cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc24lbdh
title: CCDC 1983826: Experimental Crystal Structure Determination

description: Related Article: Vladimir Е. Zhulanov, Valeria A. Vigovskaya, Maksim V. Dmitriev, Pavel S. Silaichev, Andrey N. Maslivets, Michael Rubin|2020|CSD Communication|||
description: Related Article: Vladimir E. Zhulanov, Valeria A. Vigovskaya, Maksim V. Dmitriev, Pavel S. Silaichev, Andrey N. Maslivets, Michael Rubin|2020|Org.Biomol.Chem.|18|3382|doi:10.1039/D0OB00451K
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.cc26fm5g
title: CCDC 2038875: Experimental Crystal Structure Determination

description: Related Article: Weikang Li|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.cc26n95c
title: CCDC 2045292: Experimental Crystal Structure Determination

description: Related Article: Xiao Jin|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.cc26p1qp
title: CCDC 2046022: Experimental Crystal Structure Determination

description: Related Article: Zeng Gao, Jinlong Qian, Huameng Yang, Jinlong Zhang, Gaoxi Jiang|2021|Org.Lett.|23|1731|doi:10.1021/acs.orglett.1c00156
description: Related Article: Zeng Gao|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.cc24qqsd
title: CCDC 1988054: Experimental Crystal Structure Determination

description: Related Article: Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Masami Sakamoto|2020|CSD Communication|||
description: Related Article: Naohiro Uemura,  Yasushi Yoshida,  Takashi Mino,  Masami Sakamoto|2020|Tetrahedron|76|131166|doi:10.1016/j.tet.2020.131166
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.cc24qqvg
title: CCDC 1988056: Experimental Crystal Structure Determination

description: Related Article: Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Masami Sakamoto|2020|CSD Communication|||
description: Related Article: Naohiro Uemura,  Yasushi Yoshida,  Takashi Mino,  Masami Sakamoto|2020|Tetrahedron|76|131166|doi:10.1016/j.tet.2020.131166
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.cc24qqxj
title: CCDC 1988058: Experimental Crystal Structure Determination

description: Related Article: Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Masami Sakamoto|2020|CSD Communication|||
description: Related Article: Naohiro Uemura,  Yasushi Yoshida,  Takashi Mino,  Masami Sakamoto|2020|Tetrahedron|76|131166|doi:10.1016/j.tet.2020.131166
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.cc25hmz9
title: CCDC 2011031: Experimental Crystal Structure Determination

description: Related Article: Abdelmaoujoud Taia, Mohamed Essaber, Abdeljalil Aatif, Tuncer Hökelek, Joel T. Mague, Nada Kheira Sebbar|2020|CSD Communication|||
description: Related Article: Abdelmaoujoud Taia, Brahim El Ibrahimi, Fouad Benhiba, Muhammad Ashfaq, Muhammad Nawaz Tahir, Mohamed Essaber, Abdeljalil Aatif, Tuncer Hökelek, Joel T. Mague, Nada Kheira Sebbar, El Mokhtar Essassi|2021|J.Mol.Struct.|1234|130189|doi:10.1016/j.molstruc.2021.130189
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.21618708
title: Experiment information

description: The ExpInfo.mat contains the following information: <br> subjects: the random ID for all participants, which is also the file name for the MEG data PTBFiles: file names for the Psychtoolbox data EyeFiles: file names for the eye movement data BadSensor: bad MEG sensors for each participant, data from the bad sensors were excluded/removed from further analysis, resulting in different amounts of MEG sensors in the epoch data across participants Trigger: trigger numbers and their represented events CondID: the numbers that used to index two conditions CondName: condition names EventHdr: header information about the columns in the epoch data (trailinfo) SentenceVersion: which version of sentence stimuli was used TrackRightEye: participants ID who were tracked the right eye (most participants were tracked the left eye) Gender: Age: MRIcode: nan indicated no T1 images from this participant, thus the MNI template was used instead in the source localization analysis TagSigSubID: ID of participants who has more than one tagging response sensors in the procedure of tagging-sensor-selection <br>
DOI: 10.34740/kaggle/dsv/4579285
title: Mental Disorders Identification (Reddit)
DOI: 10.5281/zenodo.7358284
title: Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.

description: Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <br> References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571
DOI: 10.5281/zenodo.7358283
title: Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.

description: Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <br> References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571
DOI: 10.6084/m9.figshare.21618714
title: Untitled Item

description: Large herbivores counts conducted in Mudumu National Park in the year 2019. Data resolution is at transect level. 22 transects/routes werewere sampled. Data contains 17 large herbivore species.
DOI: 10.6084/m9.figshare.21618714.v1
title: Untitled Item

description: Large herbivores counts conducted in Mudumu National Park in the year 2019. Data resolution is at transect level. 22 transects/routes werewere sampled. Data contains 17 large herbivore species.
DOI: 10.6084/m9.figshare.21206990.v2
title: epoch data after pre-processing

