cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/jh4cjm4ftw.2
title: Energy and protein gap analysis in dairy cows kept under fodder based diet with cut and carry feeding system

description: Study hypothesis: There is a gap in dry matter, water, energy and protein intake of dairy cows kept in fodder- based diet under cut and carry feeding system in Sub- Saharan countries, Rwanda included. A study was done in small holder dairy farmers and 111 lactating dairy cows were used for observational data. Parameters analyzed included nutritive value of offered fodder, which was 25% of dry matter, 58% neutral detergent fibre, 9% crude protein and of 5.6 mega joules per kg DM. Mean land size was 0.5ha, area for fodder was 0.1ha, number of cattle was 1.7, mean body weight of cows was 430kg, days in milk was 145; cow age was 7 and parity was 3. Data on feeds characterization were computed with Groot et al., models while data for the production requirement of dairy cows under lactation were computed using LIGAPS dairy models. Statistical analysis was conducted using the SPSS Statistical package for social sciences (SPSS) version 26.0 (George and Malley, 2019). After testing the normality of data, data were grouped. Grouping variables were lowlands and highlands regions, breed types and lactation periods. Test between groups was done by means comparisons and their variances were tested using one way analysis of variance test. Means were significantly different at P&lt;0.05. Tested variables were dry matter intake, dry matter requirement, dry matter gap, body weight, metabolically weight, water intake, water requirement, water gap; milk yield, attainable milk, milk yield gap, protein intake, protein requirement, protein gap; energy intake, energy requirement, energy gap. Results showed that daily intake was 9kg DM; 35 liters of water; 898gr of crude protein and 55 MJ ME with current milk production of 6kg. The daily requirement for both maintenance and 16 kg milk production were 15kg DM: 56 liters of water, 1908 g CP and 137 MJ. The daily gap was 4.52kg DM, 22litres of water, 1109 gr of crude protein and 84 mega joules ME with 10 kg gap in milk yield per day. Results from this analysis will serve as instruments for researchers in developing dairy diet models to improve dairy cow productivity at household level. Demonstrating the gap of available feeds resources would trigger the uptake of new feeding technologies by farmers. Extension agents should focus in knowledge transfer on optimal harvesting time of existing fodder when their nutritive value content is optimal. For dairy feeds manufacturers, this would serve as a guide for proper rationing of high concentrates ingredients rich in energy and protein in consideration of their requirements. Policy makers need to facilitate farmers to have access to facilities for upgrading the available low-quality roughages to enhance their energy and protein content. Adopting best feeding management based on enhanced energy, protein and water intake would contribute to improved milk production resulting to better remuneration to farmers, increased national milk pool and per capita milk consumption.
DOI: 10.17632/vvvhskpj36.1
title: Impact of Wine Influencers' Characteristics on Consumer Behavior

description: This curated data is part of study on Wine influencer's chareactersitcs on consumer behaviour. Using the Elaboration Likelihood Model, this study examines the impact of wine influencers' characteristics on consumers' attitudes, purchase intentions, and actual buying behavior. A survey of 404 social media users was conducted using a structured questionnaire. The structural modeling analysis found that perceived credibility impacts attitudes toward influencers but not recommended brands. However, we have included only 100 rows of data of important study constructs.
DOI: 10.17632/8fp9dcn7w6.1
title: Deciphering nitrogen isotopic signatures of dissolved organic nitrogen from its molecular composition using machine learning methods

description: Dataset used for predicting DON's stable nitrogen isotopes using machine learning methods.
DOI: 10.17632/8fp9dcn7w6.2
title: Deciphering nitrogen isotopic signatures of dissolved organic nitrogen from its molecular composition using machine learning methods

description: Dataset used for predicting DON's stable nitrogen isotopes using machine learning methods.
DOI: 10.17632/ztgwzfd6yn.1
title: SETD3-mediated histidine methylation of MCM7 regulates DNA replication by facilitating chromatin loading of MCM

description: This dataset contains 6 main figures-related original data files, 6 supplementary figures-related orginal data files, and one zipped mass spectrometry data. The first two parts are excel format, and original data include unproceeded Western blotting images and digital data. The mass spectrometry data can be de-compressed to excel format, and include two original dataset.
DOI: 10.17632/ztgwzfd6yn.2
title: SETD3-mediated histidine methylation of MCM7 regulates DNA replication by facilitating chromatin loading of MCM

description: This dataset contains 6 main figures-related original data files, 8 supplementary figures-related orginal data files, and one zipped mass spectrometry data. The first two parts are excel format, and original data include unproceeded Western blotting images and digital data. The mass spectrometry data can be de-compressed to excel format, and include three original dataset.
DOI: 10.17632/ppftwcs7zr.1
title: International Time Trends and Differences in Topical Actinic Keratosis Therapy Utilization: Supplementary Materials

description: Supplementary Material for International Time Trends and Differences in Topical Actinic Keratosis Therapy Utilization
DOI: 10.17632/rtz7g5n8tt.1
title: Cingulate microstimulation induces negative decision-making via reduced top-down influence on primate fronto-cingulo-striatal network

description: Matlab data file of Granger Causal Influences.
DOI: 10.17632/rtz7g5n8tt.2
title: Cingulate microstimulation induces negative decision-making via reduced top-down influence on primate fronto-cingulo-striatal network

description: Matlab data file of Granger Causal Influences.
DOI: 10.17632/42dvfp4pkd.1
title: Work Engagement and Organizational Justice of nursing staff during COVID-19. 2023

description: To analyze the effect of the health crisis caused by the COVID-19 pandemic on the Work Engagement and Organizational Justice of the nursing staff in a public hospital.
DOI: 10.17632/27njybxg3s.1
title: Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using machine learning

description: The original codes and data
DOI: 10.17632/27njybxg3s.2
title: Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using machine learning

description: The original codes and data
DOI: 10.17632/2d99j6r599.1
title: Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using bioinformatics analysis and machine learning

description: AILI-Cuproptosis
DOI: 10.17632/hxvz8rhtk8.1
title: Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using bioinformatics analysis and machine learning

description: Original codes
DOI: 10.5281/zenodo.10878539
title: Xenium Full SGE dataset in Flatgeobuf format

description: Xenium Full SGE dataset in Flatgeobuf format
DOI: 10.5281/zenodo.10878540
title: Xenium Full SGE dataset in Flatgeobuf format

description: Xenium Full SGE dataset in Flatgeobuf format
DOI: 10.25392/leicester.data.24788124
title: Blaming Minorities During Public Health Crises

description: This dataset was collected during the COVID-19 pandemic to measure public blaming of minorities for the spread of COVID-19. It also includes measures of potential co-variates (perceived threat, locus of control, authoritarian values, and demographic variables).
DOI: 10.25392/leicester.data.24788124.v1
title: Blaming Minorities During Public Health Crises

description: This dataset was collected during the COVID-19 pandemic to measure public blaming of minorities for the spread of COVID-19. It also includes measures of potential co-variates (perceived threat, locus of control, authoritarian values, and demographic variables).
DOI: 10.17632/rhmp3dbkf6.1
title: Termite females prefer hybrid mating to parthenogenesis when conspecific males are absent

description: Parthenogenesis and interspecific mating are alternative reproductivereproduction strategies that species have evolved alternative strategies when the opportunity of sexual reproduction issexually reproduce were often hindered by the natural environment. However, it is still unclear how to make a choice between parthenogenesis and interspecific mating when females are unable to reproduce sexually with males of the same species. This study mainly investigated the reproductive behavior of female termites in the absence of male termites of the same species. Here, reproductive behaviors of females Reticulitermes aculabialis (Ra♀) were observed when they encountered a male R. flaviceps (Rf♂) with the absence of male R.aculabialis(Ra♂). The results showed that Ra♀- Rf♂ tandems were more prevalent and stable compared to Ra♀- Ra♀ tandems. When a tandem inadvertently broke apart, the female R. aculabialis displayed longer pauses while waiting for male R. flaviceps, but shorter pauses when another female R. aculabialis was involved. Furthermore, there were mating behavior between female R. aculabialis and males R. flaviceps, resulting in offspring produced in paired Ra♀- Rf♂ colonies were hybridization between female R. aculabialis and male R. flaviceps. These results suggest that interspecific mating and recombination is an important aspect for the choice of reproduction strategy, which will provide new insights into the trade-off between gene contribution rate and genetic diversity.
DOI: 10.5281/zenodo.10863657
title: Data for Fig.1

description: Data and codes used to generated figures in main manuscript and supplementary information for the manuscript entitled "Ocean Internal Tides Suppress Tropical Cyclones in the South China Sea".
DOI: 10.5281/zenodo.10863658
title: Data and codes for "Ocean Internal Tides Suppress Tropical Cyclones in the South China Sea"

description: Data and codes used to generated figures in main manuscript and supplementary information for the manuscript entitled "Ocean Internal Tides Suppress Tropical Cyclones in the South China Sea".
DOI: 10.5281/zenodo.10878392
title: Data for Fig.1
DOI: 10.6084/m9.figshare.25452343.v4
title: 2_perception_EEGdata

