The Experts below are selected from a list of 364020 Experts worldwide ranked by ideXlab platform
Tingru Cui - One of the best experts on this subject based on the ideXlab platform.
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validation of 4d components for measuring quality of the public health Data Collection Process elicitation study
Journal of Medical Internet Research, 2021Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:Background: Identification of the essential components of the quality of the Data Collection Process is the starting point for designing effective Data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health Data Collection Process has led to the formation of a preliminary 4D component framework, that is, Data Collection management, Data Collection personnel, Data Collection system, and Data Collection environment. It is necessary to empirically validate the framework for its use in future research and practice. Objective: This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework. Methods: Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research Processes included the development of an interview guide and Data Collection form, Data Collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative Data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework. Results: A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, Data Collection management, includes Data Collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, Data Collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, Data Collection personnel, includes the perception of Data Collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for Data Collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the Data Collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and Data Collection devices. Conclusions: This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health Data Collection Process after validation of psychometric properties and item reduction.
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application of a four dimensional framework to evaluate the quality of the hiv aids Data Collection Process in china
International Journal of Medical Informatics, 2021Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:Abstract Objective To qualitatively evaluate the quality of the Data Collection Process used by the Chinese national HIV/AIDS Data repository (CRIMS), using a four-dimensional (4D) framework. The Process is vital for the acquisition of high-quality Data for ending the HIV/AIDS epidemic in China. Methods The study was carried out in China from September 2014 to April 2015. Stratified convenient sampling was conducted to recruit 28 study participants including health administrators, public health professionals and clinicians. Data were collected through semi-structured interviews with the participants and from field observations in six hospitals. Content analysis was conducted following the 4D Framework. Results 61 percent of the facilitators and 74 percent of the barriers of the 4D Framework were confirmed in the CRIMS Data Collection Process. The CRIMS achieved better-quality Data Collection management. The perceived gaps primarily included: impractical Data Collection protocol and invalid quality assessment mechanism for Data Collection management; weak leadership and unsupportive organizational policy for Data Collection environment; poor communication and job fatigue for Data Collection personnel; and inflexibility and inaccessibility of Data Collection system. Areas for improvement included: engaging frontline staff in the design of Data Collection protocol, standardizing quality assurance procedures, strengthening leadership, recognizing Data collector’s contributions, and meeting end-users’ needs for the CRIMS. Conclusion The findings generated knowledge about the quality of the CRIMS Data Collection Process. The 4D Framework has potential as an evaluation tool for decision-makers on the improvement of the public health Data Collection Process.
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identification of the essential components of quality in the Data Collection Process for public health information systems
Health Informatics Journal, 2020Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:This study identifies essential components in the Data Collection Process for public health information systems based on appraisal and synthesis of the reported factors affecting this Process in the literature. Extant Process assessment instruments and studies of public health Data Collection from electronic Databases and the relevant institutional websites were reviewed and analyzed following a five-stage framework. Four dimensions covering 12 factors and 149 indicators were identified. The first dimension, Data Collection management, includes Data Collection system and quality assurance. The second dimension, Data collector, is described by staffing pattern, skill or competence, communication and attitude toward Data Collection. The third, information system, is assessed by function and technology support, integration of different Data Collection systems, and device. The fourth dimension, Data Collection environment, comprises training, leadership, and funding. With empirical testing and contextual analysis, these essential components can be further used to develop a framework for measuring the quality of the Data Collection Process for public health information systems.
Hong Chen - One of the best experts on this subject based on the ideXlab platform.
