scholarly journals Expanding the Chemical Information Science gateway

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 294
Author(s):  
José L. Medina-Franco
Keyword(s):  
Information Science ◽  
Open Science ◽  
Quality Data ◽  
Bibliographic Databases ◽  
Chemical Information ◽  
High Quality ◽  
High Quality Data ◽  
New Members ◽  
Science Gateway ◽  
Unique Model

As chemical information evolves, impacting many chemistry areas, effective ways to disseminate results by the scientific community are also changing. Thus, publication schemes adapt to meet the needs of researchers across disciplines to share high-quality data, information, and knowledge. Since 2015, the F1000Research Chemical Information Science (CIS) gateway has offered an open and unique model to disseminate science at the interface of chemoinformatics, bioinformatics, and several other informatic-related disciplines. In response to the evolution of chemical information science, the F1000Research CIS gateway has incorporated new members to the advisory board. It is also reinforcing and expanding the gateway areas with a particular focus on machine learning and metabolomics. The range of available article types, availability of data, exposure within complementary multidisciplinary F1000Research gateways, and indexing in major bibliographic databases increases the visibility of all contributions. As part of progressing open science in this field, we look forward to your high-quality contributions to the CIS gateway.

scholarly journals Expanding the chemical information science gateway

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1764 ◽  
Author(s):  
Jürgen Bajorath
Keyword(s):  
Biological Activity ◽  
Chemical Structure ◽  
Information Science ◽  
Open Science ◽  
Chemical Information ◽  
High Quality ◽  
Activity Data ◽  
Science Gateway ◽  
Interdisciplinary Field ◽  
Scientific Environment

Broadly defined, chemical information science (CIS) covers chemical structure and data analysis including biological activity data as well as processing, organization, and retrieval of any form of chemical information. The CIS Gateway (CISG) of F1000Research was created to communicate research involving the entire spectrum of chemical information, including chem(o)informatics. CISG provides a forum for high-quality publications and a meaningful alternative to conventional journals. This gateway is supported by leading experts in the field recognizing the need for open science and a flexible publication platform enabling off-the-beaten path contributions. This editorial aims to further rationalize the scope of CISG, position it within its scientific environment, and open it up to a wider audience. Chemical information science is an interdisciplinary field with high potential to interface with experimental work.

scholarly journals Repositioning the Chemical Information Science Gateway

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 976 ◽  
Author(s):  
Jürgen Bajorath
Keyword(s):  
Open Access ◽  
Peer Review ◽  
Information Science ◽  
Open Science ◽  
Chemical Information ◽  
High Quality ◽  
Science Gateway ◽  
The Past

The Chemical Information Science Gateway (CISG) of F1000Research was originally conceptualized as a forum for high-quality publications in chemical information science (CIS) including chemoinformatics. Adding a publication venue with open access and open peer review to the CIS field was a prime motivation for the introduction of CISG, aiming to support open science in this area. Herein, the CISG concept is revisited and the development of the gateway over the past four years is reviewed. In addition, opportunities are discussed to better position CISG within the publication spectrum of F1000Research and further increase its visibility and attractiveness for scientific contributions.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

scholarly journals Fooled by Performance Randomness: Overrewarding Luck

2019 ◽  
Vol 101 (4) ◽  
pp. 658-666 ◽  
Author(s):  
Romain Gauriot ◽  
Lionel Page
Keyword(s):  
Causal Effect ◽  
Experimental Situation ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Economic Organizations ◽  
Quasi Experimental ◽  
Similar Location ◽  
Do So

We provide evidence of a violation of the informativeness principle whereby lucky successes are overly rewarded. We isolate a quasi-experimental situation where the success of an agent is as good as random. To do so, we use high-quality data on football (soccer) matches and select shots on goal that landed on the goal posts. Using nonscoring shots, taken from a similar location on the pitch, as counterfactuals to scoring shots, we estimate the causal effect of a lucky success (goal) on the evaluation of the player's performance. We find clear evidence that luck is overly influencing managers' decisions and evaluators' ratings. Our results suggest that this phenomenon is likely to be widespread in economic organizations.

scholarly journals R factors in Rietveld analysis: How good is good enough?

2006 ◽  
Vol 21 (1) ◽  
pp. 67-70 ◽  
Author(s):  
Brian H. Toby
Keyword(s):  
Rietveld Analysis ◽  
Poor Quality ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Poor Quality Data ◽  
Error Index ◽  
R Factors ◽  
Very High

The definitions for important Rietveld error indices are defined and discussed. It is shown that while smaller error index values indicate a better fit of a model to the data, wrong models with poor quality data may exhibit smaller values error index values than some superb models with very high quality data.

scholarly journals AGRIBALYSE®, the French LCI Database for agricultural products: high quality data for producers and environmental labelling

OCL ◽  
2014 ◽  
Vol 22 (1) ◽  
pp. D104 ◽  
Author(s):  
Vincent Colomb ◽  
Samy Ait Amar ◽  
Claudine Basset Mens ◽  
Armelle Gac ◽  
Gérard Gaillard ◽  
...  
Keyword(s):  
Agricultural Products ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Environmental Labelling

scholarly journals A Study on Pattern Recognition with the Histograms of Oriented Gradients in Distorted and Noisy Images

2020 ◽  
Vol 26 (4) ◽  
pp. 454-478
Author(s):  
Andrzej Bukała ◽  
Michał Koziarski ◽  
Bogusław Cyganek ◽  
Osman Koç ◽  
Alperen Kara
Keyword(s):  
Pattern Recognition ◽  
Negative Impact ◽  
Quality Data ◽  
High Quality ◽  
Denoising Method ◽  
High Quality Data ◽  
Histograms Of Oriented Gradients ◽  
Different Types ◽  
Simple Implementation ◽  
Salt And Pepper

Histograms of oriented gradients (HOG) are still one of the most frequently used low-level features for pattern recognition in images. Despite their great popularity and simple implementation performance of the HOG features almost always has been measured on relatively high quality data which are far from real conditions. To fill this gap we experimentally evaluate their performance in the more realistic conditions, based on images affected by different types of noise, such as Gaussian, quantization, and salt-and-pepper, as well on images distorted by occlusions. Different noise scenarios were tested such anti-distortions during training as well as application of a proper denoising method in the recognition stage. As underpinned with experimental results, the negative impact of distortions and noise on object recognition with HOG features can be significantly reduced by employment of a proper denoising strategy.

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