cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xr49v67fdy
title: EVALUATION OF WORK RELATED MUSCULOSKELETAL PAIN AND SLEEP HYGIENE AMONG IT PROFESSIONALS IN PONDICHERRY

description: to evaluate the prevalence of musculoskeletal pain and poor sleep hygiene among
IT professionals, who are often exposed to prolonged screen time, poor posture, irregular sleep
habits, and sedentary routines due to the nature of their work, and a total of 140 participants
were selected through randomised sampling and divided into two groups: Group A with less
than five years of IT experience and Group B with more than five years, with each participant
assessed using two validated tools—the Nordic Musculoskeletal Questionnaire (NMQ), which
measured pain in various body regions such as the neck, shoulders, lower back, and wrists, and
the Sleep Hygiene Index (SHI), which assessed behavioural patterns related to sleep such as
inconsistent bedtimes, electronic device usage, caffeine or stress-related disturbances, and the
data collected was analysed using unpaired t-tests to compare results between groups and
determine the significance of any differences, revealing that while musculoskeletal discomfort
was highly prevalent in both groups—especially in the neck and lower back—the difference
between the groups was not statistically significant, suggesting that even early-career
professionals are at similar risk as experienced ones, and the sleep hygiene scores showed
moderate issues across both groups, with no significant variation based on experience or
gender, although female participants reported significantly higher levels of musculoskeletal
pain, possibly due to anatomical, ergonomic, or stress-related factors, and overall, the study
concluded that musculoskeletal disorders and poor sleep hygiene are common across all IT
employees regardless of experience, indicating a need for workplace-level interventions such
as ergonomic training, proper posture education, frequent physical activity breaks, and sleep
hygiene awareness programs, which could collectively help in improving health outcomes,
preventing chronic conditions, and enhancing productivity and well-being among IT
professional
DOI: 10.17632/xr49v67fdy.1
title: EVALUATION OF WORK RELATED MUSCULOSKELETAL PAIN AND SLEEP HYGIENE AMONG IT PROFESSIONALS IN PONDICHERRY

description: to evaluate the prevalence of musculoskeletal pain and poor sleep hygiene among
IT professionals, who are often exposed to prolonged screen time, poor posture, irregular sleep
habits, and sedentary routines due to the nature of their work, and a total of 140 participants
were selected through randomised sampling and divided into two groups: Group A with less
than five years of IT experience and Group B with more than five years, with each participant
assessed using two validated tools—the Nordic Musculoskeletal Questionnaire (NMQ), which
measured pain in various body regions such as the neck, shoulders, lower back, and wrists, and
the Sleep Hygiene Index (SHI), which assessed behavioural patterns related to sleep such as
inconsistent bedtimes, electronic device usage, caffeine or stress-related disturbances, and the
data collected was analysed using unpaired t-tests to compare results between groups and
determine the significance of any differences, revealing that while musculoskeletal discomfort
was highly prevalent in both groups—especially in the neck and lower back—the difference
between the groups was not statistically significant, suggesting that even early-career
professionals are at similar risk as experienced ones, and the sleep hygiene scores showed
moderate issues across both groups, with no significant variation based on experience or
gender, although female participants reported significantly higher levels of musculoskeletal
pain, possibly due to anatomical, ergonomic, or stress-related factors, and overall, the study
concluded that musculoskeletal disorders and poor sleep hygiene are common across all IT
employees regardless of experience, indicating a need for workplace-level interventions such
as ergonomic training, proper posture education, frequent physical activity breaks, and sleep
hygiene awareness programs, which could collectively help in improving health outcomes,
preventing chronic conditions, and enhancing productivity and well-being among IT
professional
DOI: 10.17632/hm6c9b8y5v
title: Research_data_for_Multidimensional_analysis_MWL_SA_SC_in_HSC_navigation

description: This research data is used for multidimensional analysis of mental workload, situational awareness, and safety culture in high-speed craft navigation.
DOI: 10.17632/hm6c9b8y5v.2
title: Research_data_for_Multidimensional_analysis_MWL_SA_SC_in_HSC_navigation

description: This research data is used for multidimensional analysis of mental workload, situational awareness, and safety culture in high-speed craft navigation.
DOI: 10.17632/bcgfpmprzg
title: Occupational Safety and Health Practices, Job Satisfaction, and Environmental Responsibility among Construction Workers