description: This dataset contains EEG data in experiment 2.Each folder contains:a) Raw EEG files for one subject (subX), named as "subX.vhdr", "subX.vmrk", and "subX.eeg" generated by BP eego device;b) a log file (.txt) that records all information during preprocessing;c) pure EEG datasets (.set file generated by eeglab) for each of four conditions.
DOI: 10.6084/m9.figshare.25480621
title: data for the figures.zip

description: The source data for the paper titled "Integrated and DC-Powered Superconducting Microcomb" is available
DOI: 10.6084/m9.figshare.25480621.v1
title: data for the figures.zip

description: The source data for the paper titled "Integrated and DC-Powered Superconducting Microcomb" is available
DOI: 10.17632/rmk89k4rhb.11
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These deidentified datasets have been certified for public release by the VA Boston Healthcare System's Privacy Office. Our data include approximately XX million appointment wait times for both VA & community-based medical professionals from January 1st, 2014 to June 30th, 2021. 

Please cite the following publication when using this dataset:

Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134.

A non-exhaustive list of other peer-reviewed articles using these data:

Griffith, KN, Ndugga, NH, & Pizer, SP. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313. 

Auty, SG, & Griffith, KN. (in press). “Medicaid Expansion Increased Appointment Wait Times in Maine and Virginia.” Journal of General Internal Medicine.
DOI: 10.17632/rmk89k4rhb.12
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These deidentified datasets have been certified for public release by the VA Boston Healthcare System's Privacy Office. Our data include approximately 47 million appointment wait times for both VA & community-based medical professionals from January 1st, 2014 to June 30th, 2021. 

Please cite the following publication when using this dataset:

Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134.

A non-exhaustive list of other peer-reviewed articles using these data:

Griffith, KN, Ndugga, NH, & Pizer, SP. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313. 

Auty, SG, & Griffith, KN. (in press). “Medicaid Expansion Increased Appointment Wait Times in Maine and Virginia.” Journal of General Internal Medicine.
DOI: 10.17632/rmk89k4rhb.13
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These deidentified datasets have been certified for public release by the VA Boston Healthcare System's Privacy Office. Our data include approximately 45 million appointment wait times for both VA & community-based medical professionals from January 1st, 2014 to December 31st, 2022. Special thanks to Erin Beilstein-Wedel for helpful feedback and troubleshooting! 

Please cite the following publication when using this dataset:

Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134.


A non-exhaustive list of other peer-reviewed articles using these data:

Griffith, KN, Ndugga, NH, & Pizer, SP. (2020). "Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers." JAMA Network Open 3(8): e2014313. 

Auty, SG, & Griffith, KN. (2021). “Medicaid Expansion Increased Appointment Wait Times in Maine and Virginia.” Journal of General Internal Medicine 37(10): 2594-2596.

Griffith, KN, Asfaw, DA, Childers, RG, & Wilper, AP. (2022). “Changes in veterans’ access to specialty care during the COVID-19 pandemic.” JAMA Network Open 5(9): e2232515.

Feyman, Y, Asfaw, D, & Griffith, KN. (2022). “Geographic Variation in Appointment Wait Times for U.S. Military Veterans.” JAMA Network Open 5(8): e2228783.

See more on Google Scholar: https://scholar.google.com/scholar?oi=bibs&hl=en&cites=10379915202775127115&as_sdt=5
DOI: 10.17632/rmk89k4rhb.14
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These deidentified datasets have been approved for public release by the VA Boston Healthcare System's Institutional Review Board and Privacy Office. Our data include approximately 49 million appointment wait times for both VA & community-based medical professionals from January 1st, 2014 to December 31st, 2022. Special thanks to Erin Beilstein-Wedel for helpful feedback and troubleshooting! 

Please cite the following publication when using this dataset:

Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134.


A non-exhaustive list of other peer-reviewed articles using these data:

Griffith, KN, Ndugga, NH, & Pizer, SP. (2020). "Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers." JAMA Network Open 3(8): e2014313. 

Auty, SG, & Griffith, KN. (2021). “Medicaid Expansion Increased Appointment Wait Times in Maine and Virginia.” Journal of General Internal Medicine 37(10): 2594-2596.

Griffith, KN, Asfaw, DA, Childers, RG, & Wilper, AP. (2022). “Changes in veterans’ access to specialty care during the COVID-19 pandemic.” JAMA Network Open 5(9): e2232515.

