.21611130.v1
title: Additional file 4 of Behavioral differences among domestic cats in the response to cat-attracting plants and their volatile compounds reveal a potential distinct mechanism of action for actinidine

description: Additional file 4. DNA barcoding sequence of cox1 from Pseudasphondylia matatabi, and sequences of matK, rbcL, and psbA – trnH from Actinidia and Lonicera species.
DOI: 10.57777/kgm2022000042
title: 지붕009(시멘트기와-청색)
DOI: 10.48511/sp7d-y482
title: Columbia Lake (N1)

description: Water quality measurements on Columbia Lake, collected as part of CLSS water monitoring program. Dataset collected to better understand health of Columbia lake and to communicate information to decision makers for strategic planning for management strategies, as well as to support our contributing partners for their future projects and initiatives.
DOI: 10.5281/zenodo.5794336
title: A map of global peatland extent created using machine learning (Peat-ML)

description: Map of global peatland extent estimated by machine learning. Abstract from associated paper: Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and<br> hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed<br> by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here,<br> we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning<br> techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of<br> peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed<br> vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with<br> mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualititative comparisons to other maps<br> in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out<br> cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r<sup>2</sup><br> of 0.73 with a root mean squared error and mean bias error of 9.11% and -0.36%, respectively. Our second error estimate<br> was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited<br> Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping<br> products generated through more traditional approaches, at least for boreal peatlands.
DOI: 10.5281/zenodo.7352284
title: A map of global peatland extent created using machine learning (Peat-ML)

description: Map of global peatland extent estimated by machine learning. The download includes both a netcdf file version and a GeoTIFF (as a zip archive) Abstract from associated paper: Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and<br> hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed<br> by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here,<br> we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning<br> techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of<br> peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed<br> vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with<br> mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualititative comparisons to other maps<br> in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out<br> cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r<sup>2</sup><br> of 0.73 with a root mean squared error and mean bias error of 9.11% and -0.36%, respectively. Our second error estimate<br> was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited<br> Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping<br> products generated through more traditional approaches, at least for boreal peatlands.
DOI: 10.5281/zenodo.5794335
title: A map of global peatland extent created using machine learning (Peat-ML)

description: Map of global peatland extent estimated by machine learning. The download includes both a netcdf file version and a GeoTIFF (as a zip archive) Abstract from associated paper: Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and<br> hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed<br> by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here,<br> we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning<br> techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of<br> peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed<br> vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with<br> mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualititative comparisons to other maps<br> in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out<br> cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r<sup>2</sup><br> of 0.73 with a root mean squared error and mean bias error of 9.11% and -0.36%, respectively. Our second error estimate<br> was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited<br> Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping<br> products generated through more traditional approaches, at least for boreal peatlands.
DOI: 10.57777/kgm2022000043
title: 변성암(선캠브리아기 호상흑운모편마암(PCEbngn))
DOI: 10.48511/v4x5-ns17
title: Columbia Lake (S1)

description: Water quality data from the middle of the lake, near Spirits Reach. Dataset collected to better understand health of Columbia lake and to communicate information to decision makers for strategic planning for management strategies, as well as to support our contributing partners for their future projects and initiatives.
DOI: 10.21227/tvr4-7z39
title: An investigation of contemporary data-driven methods applied to complex systems

description: This dataset details the data used as a part of a review and mapping paper of data-driven methods. Understanding complex systems by the help of modeling, simulation, and control is a well-known challenge across several application domains. As such, these type of systems are not amenable to empirical models, typically due to their dynamic and non-linear nature. Data-driven methods, have in recent years gained traction regarding their application to such complex systems. This paper presents findings from a systematic literature review of data-driven methods applied to complex systems in recent years. The review is a result of a search strategy resulting in 1106 scientific publications, out of which forty one were identified as primary studies. It is our goal that the review findings will guide researchers with an evidence-base regarding data-driven methods, thereby equipping them with an arsenal of applied methods across various application domains. Also, the objective is to construct a knowledge-base based on these methods' contributions while applied to different types of problems. Additionally, the review findings may also guide researchers to realize potential gaps for future research.
DOI: 10.48511/phzg-h575
title: Columbia Lake (S3)

description: Data collected on south end of Columbia Lake in line with the outlet of Marion Creek as part of the CLSS water quality monitoring program. Dataset collected to better understand health of Columbia lake and to communicate information to decision makers for strategic planning for management strategies, as well as to support our contributing partners for their future projects and initiatives.
DOI: 10.48511/m44b-h052
title: Columbia Lake (S4)