description: This dataset contains responses from 246 construction workers across nine administrative regions in Bali, Indonesia. The survey was designed to assess workers’ perceptions of Occupational Safety and Health (OSH) practices, their levels of job satisfaction, and their environmental responsibility behavior. The instrument includes five dimensions of OSH practices—awareness and compliance, training, use of personal protective equipment, worker participation, and management commitment—measured using reflective indicators. The dataset supports a structural equation model (PLS-SEM) examining the direct and indirect relationships among these constructs, particularly the mediating role of job satisfaction. This data can be used for further research in construction management, occupational health, and environmental behavior in emerging economies.
DOI: 10.17632/bcgfpmprzg.1
title: Occupational Safety and Health Practices, Job Satisfaction, and Environmental Responsibility among Construction Workers

description: This dataset contains responses from 246 construction workers across nine administrative regions in Bali, Indonesia. The survey was designed to assess workers’ perceptions of Occupational Safety and Health (OSH) practices, their levels of job satisfaction, and their environmental responsibility behavior. The instrument includes five dimensions of OSH practices—awareness and compliance, training, use of personal protective equipment, worker participation, and management commitment—measured using reflective indicators. The dataset supports a structural equation model (PLS-SEM) examining the direct and indirect relationships among these constructs, particularly the mediating role of job satisfaction. This data can be used for further research in construction management, occupational health, and environmental behavior in emerging economies.
DOI: 10.17632/k5dyy67d2g
title: Digital transition in dairy production:(SSA)

description: Research Hypothesis
Digital technology adoption in SSA dairy systems significantly improves productivity, operational efficiency, and market integration, but adoption barriers (e.g., infrastructure, gender disparities) vary across countries due to contextual factors.
Data Collection Methodology
Aspect Details
Scope 137 dairy farms across Ghana (39), Kenya (41), Nigeria (16), Tanzania (16), Uganda (25)
Timing November–December 2023
Tools Closed-ended smartphone questionnaires (translated to local languages)
Sampling Hybrid: Random (representativeness) + Purposive (targeting tech-adopting farms)
Variables Digital tool usage, barriers (cost/infrastructure), efficiency gains, gender roles, market impacts
Ethics University-approved protocols; informed consent obtained

Key Findings
1. Efficiency Gains:
o Ghana showed highest improvement (mean: 9.38/21 codes) due to mobile/IoT tools.
o Nigeria/Uganda lagged (means: 3.75–3.36).
2. Adoption Barriers:
o Highest in Nigeria (mean: 20.25/33 codes: cost/infrastructure).
o Lowest in Uganda (mean: 5.20).
3. Marketing Impact:
o Tanzania/Ghana led in value-chain digitization (e.g., e-commerce platforms).
4. Gender Disparities:
o Severe in Ghana (12:1 male-farmer ratio) vs. parity in Kenya/Tanzania.

Data Interpretation
Dataset How to Interpret Use Cases
Q10 (Efficiency) Higher codes = advanced impacts (e.g., Code 15: AI-driven decisions) Prioritize high-impact tools (e.g., IoT over SMS)
Q13 (Barriers) Codes 1–10 = structural (cost); 11–20 = technical (skills); 21–33 = social (gender) Target interventions (e.g., Ghana: subsidize costs)
LDA Clustering (Fig 1) Uganda/Ghana = distinct clusters → country-specific adoption patterns Customize policies per country
Gender Data (Q19) Female participation correlates with tech adoption (r=0.68, p&lt;0.05) Design women-focused digital literacy programs

Notable Conclusions
• Tailored Solutions Needed:
o Ghana: Address cost barriers despite efficiency gains.
o Nigeria: Invest in electricity/internet.
o Uganda: Scale low-barrier model regionally.
• Gender Inclusion: Training + finance access for women farmers boosts adoption.
• Stepwise Tech Integration: Start mobile-based (SMS/market apps), then scale to AI/IoT.