Feyman, Y, Asfaw, D, & Griffith, KN. (2022). “Geographic Variation in Appointment Wait Times for U.S. Military Veterans.” JAMA Network Open 5(8): e2228783.

See more on Google Scholar: https://scholar.google.com/scholar?oi=bibs&hl=en&cites=10379915202775127115&as_sdt=5
DOI: 10.17632/rmk89k4rhb.15
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These deidentified datasets have been approved for public release by the VA Boston Healthcare System's Institutional Review Board and Privacy Office. Our data include approximately 52 million appointment wait times for both VA & community-based medical professionals from January 1st, 2014 to December 31st, 2022. Special thanks to Erin Beilstein-Wedel for helpful feedback and troubleshooting! 

Please cite the following publication when using this dataset:

Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134.


A non-exhaustive list of other peer-reviewed articles using these data:

Griffith, KN, Ndugga, NH, & Pizer, SP. (2020). "Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers." JAMA Network Open 3(8): e2014313. 

Auty, SG, & Griffith, KN. (2021). “Medicaid Expansion Increased Appointment Wait Times in Maine and Virginia.” Journal of General Internal Medicine 37(10): 2594-2596.

Griffith, KN, Asfaw, DA, Childers, RG, & Wilper, AP. (2022). “Changes in veterans’ access to specialty care during the COVID-19 pandemic.” JAMA Network Open 5(9): e2232515.

Feyman, Y, Asfaw, D, & Griffith, KN. (2022). “Geographic Variation in Appointment Wait Times for U.S. Military Veterans.” JAMA Network Open 5(8): e2228783.

See more on Google Scholar: https://scholar.google.com/scholar?oi=bibs&hl=en&cites=10379915202775127115&as_sdt=5
DOI: 10.17632/rmk89k4rhb.10
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These deidentified datasets have been certified for public release by the VA Boston Healthcare System's Privacy Office. Our data include approximately XX million appointment wait times for both VA & community-based medical professionals from January 1st, 2014 to June 30th, 2021. 

Please cite the following publication when using this dataset:

Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134.

A non-exhaustive list of other peer-reviewed articles using these data:

Griffith, KN, Ndugga, NH, & Pizer, SP. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313. 

Auty, SG, & Griffith, KN. (in press). “Medicaid Expansion Increased Appointment Wait Times in Maine and Virginia.” Journal of General Internal Medicine.
DOI: 10.17632/rmk89k4rhb.6
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These data have been certified for public release by the VA Boston Healthcare System's Privacy Officer. Please cite the following publication when using this dataset: Y. Feyman, K.N. Griffith. (under review). Appointment wait times for primary and specialty care in Veterans Health Administration facilities vs. community medical centers. Data in Brief. These data were originally collected for the following research article: K.N. Griffith, N.J. Ndugga, S.D. Pizer. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313.
DOI: 10.17632/rmk89k4rhb.7
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These data have been certified for public release by the VA Boston Healthcare System's Privacy Officer. Please cite the following publication when using this dataset: Y. Feyman, K.N. Griffith. (under review). Appointment wait times for primary and specialty care in Veterans Health Administration facilities vs. community medical centers. Data in Brief. These data were originally collected for the following research article: K.N. Griffith, N.J. Ndugga, S.D. Pizer. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313.
DOI: 10.17632/rmk89k4rhb.8
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These data have been certified for public release by the VA Boston Healthcare System's Privacy Officer. Please cite the following publication when using this dataset: Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134. These data were originally collected for the following research article: K.N. Griffith, N.J. Ndugga, S.D. Pizer. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313.
DOI: 10.17632/rmk89k4rhb.9
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: These data have been certified for public release by the VA Boston Healthcare System's Privacy Officer. Please cite the following publication when using this dataset: Y. Feyman, Legler, A., & K.N. Griffith. (2021). Appointment Wait Time Data for Primary & Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers. Data in Brief: e107134. These data were originally collected for the following research article: K.N. Griffith, N.J. Ndugga, S.D. Pizer. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313.
DOI: 10.17632/rmk89k4rhb.1
title: Appointment Wait Times for Primary and Specialty Care in Veterans Health Administration Facilities vs. Community Medical Centers

description: Please cite the following publication when using this dataset: Y. Feyman, K.N. Griffith. (under review). Appointment wait times for primary and specialty care in Veterans Health Administration facilities vs. community medical centers. Data in Brief. These data were originally collected for the following research article: K.N. Griffith, N.J. Ndugga, S.D. Pizer. (2020). Appointment Wait Times for Specialty Care in Veterans Health Administration Facilities vs Community Medical Centers. JAMA Network Open 3(8): e2014313.