scholarly journals The data and analysis underlying NIH’s decision to cap research support lacked rigor and transparency: a commentary

2017 ◽  
Author(s):  
A Cecile JW Janssens ◽  
Gary W Miller ◽  
K Venkat Narayan
Keyword(s):  
Scientific Output ◽  
National Institutes Of Health ◽  
Quality Data ◽  
Rigorous Analysis ◽  
Research Support ◽  
High Quality Data ◽  
Grant Support ◽  
Threshold Data ◽  
The Us

The US National Institutes of Health (NIH) recently announced that they would limit the number of grants per scientist and redistribute their funds across a larger group of researchers. The policy was withdrawn a month later after criticism from the scientific community. Even so, the basis of this defunct policy was flawed and it merits further examination. The amount of grant support would have been quantified using a new metric, the Grant Support Index (GSI), and limited to a maximum of 21 points, the equivalent of three R01 grants. This threshold was decided based upon analysis of a new metric of scientific output, the annual weighted Relative Citation Ratio, which showed a pattern of diminishing returns at higher values of the GSI. In this commentary, we discuss several concerns about the validity of the two metrics and the quality of the data that the NIH had used to set the grant threshold. These concerns would have warranted a re-analysis of new data to confirm the legitimacy of the GSI threshold. Data-driven policies that affect the careers of scientists should be justified by nothing less than a rigorous analysis of high-quality data.

scholarly journals The data and analysis underlying NIH’s decision to cap research support lacked rigor and transparency: a commentary

2017 ◽  
Author(s):  
A Cecile JW Janssens ◽  
Gary W Miller ◽  
K Venkat Narayan
Keyword(s):  
Scientific Output ◽  
National Institutes Of Health ◽  
Quality Data ◽  
Rigorous Analysis ◽  
Research Support ◽  
High Quality Data ◽  
Grant Support ◽  
Threshold Data ◽  
The Us

The US National Institutes of Health (NIH) recently announced that they would limit the number of grants per scientist and redistribute their funds across a larger group of researchers. The policy was withdrawn a month later after criticism from the scientific community. Even so, the basis of this defunct policy was flawed and it merits further examination. The amount of grant support would have been quantified using a new metric, the Grant Support Index (GSI), and limited to a maximum of 21 points, the equivalent of three R01 grants. This threshold was decided based upon analysis of a new metric of scientific output, the annual weighted Relative Citation Ratio, which showed a pattern of diminishing returns at higher values of the GSI. In this commentary, we discuss several concerns about the validity of the two metrics and the quality of the data that the NIH had used to set the grant threshold. These concerns would have warranted a re-analysis of new data to confirm the legitimacy of the GSI threshold. Data-driven policies that affect the careers of scientists should be justified by nothing less than a rigorous analysis of high-quality data.

scholarly journals Identification of models of nonlinear dynamic processes in mining on the basis of Volterra nuclei

2020 ◽  
Vol 201 ◽  
pp. 01028
Author(s):  
Natalia Morkun ◽  
Iryna Zavsiehdashnia ◽  
Oleksandra Serdiuk ◽  
Iryna Kasatkina
Keyword(s):  
Volterra Series ◽  
Quality Data ◽  
Weight Functions ◽  
Mineral Concentration ◽  
High Quality Data ◽  
Technological Processes ◽  
Process Formation ◽  
Nonlinear Dynamic Processes ◽  
Controlled Object

Solving the problem of improving efficiency of technological processes of mineral concentration is one of the essential for providing sustainability of mining enterprises. Currently, special attention is paid to optimization of technological processes in concentration of useful minerals. This approach calls for availability of high-quality data on the process, formation of corresponding databases and their subsequent processing to build adequate and efficient mathematical models of processes and systems. In order to improve quality of mathematical description of forming fractional characteristics of ore through applying technological aggregates in concentration, the authors suggest using power Volterra series that provide characteristics of a controlled object (its condition) as a sequence of multidimensional weight functions invariant to the type of an input signal – Volterra nuclei. Application of Volterra structures enables decreasing the modelling error to 0.039 under the root-mean-square error of 0.0594.

scholarly journals Evaluation and optimization of ICOS atmosphere station data as part of the labeling process

2021 ◽  
Vol 14 (1) ◽  
pp. 89-116
Author(s):  
Camille Yver-Kwok ◽  
Carole Philippon ◽  
Peter Bergamaschi ◽  
Tobias Biermann ◽  
Francescopiero Calzolari ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Scientific Data ◽  
Quality Data ◽  
Observation System ◽  
Quality Level ◽  
High Quality Data ◽  
Site Water ◽  
Quantitative Results ◽  
Greenhouse Gas Budget

Abstract. The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high-precision scientific data on the carbon cycle and the greenhouse gas budget. All stations have to undergo a rigorous assessment before being labeled, i.e., receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmosphere network through the 23 stations that were labeled between November 2017 and November 2019. We describe the labeling steps, as well as the quality controls, used to verify that the ICOS data (CO2, CH4, CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the greenhouse gas data, three to four calibration gases and two target gases are measured: one target two to three times a day, the other gases twice a month. The data are verified on a weekly basis, and tests on the station sampling lines are performed twice a year. From these high-quality data, we conclude that regular calibrations of the CO2, CH4 and CO analyzers used here (twice a month) are important in particular for carbon monoxide (CO) due to the analyzer's variability and that reducing the number of calibration injections (from four to three) in a calibration sequence is possible, saving gas and extending the calibration gas lifespan. We also show that currently, the on-site water vapor correction test does not deliver quantitative results possibly due to environmental factors. Thus the use of a drying system is strongly recommended. Finally, the mandatory regular intake line tests are shown to be useful in detecting artifacts and leaks, as shown here via three different examples at the stations.

scholarly journals Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4486 ◽  
Author(s):  
Mohan Li ◽  
Yanbin Sun ◽  
Yu Jiang ◽  
Zhihong Tian
Keyword(s):  
Low Cost ◽  
Real Life ◽  
Quality Data ◽  
Parameter Setting ◽  
Multiple Sources ◽  
High Quality ◽  
High Quality Data ◽  
Real Life Data ◽  
Sensor Cloud

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.

scholarly journals A Data Quality Control Program for Computer-Assisted Personal Interviews

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Janet E. Squires ◽  
Alison M. Hutchinson ◽  
Anne-Marie Bostrom ◽  
Kelly Deis ◽  
Peter G. Norton ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Control Program ◽  
Quality Data ◽  
Computer Assisted ◽  
Data Quality Control ◽  
Process Data ◽  
Quality Control Program ◽  
High Quality Data

Researchers strive to optimize data quality in order to ensure that study findings are valid and reliable. In this paper, we describe a data quality control program designed to maximize quality of survey data collected using computer-assisted personal interviews. The quality control program comprised three phases: (1) software development, (2) an interviewer quality control protocol, and (3) a data cleaning and processing protocol. To illustrate the value of the program, we assess its use in the Translating Research in Elder Care Study. We utilize data collected annually for two years from computer-assisted personal interviews with 3004 healthcare aides. Data quality was assessed using both survey and process data. Missing data and data errors were minimal. Mean and median values and standard deviations were within acceptable limits. Process data indicated that in only 3.4% and 4.0% of cases was the interviewer unable to conduct interviews in accordance with the details of the program. Interviewers’ perceptions of interview quality also significantly improved between Years 1 and 2. While this data quality control program was demanding in terms of time and resources, we found that the benefits clearly outweighed the effort required to achieve high-quality data.

scholarly journals A re-analysis of the 58 year mass-balance record of Storglaciären, Sweden

2005 ◽  
Vol 42 ◽  
pp. 389-394 ◽  
Author(s):  
Per Holmlund ◽  
Peter Jansson ◽  
Rickard Pettersson
Keyword(s):  
Time Series ◽  
Mass Balance ◽  
Measurement Methods ◽  
Summer Temperature ◽  
Quality Data ◽  
Glacier Mass Balance ◽  
Equilibrium Line Altitude ◽  
High Quality Data ◽  
Time Periods

AbstractThe use of glacier mass-balance records to assess the effects of glacier volume change from climate change requires high-quality data. The methods for measuring glacier mass balance have been developed in tandem with the measurements themselves, which implies that the quality of the data may change with time. We have investigated such effects on the mass-balance record of Storglaciären, Sweden, by re-analyzing the records using a better map base and applying successive maps over appropriate time periods. Our results show that errors <0.8 m occur during the first decades of the time series. Errors decrease with time, which is consistent with improvements in measurement methods. Comparison between the old and new datasets also shows improvements in the relationships between net balance, equilibrium-line altitude and summer temperature. A time-series analysis also indicates that the record does not contain longer-term (>10 year) oscillations. The pseudo-cyclic signal must thus be explained by factors other than cyclically occurring phenomena, although the record may still be too short to establish significant signals. We strongly recommend re-analysis of long mass-balance records in order to improve the mass-balance records used for other analyses.

scholarly journals Improvements in Data Quality, Integration and Reliability: New Developments at the IRIS DMC

2015 ◽  
Vol 40 ◽  
pp. 31-35 ◽  
Author(s):  
T. Ahern ◽  
R. Benson ◽  
R. Casey ◽  
C. Trabant ◽  
B. Weertman
Keyword(s):  
Data Access ◽  
Quality Data ◽  
New Developments ◽  
New Approach ◽  
Seismological Data ◽  
The Us ◽  
Access To Data ◽  
Seismic Networks ◽  
Access Services

Abstract. With the support of the US National Science Foundation (NSF) and on behalf of the international seismological community, IRIS developed a Data Management Center (DMC; Ahern, 2003) that has for decades acted as a primary resource for seismic networks wishing to make their data broadly available, as well as a significant point of access for researchers and monitoring agencies worldwide that wish to access high quality data for a variety of purposes. Recently IRIS has taken significant new steps to improve the quality of and access to the services of the IRIS DMC. This paper highlights some of the current new efforts being undertaken by IRIS. The primary topics include (1) steps to improve reliability and consistency of access to IRIS data resources, (2) a comprehensive new approach to assessing the quality of seismological and other data, (3) working with international partners to federate seismological data access services, and finally (4) extensions of the federated concept to extend data access to data from other geoscience domains.

Improving Data Quality Using Amazon Mechanical Turk Through Platform Setup

2021 ◽  
pp. 193896552110254
Author(s):  
Lu Lu ◽  
Nathan Neale ◽  
Nathaniel D. Line ◽  
Mark Bonn
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Amazon's Mechanical Turk ◽  
Associated Data

As the use of Amazon’s Mechanical Turk (MTurk) has increased among social science researchers, so, too, has research into the merits and drawbacks of the platform. However, while many endeavors have sought to address issues such as generalizability, the attentiveness of workers, and the quality of the associated data, there has been relatively less effort concentrated on integrating the various strategies that can be used to generate high-quality data using MTurk samples. Accordingly, the purpose of this research is twofold. First, existing studies are integrated into a set of strategies/best practices that can be used to maximize MTurk data quality. Second, focusing on task setup, selected platform-level strategies that have received relatively less attention in previous research are empirically tested to further enhance the contribution of the proposed best practices for MTurk usage.

scholarly journals Embedding Citizen Science in Research: Forms of engagement, scientific output and values for science, policy and society

2017 ◽  
Author(s):  
Dick Kasperowski ◽  
Christopher Kullenberg ◽  
Åsa Mäkitalo
Keyword(s):  
Citizen Science ◽  
Science Policy ◽  
Research Policy ◽  
Political Influence ◽  
Scientific Output ◽  
Future Research ◽  
Policy Makers ◽  
White House ◽  
The Us

This paper addresses emerging forms of Citizen Science (Citizen Science), and discusses their value for science, policy and society. It clarifies how the term Citizen Science is used and identifies different forms of Citizen Science. This is important, since with blurred distinctions there is a risk of both overrating and underestimating the value of Citizen Science and of misinterpreting what makes a significant contribution to scientific endeavour.The paper identifies three main forms of citizen science 1) Citizen Science as a research method, aiming for scientific output, 2) Citizen Science as public engagement, aiming to establish legitimacy for science and science policy in society, and, 3) Citizen Science as civic mobilization, aiming for legal or political influence in relation to specific issues. In terms of scientific output, the first form of Citizen Science exceeds the others in terms of scientific peer-reviewed articles. These projects build on strict protocols and rules for participation and rely on mass inclusion to secure the quality of contributions. Volunteers are invited to pursue very delimited tasks, defined by the scientists.The value of the three distinct forms of Citizen Science –for science, for policy and for society, is discussed to situate Citizen Science in relation to current policy initiatives in Europe and in the US. In quantitative terms the US, and particularly the NSF have so far taken a lead in allocating research funding to Citizen Science projects (primarily of the first form), however, the White House has recently issued a memorandum addressing societal and scientific challenges through citizen science covering all three forms discussed in this paper. As Citizen Science is currently being launched as a way to change the very landscape of science, important gaps in research are identified and policy recommendations are provided, in order for policy makers to be able to assess and anticipate the value of different forms of Citizen Science with regard to future research policy.

Sign in / Sign up

Export Citation Format

Share Document