Data Reusability
• Access: Restricted due to confidentiality; contact author (c.vuvor@studenti.uniss.it) for requests.
• Supplementary Files: S1 (questionnaires), S2 (qual-quant methodology), S3 (coding schemes) enable methodological replication.
• Aggregated Data: Tables 2–4 support cross-country benchmarking (e.g., barrier severity indices).
 Key Insight: Digital tools can close SSA’s dairy supply-demand gap if deployed contextually—addressing gender, infrastructure, and cost barriers
DOI: 10.17632/k5dyy67d2g.1
title: Digital transition in dairy production:(SSA)

description: Research Hypothesis
Digital technology adoption in SSA dairy systems significantly improves productivity, operational efficiency, and market integration, but adoption barriers (e.g., infrastructure, gender disparities) vary across countries due to contextual factors.
Data Collection Methodology
Aspect Details
Scope 137 dairy farms across Ghana (39), Kenya (41), Nigeria (16), Tanzania (16), Uganda (25)
Timing November–December 2023
Tools Closed-ended smartphone questionnaires (translated to local languages)
Sampling Hybrid: Random (representativeness) + Purposive (targeting tech-adopting farms)
Variables Digital tool usage, barriers (cost/infrastructure), efficiency gains, gender roles, market impacts
Ethics University-approved protocols; informed consent obtained

Key Findings
1. Efficiency Gains:
o Ghana showed highest improvement (mean: 9.38/21 codes) due to mobile/IoT tools.
o Nigeria/Uganda lagged (means: 3.75–3.36).
2. Adoption Barriers:
o Highest in Nigeria (mean: 20.25/33 codes: cost/infrastructure).
o Lowest in Uganda (mean: 5.20).
3. Marketing Impact:
o Tanzania/Ghana led in value-chain digitization (e.g., e-commerce platforms).
4. Gender Disparities:
o Severe in Ghana (12:1 male-farmer ratio) vs. parity in Kenya/Tanzania.

Data Interpretation
Dataset How to Interpret Use Cases
Q10 (Efficiency) Higher codes = advanced impacts (e.g., Code 15: AI-driven decisions) Prioritize high-impact tools (e.g., IoT over SMS)
Q13 (Barriers) Codes 1–10 = structural (cost); 11–20 = technical (skills); 21–33 = social (gender) Target interventions (e.g., Ghana: subsidize costs)
LDA Clustering (Fig 1) Uganda/Ghana = distinct clusters → country-specific adoption patterns Customize policies per country
Gender Data (Q19) Female participation correlates with tech adoption (r=0.68, p&lt;0.05) Design women-focused digital literacy programs

Notable Conclusions
• Tailored Solutions Needed:
o Ghana: Address cost barriers despite efficiency gains.
o Nigeria: Invest in electricity/internet.
o Uganda: Scale low-barrier model regionally.
• Gender Inclusion: Training + finance access for women farmers boosts adoption.
• Stepwise Tech Integration: Start mobile-based (SMS/market apps), then scale to AI/IoT.

Data Reusability
• Access: Restricted due to confidentiality; contact author (c.vuvor@studenti.uniss.it) for requests.
• Supplementary Files: S1 (questionnaires), S2 (qual-quant methodology), S3 (coding schemes) enable methodological replication.
• Aggregated Data: Tables 2–4 support cross-country benchmarking (e.g., barrier severity indices).
 Key Insight: Digital tools can close SSA’s dairy supply-demand gap if deployed contextually—addressing gender, infrastructure, and cost barriers
DOI: 10.17632/b735d2tfh7
title: Encoding the Glucose Identity by Discrete Hypothalamic Neurons via the Gut-Brain Axis

description: Animals need daily intakes of three macronutrients: sugar, protein and fat. Under fasted conditions, however, animals prioritize sugar as a primary source of energy. They must detect ingested sugar – specifically D-glucose – and quickly report its presence to the brain. Hypothalamic neurons that can respond to the caloric content in the gut regardless the identity of macronutrient have been identified, but until now, the existence of neurons that can encode the specific macronutrients remained unknown. We found that a subset of corticotropin-releasing factor (CRF)-expressing neurons in the hypothalamic paraventricular nucleus (CRFPVN) respond specifically to D-glucose in the gut, separately from other macronutrients or sugars. CRFPVN neuronal activity is essential for fasted mice to develop a preference for D-glucose. These responses of CRFPVN neurons to intestinal D-glucose require a specific spinal gut-brain pathway including the dorsal lateral parabrachial nuclei. These findings reveal the neural circuit that encodes the identity of D-glucose.
DOI: 10.17632/b735d2tfh7.2
title: Encoding the Glucose Identity by Discrete Hypothalamic Neurons via the Gut-Brain Axis

