cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34945/f5rw24
title: Imaging Features, Functional Recovery, and Selected Single Nucleotide Polymorphism Data from TRACK-TBI Pilot Dataset

description: STUDY PURPOSE: The goal of this specific analysis of a small set of TRACK-TBI Pilot dataset variables was to assess the role of specific genetic polymorphisms as biomarkers of patient outcome after mild TBI with analysis of 3 single nucleotide polymorphisms (SNPs) associated with altered striatal dopamine levels: ANKK1 C/T (rs1800497), COMT Met/Val (rs4680) and DRD2 C/T rs6277 genotypes. DATA COLLECTED: This is a set of 586 de-identified adult human patients with mild traumatic brain injury (Glasgow Coma Score &gt;12), from a dataset previously published as supplement by Nielson et al. 2017 (see originating publication and relevant links). Seventeen outcome measures were chosen based on clinical relevance, including computed tomography (CT) features, functional recovery (Glasgow Outcome Scale-Extended), cognitive outcomes (Wechsler Adult Intelligence Scale) and post-traumatic stress disorder (PTSD). CT features were derived acutely after brain injury. The functional, cognitive, and PTSD data were collected 6 months after injury. DATA USAGE NOTES:
DOI: 10.5281/zenodo.8347127
title: Gap-free 1-km PM2.5 dataset in China (2000-present)

description: This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to 10.5281/zenodo.7229348. More datasets can be found on the right navigation panel of 10.5281/zenodo.4569557. 
We also estimate other atmospheric data:
For full-coverage, 1-km, AOD data in China, please go to harvard dataverse. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.
We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method. Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The monthly average data during 2000-2022 are released here GEOTIFF format (if you want CSV files, please go to 10.5281/zenodo.8084388) and free for non-commercial use. If you want use our dataset, please cite the following publication. 
The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. 
-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. Journal of Environmental Management, 342, 118145.[url]
-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. Atmospheric Research, 281, 106481.[url]
 
If you want other atmospheric data, e.g., CO2 dataset, please go to 10.5281/zenodo.10022904.
DOI: 10.5281/zenodo.8347128
title: Gap-free 1-km PM2.5 dataset in China (2000-present)

description: This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to 10.5281/zenodo.7229348. More datasets can be found on the right navigation panel of 10.5281/zenodo.4569557. 
We also estimate other atmospheric data:
For full-coverage, 1-km, AOD data in China, please go to harvard dataverse. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.
We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method. Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The monthly average data during 2000-2022 are released here GEOTIFF format (if you want CSV files, please go to 10.5281/zenodo.8084388) and free for non-commercial use. If you want use our dataset, please cite the following publication. 
The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. 
-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. Journal of Environmental Management, 342, 118145.[url]
-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. Atmospheric Research, 281, 106481.[url]
 
If you want other atmospheric data, e.g., CO2 dataset, please go to 10.5281/zenodo.10022904.
DOI: 10.7924/r4542x03c
title: Data from: Gut mucosal cells transfer α-synuclein to the vagus nerve

description: Epidemiological and histopathological findings have raised the possibility that misfolded α-synuclein protein might spread from the gut to brain and increase the risk of Parkinson’s disease (PD). While past experimental studies in mouse models have relied on gut injections of exogenous recombinant α-synuclein fibrils to study gut to brain α-synuclein transfer, the possible origins of misfolded α-synuclein within the gut have remained elusive. We recently demonstrated that sensory cells of intestinal mucosa express α-synuclein. Here, we employed mouse intestinal organoids expressing human α-synuclein to observe the transfer of α-synuclein protein from epithelial cells in organoids to cocultured nodose neurons devoid of α-synuclein. In mice expressing human α-synuclein, but no mouse α-synuclein, α-synuclein fibril templating activity emerges in α-synuclein seeded fibril aggregation assays in intestine, vagus nerve, and dorsal motor nucleus. In newly engineered transgenic mice that restrict pathological human α-synuclein expression to intestinal epithelial cells, α-synuclein fibril-templating activity transfers to the vagus nerve and dorsal motor nucleus. Subdiaphragmatic vagotomy prior to induction of α-synuclein expression in intestinal epithelial cells effectively protects the hindbrain from emergence of α-synuclein fibril templating activity. Overall, these findings highlight a potential non-neuronal source of fibrillar α-synuclein protein that might arise in gut mucosal cells.
DOI: 10.5281/zenodo.8095000
title: Orthodontic Meta-Analyses Using Trial Sequential Analysis