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validation of 4d components for measuring quality of the public health Data Collection Process elicitation study
Journal of Medical Internet Research, 2021Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:Background: Identification of the essential components of the quality of the Data Collection Process is the starting point for designing effective Data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health Data Collection Process has led to the formation of a preliminary 4D component framework, that is, Data Collection management, Data Collection personnel, Data Collection system, and Data Collection environment. It is necessary to empirically validate the framework for its use in future research and practice. Objective: This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework. Methods: Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research Processes included the development of an interview guide and Data Collection form, Data Collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative Data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework. Results: A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, Data Collection management, includes Data Collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, Data Collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, Data Collection personnel, includes the perception of Data Collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for Data Collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the Data Collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and Data Collection devices. Conclusions: This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health Data Collection Process after validation of psychometric properties and item reduction.
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application of a four dimensional framework to evaluate the quality of the hiv aids Data Collection Process in china
International Journal of Medical Informatics, 2021Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:Abstract Objective To qualitatively evaluate the quality of the Data Collection Process used by the Chinese national HIV/AIDS Data repository (CRIMS), using a four-dimensional (4D) framework. The Process is vital for the acquisition of high-quality Data for ending the HIV/AIDS epidemic in China. Methods The study was carried out in China from September 2014 to April 2015. Stratified convenient sampling was conducted to recruit 28 study participants including health administrators, public health professionals and clinicians. Data were collected through semi-structured interviews with the participants and from field observations in six hospitals. Content analysis was conducted following the 4D Framework. Results 61 percent of the facilitators and 74 percent of the barriers of the 4D Framework were confirmed in the CRIMS Data Collection Process. The CRIMS achieved better-quality Data Collection management. The perceived gaps primarily included: impractical Data Collection protocol and invalid quality assessment mechanism for Data Collection management; weak leadership and unsupportive organizational policy for Data Collection environment; poor communication and job fatigue for Data Collection personnel; and inflexibility and inaccessibility of Data Collection system. Areas for improvement included: engaging frontline staff in the design of Data Collection protocol, standardizing quality assurance procedures, strengthening leadership, recognizing Data collector’s contributions, and meeting end-users’ needs for the CRIMS. Conclusion The findings generated knowledge about the quality of the CRIMS Data Collection Process. The 4D Framework has potential as an evaluation tool for decision-makers on the improvement of the public health Data Collection Process.
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identification of the essential components of quality in the Data Collection Process for public health information systems
Health Informatics Journal, 2020Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:This study identifies essential components in the Data Collection Process for public health information systems based on appraisal and synthesis of the reported factors affecting this Process in the literature. Extant Process assessment instruments and studies of public health Data Collection from electronic Databases and the relevant institutional websites were reviewed and analyzed following a five-stage framework. Four dimensions covering 12 factors and 149 indicators were identified. The first dimension, Data Collection management, includes Data Collection system and quality assurance. The second dimension, Data collector, is described by staffing pattern, skill or competence, communication and attitude toward Data Collection. The third, information system, is assessed by function and technology support, integration of different Data Collection systems, and device. The fourth dimension, Data Collection environment, comprises training, leadership, and funding. With empirical testing and contextual analysis, these essential components can be further used to develop a framework for measuring the quality of the Data Collection Process for public health information systems.
Matthew Sorell - One of the best experts on this subject based on the ideXlab platform.