description: Animals need daily intakes of three macronutrients: sugar, protein and fat. Under fasted conditions, however, animals prioritize sugar as a primary source of energy. They must detect ingested sugar – specifically D-glucose – and quickly report its presence to the brain. Hypothalamic neurons that can respond to the caloric content in the gut regardless the identity of macronutrient have been identified, but until now, the existence of neurons that can encode the specific macronutrients remained unknown. We found that a subset of corticotropin-releasing factor (CRF)-expressing neurons in the hypothalamic paraventricular nucleus (CRFPVN) respond specifically to D-glucose in the gut, separately from other macronutrients or sugars. CRFPVN neuronal activity is essential for fasted mice to develop a preference for D-glucose. These responses of CRFPVN neurons to intestinal D-glucose require a specific spinal gut-brain pathway including the dorsal lateral parabrachial nuclei. These findings reveal the neural circuit that encodes the identity of D-glucose.
DOI: 10.15468/dl.eac947
title: Occurrence Download

description: A dataset containing 307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Podocarpus salignus D.Don",
 "Country is Chile",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 307 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049608-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7m2mgc6fk5
title: Myths and Perception on COVID-19 Vaccination among the Adult Population: A Case of Ilala District in Dar es Salaam Region and Moshi District in Kilimanjaro Region

description: Myths and Perception on COVID-19 Vaccination among the Adult Population: A Case of
Ilala District in Dar es Salaam Region and Moshi District in Kilimanjaro Region. This data supports the findings on the research on myth and perception of COVID 19 in the 2020's pandemic.
DOI: 10.15468/dl.2hpx6n
title: Occurrence Download

description: A dataset containing 15305 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cervus nippon Temminck, 1838"
 ]
}

 The dataset includes 15305 records from 231 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049606-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7m2mgc6fk5.1
title: Myths and Perception on COVID-19 Vaccination among the Adult Population: A Case of Ilala District in Dar es Salaam Region and Moshi District in Kilimanjaro Region

description: Myths and Perception on COVID-19 Vaccination among the Adult Population: A Case of
Ilala District in Dar es Salaam Region and Moshi District in Kilimanjaro Region. This data supports the findings on the research on myth and perception of COVID 19 in the 2020's pandemic.
DOI: 10.15468/dl.vezuvr
title: Occurrence Download

description: A dataset containing 7013 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pentanema hirtum (L.) D.Gut.Larr., Santos-Vicente, Anderb., E.Rico & M.M.Mart.Ort., Tanacetum bipinnatum (L.) Sch.Bip., Alnus hirsuta (Spach) Rupr., Corylus sieboldiana Blume, Corylus heterophylla Fisch. ex Trautv.)",
 "Geometry POLYGON ((-180 45.53015, 180 45.53015, 180 83.62313, -180 83.62313, -180 45.53015))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is less than or equal to 50000"
 ]
}

 The dataset includes 7013 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049607-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/g7ncmhrcrz.1
title: Lithium-ion cells EIS dataset with fitting and degradation modes

description: This Dataset is composed of two folders: “ExperimentalDATA” and “OutputDATA” which includes data from eight cells tested according to experimental protocols described in the associated article from Authors to which this dataset is associated. 

Tested cells are cylindrical lithium-ion cells model INR18650-30Q manufactured by Samsung SDI Co., Ltd. with a nominal capacity of 2.95 Ah. Each couple of cells has been aged up to 80% of their initial capacity under four different operating profiles. Parametric check-up, at fixed steps of 25 cycles, have been performed to collect capacity fade and EIS measurement data.
ExperimentalDATA Folder: Inside this folder, there are eight .xlsx files, named according to the convention “EISexpCellxx.xlsx”, meaning EIS data experimental for the cell “xx”. Eight cells have been tested therefore there are eight files, each containing ten columns as descripted below.

• Aging cycle: Indicates the number of aging cycles the cell has undergone prior to the impedance spectroscopy measurement.
• SOH (%) (State of Health): Represents the remaining capacity of the cell as a percentage of its initial capacity, calculated after the aging cycles according to the manufacturer’s protocol.
• SOC (%) (State of Charge): Indicates the cell’s charge level, expressed as a percentage of its current capacity, at the time the spectroscopy was performed.
• R_int (%): The percentage increase in internal resistance compared to a fresh cell, calculated using Ohm’s law (ΔV/I). This value is obtained for each SOC level after a controlled discharge test. The voltage variation (ΔV) is calculated as the difference between the OCV value at the reference SOC and the voltage measured 5 seconds after the application of the constant current.
• Frequency (Hz): Frequency points used for the impedance spectroscopy measurements.
• Zmod (Ohm): The module of the impedance calculated at each frequency during the spectroscopy.
• Zphz (deg): The phase of the impedance calculated at each frequency during the spectroscopy.
• Zreal (Ohm): Real component of the complex impedance.
• Zimg (Ohm): Imaginary component of the complex impedance.
• OCV (V): The voltage measured at open circuit for each SOC level, after a 45-minute relaxation period.

OutputDATA Folder: It contains eight subfolders, each corresponding to one of the tested cells for which experimental data was provided. Inside each subfolder there are two .xlsx files named “Circuit_parameter_Cellxx.xlsx” and “z_predicted_Cellxx.xlsx”, which represent the two main outputs of the algorithm discussed in paper from Authors.

• The file “Circuit_parameter_Cellxx.xlsx” contains the nine circuit parameters, the corresponding fitting metrics and the aging-related parameters (LAM, LLI, CL) sorted for aging cycle and SOC level.
• The file “z_predicted_Cellxx.xlsx” contains the predicted values of the real and imaginary parts of the impedance for each considered frequency, sorted for aging cycle and SOC level.
DOI: 10.17632/365r7z2y3h.1
title: Field and experiment dataset

description: Seasonal measurements of seagrass morphometric (leaf length, shoot density) and biomass collected from cores samples (5x10cm diameter) collected from five sites in Langebaan Lagoon, South Africa. Related environmental data (temperature, salinity, turbidity, pH, Chl a, O2) taken using a handheld probe are also included. Experimental data from a mesocosm study assessing trait responses under varying temperature conditions are also provided.
DOI: 10.17632/dx5xyyfz2y
title: An Urban Multi-Operator QoE-Aware Dataset for Cellular Networks in Dense Environments

description: This dataset contains 30,925 labelled and cleaned records collected from a dense 2 km² urban area surrounding Sunway University, Selangor, Malaysia. Using the GNetTrack Pro mobile application and Samsung S21 Ultra devices, the data spans three anonymized commercial mobile network operators and includes both 4G and 5G technologies. The dataset captures radio signal quality metrics (RSRP, RSRQ, SNR, etc.), geospatial information, mobility patterns (walking vs. driving), and application-specific traffic scenarios (HTTP, FTP, 1080p Video Streaming).

A total of 132 physical cell sites were validated via OpenCellID and field inspections. The dataset is released in CSV format and includes Python scripts for data preprocessing and basic visualization. This makes it a valuable resource for machine learning tasks like signal metric regression, handover optimization, and QoE modeling in heterogeneous and simulation of high-density urban networks.

Key features:

Real-world 5G/4G measurements

Multi-operator and multi-mobility modes

Traffic-aware profiling

Empirical validation of base station locations

Ready for ML/DL use cases

For more information look out for our article about the dataset on data in brief journal.
DOI: 10.17632/dx5xyyfz2y.1
title: An Urban Multi-Operator QoE-Aware Dataset for Cellular Networks in Dense Environments

description: This dataset contains 30,925 labelled and cleaned records collected from a dense 2 km² urban area surrounding Sunway University, Selangor, Malaysia. Using the GNetTrack Pro mobile application and Samsung S21 Ultra devices, the data spans three anonymized commercial mobile network operators and includes both 4G and 5G technologies. The dataset captures radio signal quality metrics (RSRP, RSRQ, SNR, etc.), geospatial information, mobility patterns (walking vs. driving), and application-specific traffic scenarios (HTTP, FTP, 1080p Video Streaming).

A total of 132 physical cell sites were validated via OpenCellID and field inspections. The dataset is released in CSV format and includes Python scripts for data preprocessing and basic visualization. This makes it a valuable resource for machine learning tasks like signal metric regression, handover optimization, and QoE modeling in heterogeneous and simulation of high-density urban networks.

Key features:

Real-world 5G/4G measurements

Multi-operator and multi-mobility modes

Traffic-aware profiling

Empirical validation of base station locations

Ready for ML/DL use cases

For more information look out for our article about the dataset on data in brief journal.