description: Dataset of  Orthodontic Meta-Analyses Using Trial Sequential Analysis with a tutorial material for TSA software. The tutorial material was done for the SR with id number of 35 in the attached dataset.
DOI: 10.5281/zenodo.8095001
title: Orthodontic Meta-Analyses Using Trial Sequential Analysis

description: Dataset of  Orthodontic Meta-Analyses Using Trial Sequential Analysis with a tutorial material for TSA software. The tutorial material was done for the SR with id number of 35 in the attached dataset.
DOI: 10.5281/zenodo.10021576
title: Analysis of publisher policies on code sharing

description: At the 19th Research Data Alliance Plenary (June 2022), a Birds of a Feather session was convened to discuss aspects of code sharing and investigate if there was a consensus on which aspect of code sharing the community should be a focus of attention. The group, which included publishers, institutions, researchers and infrastructure providers, agreed to focus on "code" (i.e., "single use" or "one-shot", often referred to as "analysis code") that usually accompanies published articles and increasing the availability of this code. Note that this refers to a specific context in which "source code" is created and not "code" more generally.
Following this session, a Research Software Alliance task force on code availability was convened and three group members carried out a high level landscape analysis of publisher policies around code sharing using the CHORUS Publisher Data Availability Policies Index to gather a list of publishers. After feedback from other group members a more comprehensive analysis of the policies was carried out. Publisher policies were assessed for the presence of language related to code sharing and, if present, it was subsequently characterised. Coding of the policies and associated language was carried out independently by all three authors and a consensus derived. Details are given on the README tab of the dataset.
DOI: 10.5281/zenodo.10021575
title: Analysis of publisher policies on code sharing

description: At the 19th Research Data Alliance Plenary (June 2022), a Birds of a Feather session was convened to discuss aspects of code sharing and investigate if there was a consensus on which aspect of code sharing the community should be a focus of attention. The group, which included publishers, institutions, researchers and infrastructure providers, agreed to focus on "code" (i.e., "single use" or "one-shot", often referred to as "analysis code") that usually accompanies published articles and increasing the availability of this code. Note that this refers to a specific context in which "source code" is created and not "code" more generally.
Following this session, a Research Software Alliance task force on code availability was convened and three group members carried out a high level landscape analysis of publisher policies around code sharing using the CHORUS Publisher Data Availability Policies Index to gather a list of publishers. After feedback from other group members a more comprehensive analysis of the policies was carried out. Publisher policies were assessed for the presence of language related to code sharing and, if present, it was subsequently characterised. Coding of the policies and associated language was carried out independently by all three authors and a consensus derived. Details are given on the README tab of the dataset.
DOI: 10.5281/zenodo.8132208
title: Trial Sequential Analysis: A simple guide for judging the conclusiveness of the effect

description: The data used in analyzing a meta-analysis using Trial sequential analysis software(TSA v0.9.5.10).(https://ctu.dk/tsa)
DOI: 10.5281/zenodo.8132209
title: Trial Sequential Analysis: A simple guide for judging the conclusiveness of the effect

description: The data used in analyzing a meta-analysis using Trial sequential analysis software(TSA v0.9.5.10).(https://ctu.dk/tsa)
DOI: 10.5281/zenodo.8356579
title: A PINK1 input threshold arises from positive feedback in the PINK1/Parkin mitophagy decision circuit

description: Plasmid maps, single cell data, and analysis code for the journal article titled "A PINK1 input threshold arises from positive feedback in the PINK1/Parkin mitophagy decision circuit"
 
