Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

“…</p><p><br></p><p dir="ltr">#METADATA</p><p dir="ltr">#'data.frame': 472 obs. of 28 variables:</p><p dir="ltr"> #$ ID: Factor variable; a unique identity for the response to the survey</p><p dir="ltr"> #$ Year: Factor variable; six factors available (2016, 2017, 2018, 2019, 2020, 2021) representing the year for the response to the survey</p><p dir="ltr"> #$ N_dead_annual: Numeric variable; representing the number of colonies annually lost</p><p dir="ltr">#$ N_alive_annual: Numeric variable; representing the number of colonies annually alive</p><p dir="ltr"> #$ N_dead_dry: Numeric variable; representing the number of colonies lost during the dry season</p><p dir="ltr">#$ N_alive_dry: Numeric variable; representing the number of colonies alive during the dry season</p><p dir="ltr"> #$ N_dead_rainy: Numeric variable; representing the number of colonies lost during the rainy season</p><p dir="ltr">#$ N_alive_rainy: Numeric variable; representing the number of colonies alive during the rainy season</p><p dir="ltr"> #$ Education: Factor variable; four factors are available ("Self-taught","Learned from another melip","Intro training","Formal tech training"), representing the training level in meliponiculture</p><p dir="ltr"> #$ Operation_Size: Numeric variable; representing the number of colonies managed by the participant (in n)</p><p dir="ltr"> #$ propAgri: Numeric variable; representing the percentage of agricultural area surrounding the meliponary (in %)</p><p dir="ltr"> #$ propForest: Numeric variable; representing the percentage of forested area surrounding the meliponary (in %)</p><p dir="ltr">#$ temp.avg_annual: Numeric variable; representing the average annual temperature (in ºC)</p><p dir="ltr">#$ precip_annual_sum: Numeric variable; representing the total accumulated precipitation (in mm)</p><p dir="ltr">#$ precip_Oct_March_sum: Numeric variable; representing the total accumulated precipitation between October to March (in mm)</p><p dir="ltr">#$ precip_Apri_Sept_sum: Numeric variable; representing the total accumulated precipitation between April to September (in mm)</p><p dir="ltr">#$ temp.avg_Oct_March: Numeric variable; representing the total accumulated precipitation between October to March (in ºC)</p><p dir="ltr">#$ temp.avg_Apri_Sept: Numeric variable; representing the total accumulated precipitation between April to September (in ºC)</p><p dir="ltr"> #$ Importance_dead: Factor variable; three factors are available Normal","High","Very high"), representing the perception of the significance of annual colony losses</p><p dir="ltr"> #$ Climatic_environmental: Binary variable; representing if the participant considered climatic and environmental problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Contamination: Binary variable; representing if the participant considered contamination problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr"> #$ Nutritional: Binary variable; representing if the participant considered nutritional problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Sanitary: Binary variable; representing if the participant considered sanitary problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Queen: Binary variable; representing if the participant considered queen problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Time: Binary variable; representing if the participant considered time problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Economic: Binary variable; representing if the participant considered economic problems as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Attacks: Binary variable; representing if the participant considered time attacks as a potential driver (1) or not (0) of their annual colony losses</p><p dir="ltr">#$ Swarming: Binary variable; representing if the participant considered swarming problems as a potential driver (1) or not (0) of their annual colony losses</p><p><br></p>…”

Scatter plot of scores in the first six progress tests by entry cohort. by Franco Romaní-Romaní (21989788)

Overview of the six original ACTIV-3/TICO trials. by Elizabeth S. Munroe (21568166)

Subjects:

Trends in average work volume (in hrs.) in six focal countries, 1992–2022. by Lena Hipp (11720192)

Characteristics of HACE respondents by year. by David Henderson (4612354)

Subjects:

Participants enrollment. by Chikondi Maluwa (20660522)

Subjects:

KAP assessment scores (n = 422). by Chikondi Maluwa (20660522)

Subjects:

Labor force participation rates and average working hours in six focal countries, 1992–2022. by Lena Hipp (11720192)

A flow diagram of the study entry. by Sakiko Fukui (387048)

“…</p><p>Methods</p><p>Sixty-nine deceased older adults from five special nursing homes were studied over 3.5 years (January 2016 to June 2020). Criteria included: at least six months’ residency before death, ability to eat orally during the study period, and death within the facility. …”

The life cycle-based CO₂ emissions from four technology-based categories of light-duty vehicles in GCAM/GLIMPSE from sales of new vehicles in 2030, (a) during lifetime,... by Pankaj Sadavarte (2012029)

Data Sheet 1_The six-year decomposition of coarse woody debris drives shifts in soil fungal communities in subtropical forests.docx by Nan Wang (21935)

Open data. by Larissa Brazolotto Ferreira (17671168)

Subjects:

Overview of A) precipitation, B) discharge and C) baseline PM water level data in the calibration period (April 2016 – April 2019) with six different scenario starting points, n = ... by Fabian Mitze (21084251)

Accuracy of multiple classifiers with a deep-learning-based feature set by Afira Aslam (21433666)

Subjects:

Demographics and home ranges of the study groups within and along the periphery of the Nouabalé-Ndoki National Park (NNNP), Republic of Congo. by Kristena Cooksey (20614095)

Subjects:

Characterising the i-band variability of YSOs over six orders of magnitude in timescale (dataset) by D Sergison (21909218)

“…We derive a new way of calculating structure functions for large simulated datasets (the “fast structure function”), based on fast Fourier transforms.…”

Participants characteristics. by Sakiko Fukui (387048)

“…</p><p>Methods</p><p>Sixty-nine deceased older adults from five special nursing homes were studied over 3.5 years (January 2016 to June 2020). Criteria included: at least six months’ residency before death, ability to eat orally during the study period, and death within the facility. …”

A flowchart of the methodology in this study. by HaiQun Huang (20978503)

Subjects:

Participant and lab results flow. by Gad Murenzi (6243719)

Subjects:

High-risk human papillomavirus prevalence stratified by age group and CD4 count category. by Gad Murenzi (6243719)

Subjects: