cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sxpdsszps8.1
title: Longitudinal Quasi-Experimental Assessment Data: Kazakhstan

description: The data in this paper pertain to those analyzed as part of a pilot monitoring study as described by Courtney et al. [1]. While the study reported on the results for three languages, the paper at hand references the results for just one study involving the Kazakh medium-of-instruction. The first variable in that data includes is ‘Student_id’, which provides a unique anonymized student ID number for each student. The next six variables in the data vary between-schools: (1) ‘experiment’, for which each school is defined as either a pilot (experiment) or control school; ‘Region’, defining the region that each school is situated in the country; (3) School_name, the name assigned to each unique school; (4) ‘Language’ defining the medium-of-instruction used in each school; ‘School_type’ where schools are defined as either (i) small, (ii) gymnasium, (iii) lyceum, or (iv) common (common used as baseline in modelling).
Thereafter, the within-school variable, ‘Gender’ is given for which each child is defined as either male or female.
In addition, columns 9-92 pertain to the item-response data (for all elements, 0 = incorrect, 1 = correct) for each child on each item in the five tests.
2015 Test: Items OG1 to OG5.4 (columns 9-19; link items to 2016 are OG2.2A, OG2.3A, OG23A)
2016 Test: Items OG2.2 to OG11.2 (20-35; link items to 2017 are )
2017 Test: Items OG11.1 to K3.6 (36-47)
2018 Test: Items K4.1 to K3.3 (48-68)
2019 Test: Items X19K1 to K6 (69-92)

The data is filtered data. Only students who completed all five literacy assessments (2015 to 2019) were included in the data. Alternative approaches for dealing with missing data (attrition) in the analysis described in this article. Original data in Cyrillic used to showcase the R software’s capacity to manage and recode such data.
DOI: 10.17632/sxpdsszps8.2
title: Longitudinal Quasi-Experimental Assessment Data: Kazakhstan

description: The data in this paper pertain to those analyzed as part of a pilot monitoring study as described by Courtney et al. [1]. While the study reported on the results for three languages, the paper at hand references the results for just one study involving the Kazakh medium-of-instruction. The first variable in that data includes is ‘Student_id’, which provides a unique anonymized student ID number for each student. The next six variables in the data vary between-schools: (1) ‘experiment’, for which each school is defined as either a pilot (experiment) or control school; ‘Region’, defining the region that each school is situated in the country; (3) School_name, the name assigned to each unique school; (4) ‘Language’ defining the medium-of-instruction used in each school; ‘School_type’ where schools are defined as either (i) small, (ii) gymnasium, (iii) lyceum, or (iv) common (common used as baseline in modelling).
Thereafter, the within-school variable, ‘Gender’ is given for which each child is defined as either male or female.
In addition, columns 9-92 pertain to the item-response data (for all elements, 0 = incorrect, 1 = correct) for each child on each item in the five tests.
2015 Test: Items OG1 to OG5.4 (columns 9-19; link items to 2016 are OG2.2A, OG2.3A, OG23A)
2016 Test: Items OG2.2 to OG11.2 (20-35; link items to 2017 are )
2017 Test: Items OG11.1 to K3.6 (36-47)
2018 Test: Items K4.1 to K3.3 (48-68)
2019 Test: Items X19K1 to K6 (69-92)

The data is filtered data. Only students who completed all five literacy assessments (2015 to 2019) were included in the data. Alternative approaches for dealing with missing data (attrition) in the analysis described in this article. Original data in Cyrillic used to showcase the R software’s capacity to manage and recode such data.
DOI: 10.17632/sxpdsszps8.3
title: Longitudinal Quasi-Experimental Assessment Data: Kazakhstan

description: The data in this paper pertain to those analyzed as part of a pilot monitoring study as described by Courtney et al. [1]. While the study reported on the results for three languages, the paper at hand references the results for just one study involving the Kazakh medium-of-instruction. The first variable in that data includes is ‘Student_id’, which provides a unique anonymized student ID number for each student. The next six variables in the data vary between-schools: (1) ‘experiment’, for which each school is defined as either a pilot (experiment) or control school; ‘Region’, defining the region that each school is situated in the country; (3) School_name, the name assigned to each unique school; (4) ‘Language’ defining the medium-of-instruction used in each school; ‘School_type’ where schools are defined as either (i) small, (ii) gymnasium, (iii) lyceum, or (iv) common (common used as baseline in modelling).
Thereafter, the within-school variable, ‘Gender’ is given for which each child is defined as either male or female.
In addition, columns 9-92 pertain to the item-response data (for all elements, 0 = incorrect, 1 = correct) for each child on each item in the five tests.
2015 Test: Items OG1 to OG5.4 (columns 9-19; link items to 2016 are OG2.2A, OG2.3A, OG23A)
2016 Test: Items OG2.2 to OG11.2 (20-35; link items to 2017 are )
2017 Test: Items OG11.1 to K3.6 (36-47)
2018 Test: Items K4.1 to K3.3 (48-68)
2019 Test: Items X19K1 to K6 (69-92)

The data is filtered data. Only students who completed all five literacy assessments (2015 to 2019) were included in the data. Alternative approaches for dealing with missing data (attrition) in the analysis described in this article. Original data in Cyrillic used to showcase the R software’s capacity to manage and recode such data.
DOI: 10.17632/sxpdsszps8.4
title: Longitudinal Quasi-Experimental Assessment Data: Kazakhstan

description: The data in this paper pertain to those analyzed as part of a pilot monitoring study as described by Courtney et al. [1]. While the study reported on the results for three languages, the paper at hand references the results for just one study involving the Kazakh medium-of-instruction. The first variable in that data includes is ‘Student_id’, which provides a unique anonymized student ID number for each student. The next six variables in the data vary between-schools: (1) ‘experiment’, for which each school is defined as either a pilot (experiment) or control school; ‘Region’, defining the region that each school is situated in the country; (3) School_name, the name assigned to each unique school; (4) ‘Language’ defining the medium-of-instruction used in each school; ‘School_type’ where schools are defined as either (i) small, (ii) gymnasium, (iii) lyceum, or (iv) common (common used as baseline in modelling).
Thereafter, the within-school variable, ‘Gender’ is given for which each child is defined as either male or female.
In addition, columns 9-92 pertain to the item-response data (for all elements, 0 = incorrect, 1 = correct) for each child on each item in the five tests.
2015 Test: Items OG1 to OG5.4 (columns 9-19; link items to 2016 are OG2.2A, OG2.3A, OG23A)
2016 Test: Items OG2.2 to OG11.2 (20-35; link items to 2017 are )
2017 Test: Items OG11.1 to K3.6 (36-47)
2018 Test: Items K4.1 to K3.3 (48-68)
2019 Test: Items X19K1 to K6 (69-92)

The data is filtered data. Only students who completed all five literacy assessments (2015 to 2019) were included in the data. Alternative approaches for dealing with missing data (attrition) in the analysis described in this article. Original data in Cyrillic used to showcase the R software’s capacity to manage and recode such data.
DOI: 10.17632/sxpdsszps8.5
title: Longitudinal Quasi-Experimental Assessment Data: Kazakhstan

