cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5099617
title: Calcular e apresentar tamanhos do efeito em trabalhos científicos (2): Guia para reportar a força das relações [Calculating and reporting effect sizes on scientific papers (2): Guide to report the strength of relationships].

description: In the first issue of the Portuguese Journal of Behavioral and Social Research, it was described the importance of calculating, indicating and interpreting the effect sizes for the differences between means of two groups. The PJBSR intends to continue to remind of the importance of reporting effect sizes for other statistical tests. The magnitude of the strength of the relationships was not indicated in the previous paper, and it may not be known that correlation is an effect size. Thus, this paper intends to provide some guidelines to the authors on the procedures for calculating the Pearson correlation coefficient and some correlation coefficients for special data (Spearman Rho, Kendall’s Tau, Point-biserial and biserial, Phi, Cramér’s <em>V</em>, and Eta).<br>For this purpose, the formulas, steps in the SPSS (Statistical Package for the Social Sciences), assumptions and precautions, classification of values and their interpretation will be presented. Since SPSS does not compute all the mentioned coefficients, five spreadsheets (3 ways of comparing correlations, point-biserial and biserial, and correction of correlations for samples &lt; 60) were included in the article supplements.<br>
DOI: 10.6084/m9.figshare.5099617.v2
title: Calcular e apresentar tamanhos do efeito em trabalhos científicos (2): Guia para reportar a força das relações [Calculating and reporting effect sizes on scientific papers (2): Guide to report the strength of relationships].

description: In the first issue of the Portuguese Journal of Behavioral and Social Research, it was described the importance of calculating, indicating and interpreting the effect sizes for the differences between means of two groups. The PJBSR intends to continue to remind of the importance of reporting effect sizes for other statistical tests. The magnitude of the strength of the relationships was not indicated in the previous paper, and it may not be known that correlation is an effect size. Thus, this paper intends to provide some guidelines to the authors on the procedures for calculating the Pearson correlation coefficient and some correlation coefficients for special data (Spearman Rho, Kendall’s Tau, Point-biserial and biserial, Phi, Cramér’s <em>V</em>, and Eta).<br>For this purpose, the formulas, steps in the SPSS (Statistical Package for the Social Sciences), assumptions and precautions, classification of values and their interpretation will be presented. Since SPSS does not compute all the mentioned coefficients, five spreadsheets (3 ways of comparing correlations, point-biserial and biserial, and correction of correlations for samples &lt; 60) were included in the article supplements.<br>
DOI: 10.6084/m9.figshare.5099536
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099536.v1
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099536.v2
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099485
title: All Simulation Results (.csv) from Asymmetric competition impacts evolutionary rescue in a changing environment

description: This file contains all of the simulation results, including the parameter values and extinction generations for every species in every simulation run.
DOI: 10.6084/m9.figshare.5099536.v3
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099485.v1
title: All Simulation Results (.csv) from Asymmetric competition impacts evolutionary rescue in a changing environment

description: This file contains all of the simulation results, including the parameter values and extinction generations for every species in every simulation run.
DOI: 10.6084/m9.figshare.5099467
title: Mathematica File: Example Simulation Code (.nb) from Asymmetric competition impacts evolutionary rescue in a changing environment

description: This file contains example code to run a simulation, analyze the data from simulations, and find ESS trait values and population sizes.
DOI: 10.6084/m9.figshare.5099536.v4
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099536.v5
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099536.v6
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099536.v8
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099536.v7
title: Fatal Police Shootings (2013-2016) & County Level Data

description: This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016. Other variables are included from the American Community Survey (ACS), the FBI's Uniform Crime Reports (UCR), the Law Enforcement Management and Administrative Statistics (LEMAS), Stanford Education Data Archive (SEDA), the US Census, and the Local Area Unemployment Statistics (LAUS) provided by the Bureau of Labor Statistics (BLS). <br>The base file is the county-level data. The two files labelled "tenk" consist of regions with at least 10,000 residents. Counties with fewer than 10,000 residents are combined with adjacent counties, ranked by population in ascending order, so that counties with fewer than 10,000 residents are iteratively combined with adjacent neighbors with the fewest residents until they reach the threshold of 10,000 residents. The same algorithm is used to produce regions with 100,000 and 1 million residents. The final two files are state-level aggregations.
DOI: 10.6084/m9.figshare.5099272
title: Apéndices del estudio 'Colocaciones léxicas utilizadas en métodos de español para extranjeros'

description: These files contain data from a corpus linguistics analysis.
DOI: 10.6084/m9.figshare.5099317
title: Comparing a lemma list with a frequency wordlist from a reference corpus

description: A comparison between a lemma list derived from a Spanish as a Foreign Languaje textbook and a wordlist. The textbook is called 'Dicho y hecho' (Gonzalez C. et al. (2014) <i>Dicho y hecho (tomos 5 y 6). Español como lengua extranjera. </i>Centro de Enseñanza para Extranjeros-Universidad Nacional Autónoma de México. México.) and the wordlist was created from the 'Corpus del español' (Davies, M. (2002-) Corpus del Español: 100 million words, 1200s-1900s. Available online at http://www.corpusdelespanol.org.)
DOI: 10.6084/m9.figshare.5099272.v1
title: AI.txt