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Raspi Flat field Images - Camera 04
2019Co-Authors: Richard Matthews, Nickolas Falkner, Matthew SorellAbstract:Various single flat field images taken with different integrated lenses using a Raspberry Pi V2 camera at 30°C.The set contains 3669 flat field images per set in TIFF and JPEG format. Each image is approximattely16.2MB in size. Each JPEG image is 10.5MB. Each camera set is approximately 48GB in size.Images are captured in JPEG format with Bayer 10-bit raw Data appended to the end of the file. This is extracted using DCRAW. Full research methodology for obtaining these images can be read in DOI:10.1016/j.diin.2019.02.002File structure:Each compressed folder contains one camera's Data. This is then segregated into subfolders which contains six folders representing each discrete lens used in the Data Collection Process. This is combined with six pinhole lenses that were also used in the Data Collection Process. Each of these folders is divided into a JPEG folder which contains the raw output of the sensor and a TIFF folder which contains the DCRAW converted images. The TIFF folder is then broken down into the images and a seperate folder called Fingerprint which contains the 50 images used to construct a reference pattern for the camera with that discrete lens.The image is stored as a file name "BAYERYYYYMMDD-HHMMSS.tiff" where YYYY, MM, DD, HH, MM, SS represent standard ISO time stamp format.Example Tree for Raspi01/Raspi01.zip|-- Lens-01| |--JPEG| | |-- BAYER20170926-082159.jpg| | |-- BAYER20170926-082205.jpg| | | ...| | |-- BAYER20170926-084017.jpg| || |--TIFF| | | fingerprint| | | | -- BAYER20170926-083446.tiff| | | | -- BAYER20170926-083452.tiff| | | | ...| | | | -- BAYER20170926-084017.tiff| | || | |-- BAYER20170926-082159.tiff| | |-- BAYER20170926-082205.tiff| | | ...| | |-- BAYER20170926-083440.tiff||-- Lens-02|-- Lens-03|-- Lens-04|-- Lens-05|-- Lens-06|-- Pinhole-01|-- Pinhole-02|-- Pinhole-03|-- Pinhole-04|-- Pinhole-05|-- Pinhole-05
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Raspi Flat field Images - Camera 02
2019Co-Authors: Richard Matthews, Nickolas Falkner, Matthew SorellAbstract:Various single flat field images taken with different integrated lenses using a Raspberry Pi V2 camera at 30°C.The set contains 3669 flat field images per set in TIFF and JPEG format. Each image is approximattely16.2MB in size. Each JPEG image is 10.5MB. Each camera set is approximately 48GB in size.Images are captured in JPEG format with Bayer 10-bit raw Data appended to the end of the file. This is extracted using DCRAW. Full research methodology for obtaining these images can be read in DOI:10.1016/j.diin.2019.02.002File structure:Each compressed folder contains one camera's Data. This is then segregated into subfolders which contains six folders representing each discrete lens used in the Data Collection Process. This is combined with six pinhole lenses that were also used in the Data Collection Process. Each of these folders is divided into a JPEG folder which contains the raw output of the sensor and a TIFF folder which contains the DCRAW converted images. The TIFF folder is then broken down into the images and a seperate folder called Fingerprint which contains the 50 images used to construct a reference pattern for the camera with that discrete lens.The image is stored as a file name "BAYERYYYYMMDD-HHMMSS.tiff" where YYYY, MM, DD, HH, MM, SS represent standard ISO time stamp format.Example Tree for Raspi01/Raspi01.zip|-- Lens-01| |--JPEG| | |-- BAYER20170926-082159.jpg| | |-- BAYER20170926-082205.jpg| | | ...| | |-- BAYER20170926-084017.jpg| || |--TIFF| | | fingerprint| | | | -- BAYER20170926-083446.tiff| | | | -- BAYER20170926-083452.tiff| | | | ...| | | | -- BAYER20170926-084017.tiff| | || | |-- BAYER20170926-082159.tiff| | |-- BAYER20170926-082205.tiff| | | ...| | |-- BAYER20170926-083440.tiff||-- Lens-02|-- Lens-03|-- Lens-04|-- Lens-05|-- Lens-06|-- Pinhole-01|-- Pinhole-02|-- Pinhole-03|-- Pinhole-04|-- Pinhole-05|-- Pinhole-05
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Raspi Flat field Images - Camera 01
2019Co-Authors: Richard Matthews, Nickolas Falkner, Matthew SorellAbstract:Various single flat field images taken with different integrated lenses using a Raspberry Pi V2 camera at 30°C.The set contains 3669 flat field images per set in TIFF and JPEG format. Each image is approximattely16.2MB in size. Each JPEG image is 10.5MB. Each camera set is approximately 48GB in size.Images are captured in JPEG format with Bayer 10-bit raw Data appended to the end of the file. This is extracted using DCRAW. Full research methodology for obtaining these images can be read in DOI:10.1016/j.diin.2019.02.002File structure:Each compressed folder contains one camera's Data. This is then segregated into subfolders which contains six folders representing each discrete lens used in the Data Collection Process. This is combined with six pinhole lenses that were also used in the Data Collection Process. Each of these folders is divided into a JPEG folder which contains the raw output of the sensor and a TIFF folder which contains the DCRAW converted images. The TIFF folder is then broken down into the images and a seperate folder called Fingerprint which contains the 50 images used to construct a reference pattern for the camera with that discrete lens.The image is stored as a file name "BAYERYYYYMMDD-HHMMSS.tiff" where YYYY, MM, DD, HH, MM, SS represent standard ISO time stamp format.Example Tree for Raspi01/Raspi01.zip|-- Lens-01| |--JPEG| | |-- BAYER20170926-082159.jpg| | |-- BAYER20170926-082205.jpg| | | ...