Mechanisms that prevent accidental activation of the PINK1/Parkin mitophagy circuit on healthy mitochondria are poorly understood. On the surface of damaged mitochondria, PINK1 accumulates and acts as the input signal to a positive feedback loop of Parkin recruitment, which in turn promotes mitochondrial degradation via mitophagy. However, PINK1 is also present on healthy mitochondria, where it could errantly recruit Parkin and thereby activate this positive feedback loop. Here, we explore emergent properties of the PINK1/Parkin circuit by quantifying the relationship between mitochondrial PINK1 concentrations and Parkin recruitment dynamics. We find that Parkin is recruited to mitochondria only if PINK1 levels exceed a threshold and then only after a delay that is inversely proportional to PINK1 levels. Furthermore, these two regulatory properties arise from the input-coupled positive feedback topology of the PINK1/Parkin circuit. These results outline an intrinsic mechanism by which the PINK1/Parkin circuit can avoid errant activation on healthy mitochondria.
DOI: 10.5281/zenodo.8356580
title: A PINK1 input threshold arises from positive feedback in the PINK1/Parkin mitophagy decision circuit

description: Plasmid maps, single cell data, and analysis code for the journal article titled "A PINK1 input threshold arises from positive feedback in the PINK1/Parkin mitophagy decision circuit"
 
Mechanisms that prevent accidental activation of the PINK1/Parkin mitophagy circuit on healthy mitochondria are poorly understood. On the surface of damaged mitochondria, PINK1 accumulates and acts as the input signal to a positive feedback loop of Parkin recruitment, which in turn promotes mitochondrial degradation via mitophagy. However, PINK1 is also present on healthy mitochondria, where it could errantly recruit Parkin and thereby activate this positive feedback loop. Here, we explore emergent properties of the PINK1/Parkin circuit by quantifying the relationship between mitochondrial PINK1 concentrations and Parkin recruitment dynamics. We find that Parkin is recruited to mitochondria only if PINK1 levels exceed a threshold and then only after a delay that is inversely proportional to PINK1 levels. Furthermore, these two regulatory properties arise from the input-coupled positive feedback topology of the PINK1/Parkin circuit. These results outline an intrinsic mechanism by which the PINK1/Parkin circuit can avoid errant activation on healthy mitochondria.
DOI: 10.5066/p9mi5sk1
title: 2021 Organic Matter Research Lab Full Spectra Absorbance Data

description: The USGS CAWSC Organic Matter Research Laboratory (OMRL) provides laboratory services and support to regional and national projects in the analysis of organic matter using the latest methods in absorbance and fluorescence spectroscopy. Optical measurements such as absorbance and fluorescence are used to gain insight into dissolved organic matter (DOM) composition, and can also serve as proxies for more expensive and difficult to obtain measurements. These techniques are relatively rapid and inexpensive and allow for the comprehensive tracking of DOM dynamics in aquatic ecosystems ranging from rivers and lakes to estuaries to open marine systems. Absorbance spectra and fluorescence matrices were simultaneously collected on filtered water samples at room temperature (21 �C) in an acid-cleaned 1 cm quartz cuvette using a spectrofluorometer equipped with a charge-coupled device (CCD) (Aqualog�, Horiba Instruments, New Jersey, U.S.A.). Excitation and absorbance scans were performed using a double-grating monochrometer, a 150 W Xenon arc lamp, a 5 nm bandpass, and a 1 s integration time at wavelengths of 240-800 nm. Emission spectra were collected with a CCD at approximately 2.3 nm (4 pixel) intervals at wavelengths of 245?800 nm. Excitation and absorbance wavelengths were scanned from low to high energy (i.e., VIS to UV) to reduce UV exposure of the sample, thus limiting the effects of photobleaching during analysis. Data presented here were analyzed by the U.S Geological Survey California Water Science Center Organic Matter Research Lab, Sacramento CA. This release contains full spectra absorbance data measured on the Aqualog � fluorometer January-December 2021.
DOI: 10.34740/kaggle/dsv/6787225
title: Latest U.S. Agricultural Trade Data
DOI: 10.6084/m9.figshare.24430492
title: Text Mining for Genetic Interactions - Intermediate data files

description: These code files serve as intermediate code files for Text Mining of Scientific Articles for Genetic Interactions Project carried out by RMIT University final year data science students as part of Work Integrated Learning Project supervised by Dr. Roc, CSIRO.<br><br>The detailed description is provided in code files hosted on GitHub.<br>Repository link: https://github.com/rocreguant/text-mining
DOI: 10.6084/m9.figshare.24430492.v1
title: Text Mining for Genetic Interactions - Intermediate data files