description: This file contain data from a corpus linguistics analysis.
DOI: 10.6084/m9.figshare.5099272.v3
title: Apéndices del estudio 'Colocaciones léxicas utilizadas en métodos de español para extranjeros'

description: These files contain data from a corpus linguistics analysis.
DOI: 10.6084/m9.figshare.5099272.v2
title: A Corpus-Based Analysis of Lexical Collocations used in Spanish as a Foreign Languaje Textbooks

description: These files contain data from a corpus linguistics analysis.
DOI: 10.6084/m9.figshare.5098201
title: Repeated annual influenza vaccination and vaccine effectiveness: review of evidence

description: <b>Introduction</b>: Studies in the 1970s and 1980s signaled concern that repeated influenza vaccination could affect vaccine protection. The antigenic distance hypothesis provided a theoretical framework to explain variability in repeat vaccination effects based on antigenic similarity between successive vaccine components and the epidemic strain. <b>Areas covered</b>: A meta-analysis of vaccine effectiveness studies from 2010–11 through 2014–15 shows substantial heterogeneity in repeat vaccination effects within and between seasons and subtypes. When negative effects were observed, they were most pronounced for H3N2, especially in 2014–15 when vaccine components were unchanged and antigenically distinct from the epidemic strain. Studies of repeated vaccination across multiple seasons suggest that vaccine effectiveness may be influenced by more than one prior season. In immunogenicity studies, repeated vaccination blunts the hemagglutinin antibody response, particularly for H3N2. <b>Expert commentary</b>: Substantial heterogeneity in repeated vaccination effects is not surprising given the variation in study populations and seasons, and the variable effects of antigenic distance and immunological landscape in different age groups and populations. Caution is required in the interpretation of pooled results across multiple seasons, since this can mask important variation in repeat vaccination effects between seasons. Multi-season clinical studies are needed to understand repeat vaccination effects and guide recommendations.
DOI: 10.6084/m9.figshare.5097883
title: Management of allergic rhinitis in the era of effective over-the-counter treatments

description: Allergic rhinitis (AR) may be regarded as a trivial issue unworthy of the doctor’s time, and with the availability of many different over-the-counter (OTC) treatments, up to two thirds of patients self-manage AR before seeking medical care. Yet, AR can have a significant impact on health-related quality of life and is associated with a greater detriment to work productivity than other chronic diseases such as diabetes and hypertension. For many patients, the impact on quality of life is greater than suggested by reported symptoms and should also be a focus of treatment. Although many patients can effectively manage AR symptoms independently, a significant percentage will need direction from a physician to obtain optimal results. The availability of several different classes of treatment – including decongestants, sedating and non-sedating antihistamines, and more recently intranasal corticosteroids (INS) – has increased the complexity of self-management, leaving patients confused about the best approach to treatment. Treatment guidelines universally classify INS as the most effective medical agents available for use in the OTC and primary care settings. Many patients are unaware that INS are available OTC and that they are more effective than other therapies. Patients may have negative perceptions about the safety of INS and may have experienced unpleasant taste, scent, and feel with nasal sprays. Unless a patient volunteers the information, healthcare professionals (HCPs) may be unaware that the patient has significant AR and is using one or more OTC AR therapies. To address this gap in communication, HCPs must be proactive in identifying, assessing, and advising patients with AR, including best strategies to assess allergen trigger symptoms, which treatments are appropriate, and when and how to use them. Proper use of delivery devices is especially important. This article reviews the primary care management of AR in the context of the availability of effective OTC medicines.
DOI: 10.6084/m9.figshare.5097880
title: Spinal racemose cysticercosis: case report and review

description: Cysticercosis is a common helminthic infection worldwide, endemic in Central and South America, sub-Saharan Africa, and Southeast Asia. Neurocysticercosis typically involves brain parenchyma, subarachnoid space, and the ventricular system. Although the spinal cord is frequently involved in patients with parenchymal neurocysticercosis, isolated spinal involvement is rare, occurring in only 1–3% of patients. We report a case of racemose spinal neurocysticercosis with brain parenchyma involvement in a 49-year-old Mexican man, who presented with unsteady gait and bilateral arm numbness and weakness. Magnetic resonance imaging revealed multiseptated cystic lesions in the upper cervical spinal canal and nonenhancing intradural extramedullary cystic lesions in the thoracic spine. The patient underwent sub-occipital craniectomy with decompression, followed by a course of steroids and albendazole. Pathology confirmed the diagnosis of neurocysticercosis, and the patient’s symptoms resolved after treatment. We include discussion on the symptoms, diagnosis, and treatment of neurocysticercosis with a focus on the spinal form.