| | |-- BAYER20170926-084017.jpg| || |--TIFF| | | fingerprint| | | | -- BAYER20170926-083446.tiff| | | | -- BAYER20170926-083452.tiff| | | | ...| | | | -- BAYER20170926-084017.tiff| | || | |-- BAYER20170926-082159.tiff| | |-- BAYER20170926-082205.tiff| | | ...| | |-- BAYER20170926-083440.tiff||-- Lens-02|-- Lens-03|-- Lens-04|-- Lens-05|-- Lens-06|-- Pinhole-01|-- Pinhole-02|-- Pinhole-03|-- Pinhole-04|-- Pinhole-05|-- Pinhole-05
David Hailey - One of the best experts on this subject based on the ideXlab platform.
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validation of 4d components for measuring quality of the public health Data Collection Process elicitation study
Journal of Medical Internet Research, 2021Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:Background: Identification of the essential components of the quality of the Data Collection Process is the starting point for designing effective Data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health Data Collection Process has led to the formation of a preliminary 4D component framework, that is, Data Collection management, Data Collection personnel, Data Collection system, and Data Collection environment. It is necessary to empirically validate the framework for its use in future research and practice. Objective: This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework. Methods: Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research Processes included the development of an interview guide and Data Collection form, Data Collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative Data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework. Results: A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, Data Collection management, includes Data Collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, Data Collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, Data Collection personnel, includes the perception of Data Collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for Data Collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the Data Collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and Data Collection devices. Conclusions: This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health Data Collection Process after validation of psychometric properties and item reduction.
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application of a four dimensional framework to evaluate the quality of the hiv aids Data Collection Process in china
International Journal of Medical Informatics, 2021Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:Abstract Objective To qualitatively evaluate the quality of the Data Collection Process used by the Chinese national HIV/AIDS Data repository (CRIMS), using a four-dimensional (4D) framework. The Process is vital for the acquisition of high-quality Data for ending the HIV/AIDS epidemic in China. Methods The study was carried out in China from September 2014 to April 2015. Stratified convenient sampling was conducted to recruit 28 study participants including health administrators, public health professionals and clinicians. Data were collected through semi-structured interviews with the participants and from field observations in six hospitals. Content analysis was conducted following the 4D Framework. Results 61 percent of the facilitators and 74 percent of the barriers of the 4D Framework were confirmed in the CRIMS Data Collection Process. The CRIMS achieved better-quality Data Collection management. The perceived gaps primarily included: impractical Data Collection protocol and invalid quality assessment mechanism for Data Collection management; weak leadership and unsupportive organizational policy for Data Collection environment; poor communication and job fatigue for Data Collection personnel; and inflexibility and inaccessibility of Data Collection system. Areas for improvement included: engaging frontline staff in the design of Data Collection protocol, standardizing quality assurance procedures, strengthening leadership, recognizing Data collector’s contributions, and meeting end-users’ needs for the CRIMS. Conclusion The findings generated knowledge about the quality of the CRIMS Data Collection Process. The 4D Framework has potential as an evaluation tool for decision-makers on the improvement of the public health Data Collection Process.