description: The data in this paper pertain to those analyzed as part of a pilot monitoring study as described by Courtney et al. [1]. While the study reported on the results for three languages, the paper at hand references the results for just one study involving the Kazakh medium-of-instruction. The first variable in that data includes is (Excel column A) ‘Student_id’, which provides a unique anonymized student ID number for each student. The next six variables in the data vary between-schools: (B) ‘experiment’, for which each school is defined as either a pilot (experiment) or control school; (C) ‘Region’, defining the region that each school is situated in the country; (D) School_name, the name assigned to each unique school; (E) ‘Language’ defining the medium-of-instruction used in each school (Kazakh or Russian); (F) ‘School_type’ where schools are defined as either (i) small, (ii) gymnasium, (iii) lyceum, or (iv) common (common used as baseline in modelling, see R code for recodes into numeric). (G) School location follows with schools defined as either (i) city or (ii) rural.
Thereafter, the within-school variable, (H) ‘Gender’ is given for which each child is defined as either male or female.
In addition, columns I to CN pertain to the item-response data for the project (for all elements, 0 = incorrect, 1 = correct) for each child on each item in the five tests.
2015 Test: Items OG1 to OG5.4 (columns I-S; link items for 2015 are OG2.2A, OG2.3A, OG3A; link items for 2016 are OG2.2B, OG2.3B, OG3B, columns T, U, and W).
2016 Test: Items OG2.2 to OG11.2 (columns T-AI; link items for 2016 are OG11.1A and OG11.2A; link items for 2017 are OG11.1B and OG11.2B, columns AJ and AK).
2017 Test: Items OG11.1 to K3.6 (columns AJ-AU; link items for 2017 are K3.3A and K3.6A; link items for 2018 are K3.3B and K3.6B, columns BP and BO).
2018 Test: Items K4.1 to K3.3 (AV-BP; link items for 2018 are K10.2A and K13.4A; link item for 2019 are K10.2B and K13.4B, columns BU and CC).
2019 Test: Items X19K1 to K6 (BQ-CN).

References:
[1] Courtney, M. G. R., Rakhymbayeva, Z., Shilibekova, A., Ziyedenova, D., Soltangazina, S., Muratkyzy, A., Goodman, B., & Olzhayeva, A. (2022). Kazakh, Russian, and Uyghur child language literacy: The role of updated content of education on longitudinal growth trajectories in Kazakhstan. Studies in Educational Evaluation, 75(101189). doi:10.1016/j.stueduc.2022.101189
DOI: 10.17632/sxpdsszps8.6
title: Longitudinal Quasi-Experimental Assessment Data: Kazakhstan

description: The data in this paper pertain to those analyzed as part of a pilot monitoring study as described by Courtney et al. [1]. While the study reported on the results for three languages, the paper at hand references the results for just one study involving the Kazakh medium-of-instruction. The first variable in that data includes is (Excel column A) ‘Student_id’, which provides a unique anonymized student ID number for each student. The next six variables in the data vary between-schools: (B) ‘experiment’, for which each school is defined as either a pilot (experiment) or control school; (C) ‘Region’, defining the region that each school is situated in the country; (D) School_name, the name assigned to each unique school; (E) ‘Language’ defining the medium-of-instruction used in each school (Kazakh or Russian); (F) ‘School_type’ where schools are defined as either (i) small, (ii) gymnasium, (iii) lyceum, or (iv) common (common used as baseline in modelling, see R code for recodes into numeric). (G) School location follows with schools defined as either (i) city or (ii) rural. Thereafter, the within-school variable, (H) ‘Gender’ is given for which each child is defined as either male or female. In addition, columns I to CN pertain to the item-response data for the project (for all elements, 0 = incorrect, 1 = correct) for each child on each item in the five tests. 2015 Test: Items OG1 to OG5.4 (columns I-S; link items for 2015 are OG2.2A, OG2.3A, OG3A; link items for 2016 are OG2.2B, OG2.3B, OG3B, columns T, U, and W). 2016 Test: Items OG2.2 to OG11.2 (columns T-AI; link items for 2016 are OG11.1A and OG11.2A; link items for 2017 are OG11.1B and OG11.2B, columns AJ and AK). 2017 Test: Items OG11.1 to K3.6 (columns AJ-AU; link items for 2017 are K3.3A and K3.6A; link items for 2018 are K3.3B and K3.6B, columns BP and BO). 2018 Test: Items K4.1 to K3.3 (AV-BP; link items for 2018 are K10.2A and K13.4A; link item for 2019 are K10.2B and K13.4B, columns BU and CC). 2019 Test: Items X19K1 to K6 (BQ-CN). References: [1] Courtney, M. G. R., Rakhymbayeva, Z., Shilibekova, A., Ziyedenova, D., Soltangazina, S., Muratkyzy, A., Goodman, B., & Olzhayeva, A. (2022). Kazakh, Russian, and Uyghur child language literacy: The role of updated content of education on longitudinal growth trajectories in Kazakhstan. Studies in Educational Evaluation, 75(101189). doi:10.1016/j.stueduc.2022.101189
DOI: 10.17632/v5pbkj7yzb.1
title: Relative Responses of Trade in Goods and Services to Currency Devaluation: Evidence from Ethiopia

description: This dataset was used in the production of the research article entitled "Relative Responses of Trade in Goods and Services to Currency Devaluation: Evidence from Ethiopia." The dataset was compiled from online databases, particularly the WB, IMF, and UNCTAD databases.
DOI: 10.25582/data.2021-12.2403536/2092509
title: Data for EMSL Project 51241 from December 2021
DOI: 10.25582/data.2022-05.2510163/2092510
title: Data for EMSL Project 51241 from May 2022
DOI: 10.25582/data.2021-10.2344634/2092511
title: Data for EMSL Project 51241 from October 2021
DOI: 10.17632/styystf2nj.1
title: Antibiotic Prescription Patterns in Patients with Suspected Urinary Tract Infection in Ecuador

description: The data source for the variables included in this study was the Electronic Health Records (EHR) of patients from 14 outpatient centers (8 urban and 6 rural) of the first level of healthcare, and from one second-level hospital of the Ministry of Public Health (MOPH) in Ibarra, Ecuador during 2019. These centers have been using EHR since 2010. The information was manually extracted from the EHR between March 27, 2021 and June 27, 2021. The selection of acute UTI cases was based on the International Classification of Diseases Tenth Revision (ICD-10). Two peer reviewers autonomously extracted data, adhering to the specified inclusion criteria: UTI cases identified by ICD-10 codes N10: Acute pyelonephritis, N300: Acute cystitis, and N390: Urinary tract infection, site not specified; individuals aged 18 and above who had been diagnosed with acute UTI and received treatment within the MOPH system, whether as outpatients or inpatients; and patients under the care of family, emergency, and internal medicine physicians. Exclusions comprised pregnant patients, those managed by other healthcare subsystems, and chronic UTI diagnoses including N30.1: Interstitial cystitis (chronic), N30.2: Other chronic cystitis, N11.0: Nonobstructive reflux-associated chronic pyelonephritis, and N11.1: Chronic obstructive pyelonephritis. Upon completion of individual data extraction, a thorough comparison was conducted, leading to consensus decisions regarding the inclusion or exclusion of each patient.
DOI: 10.17632/styystf2nj.2
title: Antibiotic Prescription Patterns in Patients with Suspected Urinary Tract Infection in Ecuador

description: The data source for the variables included in this study was the Electronic Health Records (EHR) of patients from 14 outpatient centers (8 urban and 6 rural) of the first level of healthcare, and from one second-level hospital of the Ministry of Public Health (MOPH) in Ibarra, Ecuador during 2019. These centers have been using EHR since 2010. The information was manually extracted from the EHR between March 27, 2021 and June 27, 2021. The selection of acute UTI cases was based on the International Classification of Diseases Tenth Revision (ICD-10). Two peer reviewers autonomously extracted data, adhering to the specified inclusion criteria: UTI cases identified by ICD-10 codes N10: Acute pyelonephritis, N300: Acute cystitis, and N390: Urinary tract infection, site not specified; individuals aged 18 and above who had been diagnosed with acute UTI and received treatment within the MOPH system, whether as outpatients or inpatients; and patients under the care of family, emergency, and internal medicine physicians. Exclusions comprised pregnant patients, those managed by other healthcare subsystems, and chronic UTI diagnoses including N30.1: Interstitial cystitis (chronic), N30.2: Other chronic cystitis, N11.0: Nonobstructive reflux-associated chronic pyelonephritis, and N11.1: Chronic obstructive pyelonephritis. Upon completion of individual data extraction, a thorough comparison was conducted, leading to consensus decisions regarding the inclusion or exclusion of each patient. Page 1: All dataset Page 2: Variables and Definitions
DOI: 10.25582/data.2021-12.2403503/2092512
title: Data for EMSL Project 51241 from December 2021
DOI: 10.25582/data.2021-12.2403532/2092513
title: Data for EMSL Project 51241 from December 2021
DOI: 10.17632/fk83s6j4x2.1
title: Seasonal variation in great tit energy requirements: reallocation versus increased demand