description: These code files serve as intermediate code files for Text Mining of Scientific Articles for Genetic Interactions Project carried out by RMIT University final year data science students as part of Work Integrated Learning Project supervised by Dr. Roc, CSIRO.<br><br>The detailed description is provided in code files hosted on GitHub.<br>Repository link: https://github.com/rocreguant/text-mining
DOI: 10.34740/kaggle/ds/3904922
title: MystiqueMart: Deciphering Retail Rhythms
DOI: 10.5517/ccdc.csd.cc28k2lj
title: CCDC 2101787: Experimental Crystal Structure Determination

description: Related Article: Fei-Yan Yi|2021|CSD Communication|||
description: Related Article: Chunxiao Lu, Dengke Xiong, Chen Chen, Jiang Wang, Yuxuan Kong, Tian Liu, Shuanglu Ying, Fei-Yan Yi|2022|Inorg.Chem.|61|2587|doi:10.1021/acs.inorgchem.1c03628
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.cc28nd9n
title: CCDC 2104971: Experimental Crystal Structure Determination

description: Related Article: Yuan-He Li, Su-Lei Zhang, Yong Lu, Bo Xiao, Tian-Yu Sun, Qian-Qian Xu, Jia-Hua Chen, Zhen Yang|2023|Angew.Chem.,Int.Ed.|62|e202303075|doi:10.1002/anie.202303075
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.cc29kjp3
title: CCDC 2132015: Experimental Crystal Structure Determination

description: Related Article: Jiaxing Zhu, Fangfang Fu, Zhihao Zhang, Chuanjiang Hu, Yong Wang|2023|J.Porphyrins Phthalocyanines|27|861|doi:10.1142/S1088424623500864
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.cc2cbb79
title: CCDC 2184670: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Georgios Velkos, Sandra Schiemenz, Marco Rosenkranz, Yaofeng Wang, Bernd Büchner, Stanislav M. Avdoshenko, Alexey A. Popov, Fupin Liu|2022|Inorg.Chem.Front.|10|468|doi:10.1039/D2QI02224A
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.cc2cbb8b
title: CCDC 2184671: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Georgios Velkos, Sandra Schiemenz, Marco Rosenkranz, Yaofeng Wang, Bernd Büchner, Stanislav M. Avdoshenko, Alexey A. Popov, Fupin Liu|2022|Inorg.Chem.Front.|10|468|doi:10.1039/D2QI02224A
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.cc2cbb9c
title: CCDC 2184672: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Georgios Velkos, Sandra Schiemenz, Marco Rosenkranz, Yaofeng Wang, Bernd Büchner, Stanislav M. Avdoshenko, Alexey A. Popov, Fupin Liu|2022|Inorg.Chem.Front.|10|468|doi:10.1039/D2QI02224A
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.cc2cbbbd
title: CCDC 2184673: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Georgios Velkos, Sandra Schiemenz, Marco Rosenkranz, Yaofeng Wang, Bernd Büchner, Stanislav M. Avdoshenko, Alexey A. Popov, Fupin Liu|2022|Inorg.Chem.Front.|10|468|doi:10.1039/D2QI02224A
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.cc2cn3v0
title: CCDC 2194082: Experimental Crystal Structure Determination

description: Related Article: Bingze Wu, Hao Su, Aoyuan Cheng, Xuepeng Zhang, Tao Wang, Guoqing Zhang
|2023|Cell Reports Physical Science|4|101245|doi:10.1016/j.xcrp.2022.101245
description: Related Article: Tao Wang|2023|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.cc2d3flk
title: CCDC 2207838: Experimental Crystal Structure Determination

description: Related Article: E. Contreras Aguilar, E. Espitia Cogollo, G. A. Echeverría, O. E. Piro, J. L. Jios, S. E. Ulic, R. D. I. Molina, M. E. Arena|2023|New J.Chem.|47|18341|doi:10.1039/D3NJ02529B
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.cc2d3qkt
title: CCDC 2208116: Experimental Crystal Structure Determination

description: Related Article: Kai-Kai Wang, Jun Jing, Wen-Wen Zhou, Can Wang, Jun-Wei Ye, Ran Zhou, Ting-Ting Wang, Zhan-Yong Wang, Rongxiang Chen|2023|J.Org.Chem.|88|5982|doi:10.1021/acs.joc.3c00331
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.