Best practice data life cycle approaches for the life sciences
- Griffin, Philippa, Khadake, Jyoti, LeMay, Kate, Lewis, Suzanna, Orchard, Sandra, Pask, Andrew, Pope, Bernard, Roessner, Ute, Russell, Keith, Seemann, Torsten, Treloar, Andrew, Tyagi, Sonika, Christiansen, Jeffrey, Dayalan, Saravanan, Gladman, Simon, Hangartner, Sandra, Hayden, Helen, Ho, William, Keeble-Gagnère, Gabriel, Korhonen, Pasi, Neish, Peter, Prestes, Priscilla, Richardson, Mark, Watson-Haigh, Nathan, Wyres, Kelly, Young, Neil, Schneider, Maria
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
- Gomez, Rapson, Vance, Alasdair, Gomez, Andre
- Authors: Gomez, Rapson , Vance, Alasdair , Gomez, Andre
- Date: 2012
- Type: Text , Journal article
- Relation: Psychological Assessment Vol. 24, no. 1 (2012), p. 1-10
- Full Text: false
- Reviewed:
- Description: In the study, the authors examined the measurement (configural, factor loadings, thresholds, and error variances) and structural (factor variances, covariances, and mean scores) invariance of the Children's Depression Inventory (CDI; Kovacs, 1992) across ratings provided by clinic-referred children and adolescents with (N = 383) and without (N = 412) depressive disorders. Multiple-group confirmatory factor analysis of the Craighead, Smucker, Craighead, and Ilardi (1998) CDI model supported full measurement invariance and invariance for structural variances and covariances. Invariance for thresholds was also supported by multiple indicators multiple causes (MIMIC) procedures that controlled for the effects of age; sex; and the presence or absence of anxiety disorders, attention-deficit/hyperactivity disorder, and oppositional defiant/conduct disorders. The MIMIC analyses showed that for latent mean scores, the group with depressive disorders had higher scores, with at least medium effect sizes, for Self-Deprecation and Biological Dysregulation. The theoretical, psychometric, and clinical implications of the findings are discussed. © 2011 American Psychological Association.
Psychometric properties of patient-reported outcome measures for hip arthroscopic surgery
- Kemp, Joanne, Collins, Natalie, Roos, Ewa, Crossley, Kay
- Authors: Kemp, Joanne , Collins, Natalie , Roos, Ewa , Crossley, Kay
- Date: 2013
- Type: Text , Journal article
- Relation: American Journal of Sports Medicine Vol. 41, no. 9 (2013), p. 2065-2073
- Full Text: false
- Reviewed:
- Description: Background: Patient-reported outcomes (PROs) are considered the gold standard when evaluating outcomes in a surgical population. While the psychometric properties of some PROs have been tested, the properties of newer PROs in patients undergoing hip arthroscopic surgery remain somewhat unknown. Purpose: To evaluate the reliability, validity, responsiveness, and interpretability of 5 PROs (Copenhagen Hip and Groin Outcome Score [HAGOS], Hip Disability and Osteoarthritis Outcome Score [HOOS], Hip Outcome Score [HOS], International Hip Outcome Tool [iHOT-33], and Modified Harris Hip Score [MHHS]) in a population undergoing hip arthroscopic surgery and also to provide a recommendation of the best PROs in patients undergoing hip arthroscopic surgery. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Study participants were adults (mean age, 37 ± 11 years) who had undergone hip arthroscopic surgery 12 to 24 months previously and pain-free, healthy age-matched controls (mean age, 35 ± 11 years). Baseline characteristics including age, height, weight, waist girth, physical activity, and occupation were collected for both groups. The hip arthroscopic surgery group completed the 5 PRO questionnaires on 3 occasions, while the healthy control group completed the PRO questionnaires on 1 occasion. The reliability (test-retest reliability [intraclass correlation coefficient, or ICC] and minimal detectable change [MDC]), validity (construct validity, ability to detect a difference between groups, acceptability including floor and ceiling effects), responsiveness, and interpretability (minimal important change [MIC]) of each measure were calculated. Results: The test-retest reliability of PROs was excellent (ICC, 0.91-0.97), with an MDC of<20%. The HOOS, HAGOS, and iHOT- 33 had acceptable content validity. All PROs demonstrated construct validity and were able to detect a difference between the hip arthroscopic surgery and control groups. No measures demonstrated a floor effect; however, the MHHS and subscales relating to activities of daily living of the HOOS, HOS, and HAGOS demonstrated a ceiling effect. The HOOS, iHOT-33, and MHHS demonstrated adequate responsiveness, and the MIC for all measures was<11 points of a possible 100 points. Conclusion: The PROs of the HOOS and iHOT-33 demonstrate psychometric properties that may enable researchers and clinicians to use them with confidence in a population undergoing hip arthroscopic surgery. The psychometric properties of the MHHS, HOS, and some subscales of the HAGOS are reduced, and these PROs may be less valuable in this group. © 2013 The Author(s). National Health and Medical Research Council (Australia) Health Professional Research Training (Postdoctoral) Fellowship (No. 628918).
- «
- ‹
- 1
- ›
- »