description: The epoch data are a fieldtrip format structure, all epochs (ended with 'WrdOn') are aligned with fixation onset to a given word (as time 0), with a length of one second ([-0.5 0.5]s). Epochs were named as the combination of the acquisition date, subject code, and data type. Epochs that ended with 'BL_Cross' were the baseline period before the presentation of the sentences. This filed 'trialinfo' is the information about each trial, the header of all columns is as followings: 1- sentence_id: sentence number for this epoch 2- word_loc: the location of the current word in a sentence 3- loc2targ:location distance between the current word and target word; loc2targ for pre-target, target, post-target are -1, 0, and 1 4- saccade2this_duration: saccade duration toward this word 5- fixation_on_MEG: MEG trigger for fixation onset to this word 6- fixation_duration 7- NextOrder: next word location minus the current word location; negative value indicates saccade backward to the previous words 8- FirstPassFix: whether this fixation is the first for this word or not 9- PreviousOrder: previous word location minus the current word location; negative value indicates saccade forward to the next words 10- SentenceCondition: the current word is in a sentence with incongruent or congruent target word; 11 -- incongruent, 2 -- congruent 11- PupilSize: averaged pupil size during this fixation
DOI: 10.5281/zenodo.6555216
title: Global Plantation Forest Carbon database

description: This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. <em>Nature Communications</em> 13(4206). doi: 10.1038/s41467-022-31380-7
description: The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).
DOI: 10.5281/zenodo.6555215
title: Global Plantation Forest Carbon database

description: This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. <em>Nature Communications</em> 13(4206). doi: 10.1038/s41467-022-31380-7
description: The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).
DOI: 10.5281/zenodo.7358591
title: Post-remediation evaluation of contaminated site using geophysical methods: ERT

description: The ERT measurements (7 profiles: M1-M7) were performed using the LUND electrical imaging system with a SAS 4000 Terrameter produced by ABEM Malå (Guideline Geo) with 0.5 m electrode separation and the Wenner-Schlumberger configuration. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358590
title: Post-remediation evaluation of contaminated site using geophysical methods: ERT

description: The ERT measurements (7 profiles: M1-M7) were performed using the LUND electrical imaging system with a SAS 4000 Terrameter produced by ABEM Malå (Guideline Geo) with 0.5 m electrode separation and the Wenner-Schlumberger configuration. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358703
title: Post-remediation evaluation of contaminated site using geophysical methods: IP

description: Five IP profiles were performed using the LUND electrical imaging system with a SAS 4000 Terrameter produced by ABEM Malå (Guideline Geo) with 0.5 m electrode separation and the Wenner-Schlumberger configuration. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358702
title: Post-remediation evaluation of contaminated site using geophysical methods: IP

description: Five IP profiles were performed using the LUND electrical imaging system with a SAS 4000 Terrameter produced by ABEM Malå (Guideline Geo) with 0.5 m electrode separation and the Wenner-Schlumberger configuration. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358715
title: Post-remediation evaluation of contaminated site using geophysical methods: EMI

description: For the EMI measurements, a ground conductivity meter EM38-MK2 (Geonics) was used. Based on the measurements carried out on two coil spacings every 0.5 m and 1 m in horizontal (H-mode) and vertical dipole mode (V-mode), a component reflecting the electrical conductivity was obtained at three effective depth ranges approx. 0.37 m and 0.75 m for H-mode, 0.75 m and 1.5 m for V-mode. Recognition of the value of electrical conductivity was obtained in a grid of 50 x 50 m This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358714
title: Post-remediation evaluation of contaminated site using geophysical methods: EMI

description: For the EMI measurements, a ground conductivity meter EM38-MK2 (Geonics) was used. Based on the measurements carried out on two coil spacings every 0.5 m and 1 m in horizontal (H-mode) and vertical dipole mode (V-mode), a component reflecting the electrical conductivity was obtained at three effective depth ranges approx. 0.37 m and 0.75 m for H-mode, 0.75 m and 1.5 m for V-mode. Recognition of the value of electrical conductivity was obtained in a grid of 50 x 50 m This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358721
title: Post-remediation evaluation of contaminated site using geophysical methods: GPR

description: GPR measurements were conducted using RAMAC/GPR pulse radar with a CUII control unit (ABEM Malå Guideline Geo) with 500 MHz and 800 MHz shielded antennas. The time window was set as 61 ns and 98 ns, respectively, for 800 MHz and 500 MHz. The sampling frequency was set as 10 times the antenna frequency and the number of signals as 500 for both antennas for the best performance of the measurements. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358720
title: Post-remediation evaluation of contaminated site using geophysical methods: GPR

description: GPR measurements were conducted using RAMAC/GPR pulse radar with a CUII control unit (ABEM Malå Guideline Geo) with 500 MHz and 800 MHz shielded antennas. The time window was set as 61 ns and 98 ns, respectively, for 800 MHz and 500 MHz. The sampling frequency was set as 10 times the antenna frequency and the number of signals as 500 for both antennas for the best performance of the measurements. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358727
title: Post-remediation evaluation of contaminated site using geophysical methods: chemical analysis

description: The total concentration of trace elements analysis (of Al, As, Cd, Co, Cr, Cu, Fe, K, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Tl, Zn) was determined using ICP-ES for 50 samples and BCR sequential extraction for 20 samples. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”.
DOI: 10.5281/zenodo.7358726
title: Post-remediation evaluation of contaminated site using geophysical methods: chemical analysis

description: The total concentration of trace elements analysis (of Al, As, Cd, Co, Cr, Cu, Fe, K, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Tl, Zn) was determined using ICP-ES for 50 samples and BCR sequential extraction for 20 samples. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”.
DOI: 10.5281/zenodo.7358741
title: Post-remediation evaluation of contaminated site using geophysical methods: GPS

description: Localization of soilpits, profiles. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5281/zenodo.7358740
title: Post-remediation evaluation of contaminated site using geophysical methods: GPS

description: Localization of soilpits, profiles. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”
DOI: 10.5519/qd.6tcfjewk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.7358745
title: Post-remediation evaluation of contaminated site using geophysical methods: soilpits

description: Discription of soilpits. This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”