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identification of the essential components of quality in the Data Collection Process for public health information systems
Health Informatics Journal, 2020Co-Authors: Hong Chen, David Hailey, Tingru CuiAbstract:This study identifies essential components in the Data Collection Process for public health information systems based on appraisal and synthesis of the reported factors affecting this Process in the literature. Extant Process assessment instruments and studies of public health Data Collection from electronic Databases and the relevant institutional websites were reviewed and analyzed following a five-stage framework. Four dimensions covering 12 factors and 149 indicators were identified. The first dimension, Data Collection management, includes Data Collection system and quality assurance. The second dimension, Data collector, is described by staffing pattern, skill or competence, communication and attitude toward Data Collection. The third, information system, is assessed by function and technology support, integration of different Data Collection systems, and device. The fourth dimension, Data Collection environment, comprises training, leadership, and funding. With empirical testing and contextual analysis, these essential components can be further used to develop a framework for measuring the quality of the Data Collection Process for public health information systems.
Richard L Allgire - One of the best experts on this subject based on the ideXlab platform.
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benchmark sediment monitoring program for illinois streams Data report for water years 1998 and 1999
2002Co-Authors: Richard L AllgireAbstract:The Benchmark Sediment Monitoring Program for Illinois Streams was initiated by the Illinois State Water Survey in 1981 to generate a long-term Database of suspended sediment concentrations and instantaneous sediment loads. The program is now part of the Water Survey’s Water and Atmospheric Resources Monitoring (WARM) Network, which monitors climate, soil moisture, surface water, groundwater, and sediment throughout Illinois. This report summarizes the suspended sediment Data collected for the program during Water Years 1998 and 1999. All the techniques used in the Data Collection Process and laboratory analyses are based on U.S. Geological Survey procedures and techniques. The report appendices present tables of instantaneous suspended sediment measurements, particle size analysis, sediment transport curves, and plots of instantaneous sediment concentrations for the period of record for the current monitoring stations.
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benchmark sediment monitoring program for illinois streams Data report for water years 1996 and 1997
2001Co-Authors: Richard L AllgireAbstract:The Benchmark Sediment Monitoring Program for Illinois Streams was initiated by the Illinois State Water Survey in 1981 to generate a long-term Database of suspended sediment transport. The program is now part of the Water Survey’s Water and Atmospheric Resources Monitoring (WARM) Network, which monitors the climate, soil moisture, surface water, groundwater, and sediment throughout Illinois. This report summarizes the suspended sediment Data collected for the program during Water Years 1996 and 1997. All the techniques used in the Data Collection Process and laboratory analyses are based on U.S. Geological Survey procedures and techniques. The report appendices present tables of instantaneous suspended sediment measurements, particle size analysis, sediment transport curves, and plots of instantaneous sediment concentrations for the period of record for the current monitoring stations.
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benchmark sediment monitoring program for illinois streams Data report for water years 1994 and 1995
2001Co-Authors: Richard L AllgireAbstract:The Benchmark Sediment Monitoring Program for Illinois Streams was initiated by the Illinois State Water Survey in 1981 to generate a long-term Database of suspended sediment transport. The program is now part of the Water Survey’s Water and Atmospheric Resources Monitoring (WARM) Network, which monitors the climate, soil moisture, surface water, ground water, and sediment throughout Illinois. This report summarizes the suspended sediment Data collected for the program during Water Years 1994 and 1995. All the techniques used in the Data Collection Process and laboratory analyses are based on U.S. Geological Survey procedures and techniques. The report appendices present tables of instantaneous suspended sediment measurements, particle size analysis, sediment transport curves, and plots of instantaneous sediment concentrations for the period of record for the current monitoring stations.