description: Water quality measurements on Columbia Lake north of Tilley Memorial Park, collected as part of CLSS water monitoring program. Dataset collected to better understand health of Columbia lake and to communicate information to decision makers for strategic planning for management strategies, as well as to support our contributing partners for their future projects and initiatives.
DOI: 10.6084/m9.figshare.21611289
title: CPUASP

description: Species specific seining data for different fish species standardized over sampled area, over time and treatment.
DOI: 10.6084/m9.figshare.21611289.v1
title: CPUASP

description: Species specific seining data for different fish species standardized over sampled area, over time and treatment.
DOI: 10.5281/zenodo.7352171
title: Database on holdover time of lightning-ignited wildfires

description: This database contains open, harmonized, and ready-to-use global data on holdover time. Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The first version of the database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). These data were collected through a literature review of LIW studies and some datasets were assembled by authors of the original studies, covering more than 150,000 LIW from 13 countries in five continents and a time span of a century from 1921 to 2020. Censored data are the core of the database and consist of frequency data reporting the number or relative frequency of LIW per interval of holdover time. Ancillary data provide additional information on the methods and contexts in which the data were generated in the original studies. Potential contributors to the database are encouraged to contact the corresponding author in the readme file.
DOI: 10.57777/kgm2022000044
title: 도로012(시멘트-블록)
DOI: 10.5517/ccdc.csd.cc26l5mm
title: CCDC 2043260: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Robert Gericke|2020|CSD Communication|||
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.cc26l5pp
title: CCDC 2043262: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5qq
title: CCDC 2043263: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5rr
title: CCDC 2043264: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5ss
title: CCDC 2043265: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5tt
title: CCDC 2043266: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5vv
title: CCDC 2043267: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5ww
title: CCDC 2043268: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5xx
title: CCDC 2043269: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26l5yy
title: CCDC 2043270: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Sven Wahlicht|2020|CSD Communication|||
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.cc26lms7
title: CCDC 2043699: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Edwin Kroke, Janine Kowalke|2020|CSD Communication|||
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: 10.57777/kgm2022000045
title: 변성암(선캠브리아기 우백질화강편마암(PCEgne))
DOI: 10.5517/ccdc.csd.cc2bbqr5
title: CCDC 2155267: Experimental Crystal Structure Determination

description: Related Article: Min Li, Yihua Chen, Yingkun Yan, Min Liu, Min Huang, Wenzhe Li, Lianyi Cao, Xiaomei Zhang|2022|Org.Biomol.Chem.|20|8849|doi:10.1039/D2OB01746F
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.cc2bbqw9
title: CCDC 2155271: Experimental Crystal Structure Determination

description: Related Article: Min Li, Yihua Chen, Yingkun Yan, Min Liu, Min Huang, Wenzhe Li, Lianyi Cao, Xiaomei Zhang|2022|Org.Biomol.Chem.|20|8849|doi:10.1039/D2OB01746F
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.cc2bf813
title: CCDC 2157694: Experimental Crystal Structure Determination

description: Related Article: Yi Liu, Haojie Xu, Xitao Liu, Shiguo Han, Wuqian Guo, Yu Ma, Qingshun Fan, Xinxin Hu, Zhihua Sun, Junhua Luo|2022|Chemical Science|13|13499|doi:10.1039/D2SC05285G
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: 10.57777/kgm2022000046
title: 화성암(백악기 밀양안산암(Kma))
DOI: 10.5281/zenodo.7042562
title: Code Style Transfer Non-Tokenized Data

description: Data for code style probing
DOI: 10.5281/zenodo.7115637
title: Code Style Transfer Non-Tokenized Data

description: Data for code style probing
DOI: 10.5281/zenodo.7118277
title: Code Style Transfer Non-Tokenized Data

description: Data for code style probing
DOI: 10.5281/zenodo.7127665
title: Code Style Transfer Non-Tokenized Data

description: Data for code style probing
DOI: 10.5281/zenodo.7042561
title: Anonymous Submission

description: Anonymous Submission
DOI: 10.5281/zenodo.7133639
title: Code Style Transfer Non-Tokenized Data

description: Data for code style probing
DOI: 10.5281/zenodo.7352292
title: Anonymous Submission

description: Anonymous Submission
DOI: 10.57777/kgm2022000047
title: 수박002
DOI: 10.57777/kgm2022000048
title: 담배004
DOI: 10.5517/ccdc.csd.cc2bbnn0
title: CCDC 2155202: Experimental Crystal Structure Determination

description: Related Article: Francisco J. Ballester, Enrique Ortega-Forte, Delia Bautista, M. Dolores Santana, Giampaolo Barone, José Ruiz|2023|Dyes Pigm.|209|110905|doi:10.1016/j.dyepig.2022.110905
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.