description: Understanding how birds annually allocate energy to cope with changing environmental conditions and physiological states is a fundamental question in avian ecology. The two main hypotheses to explain annual patterns in energy use are "reallocation" and "increased demand". The reallocation hypothesis suggests equal energetic costs in winter and breeding seasons, while the increased demand suggests that energy demand should be highest during breeding. Under the standard aerobic capacity model of endothermy, birds are expected to adjust the mass and/or metabolic intensity of their bodies in ways that are consistent with expected cold- and/or activity-induced costs. Here, we look for metabolic signatures of reallocation versus increased demands in the energy requirements of a small, resident passerine of a temperate-zone (great tit, Parus major). To do so, we measured whole-body and mass-independent basal (BMR), summit (Msum), and field (FMR) metabolic rates during late winter and during the chick-rearing period (breeding). We also assessed whether, and to what extent, metabolic rates conform to the predictions of the aerobic capacity model of endothermy. We found that great tits showed no substantial differences in energy expenditure between winter and the breeding season, providing support for the reallocation hypothesis. Only mass-independent Msum showed seasonal variation, with significantly higher values (~4%) in winter compared to the breeding season. Our results also lend support to the predictions of the aerobic capacity model for the evolution of endothermy, as we found that whole-body BMR and Msum were positively related. We argue that both energy reallocation and the limited increase in mass-independent Msum are consistent with the relatively mild winter temperatures recorded during our study period. Our results confirm that both BMR and Msum are flexible traits that vary in ways that are consistent with expected cold- and/or activity-induced costs.
DOI: 10.17632/fk83s6j4x2.2
title: Great tits (Parus major) in a west European temperate forest show little seasonal variation in metabolic energy requirements

description: Dataset from Great tits (Parus major) in a west European temperate forest show little seasonal variation in metabolic energy requirements
DOI: 10.25582/data.2022-05.2509181/2092514
title: Data for EMSL Project 51241 from May 2022
DOI: 10.17632/mbcgnctsdn.1
title: Oral fibrolipoma: Clinical case

description: A lipoma is a benign mesenchymal tumor that typically does not commonly involve the oral cavity. However, when it does, the cheeks and tongue are the most commonly affected areas. It presents as a papular or tumoral lesion, typically yellowish in color, with superficial blood vessels. Lipomas are soft to the touch, and their base of attachment can be sessile or pedunculated. Treatment usually involves surgery, and histopathological examination can reveal various variants, including fibrolipoma, characterized by abundant mature adipocytes and bands of mature fibrous tissue. Here, we present the case of a 55-year-old male patient who sought oral medicine consultation at the School of Dentistry of UNPHU due to a non-painful lesion that was affecting oral hygiene and eating. Upon clinical examination, a rounded, yellowish, pedunculated tumoral lesion with superficial telangiectasias, measuring approximately 1 cm in diameter, was observed in the interdental papilla between teeth 36 and 37. Based on the clinical characteristics and patient-provided data, a provisional diagnosis of lipoma was made. An excisional biopsy was performed, and pre-surgical tests, including a complete blood count, yielded normal results. Surgery was successful, and the histopathological diagnosis was fibrolipoma. Postoperative follow-ups showed a normal healing pattern, and after 3 months, there was no evidence of lesion recurrence.
DOI: 10.25582/data.2022-10.2616065/2092515
title: Data for EMSL Project 51241 from October 2022
DOI: 10.17632/bdhdymnxsp.1
title: butterfly_urban_habitat_models

description: Data and code required to reproduce the models and graphical outputs presented in the research article: "Using butterfly survey data to model habitat associations in urban developments", JEJ Cooper et al., (2023)
DOI: 10.25582/data.2022-05.2509411/2092516
title: Data for EMSL Project 51241 from May 2022
DOI: 10.25582/data.2022-05.2509427/2092517
title: Data for EMSL Project 51241 from May 2022