Reproducibility is a cornerstone of science, but non-reproducible findings can also drive innovation and paradigm shifts. Failed replication does not necessarily mean the original result was false, and successful replication does not prove the result is true. Contextual factors and hidden moderators can influence reproducibility. Both discovery and replication are important for scientific progress, and rewarding replications can help balance these goals. While some non-reproducibility is essential, detrimental non-reproducibility should be avoided through good design, reporting and avoidance of misconduct.
Scientific research in a number of fields is in a state of crisis due to the discovery that many published results are non-reproducible, and applied statistics has been assigned a substantial share of the blame. Proposed solutions range from requiring independent statistical review of results for major journals to abolishing the use of certain methods entirely.
Lennox argues that the problem does not lie with statistical methods, but rather from misleading training for non-statisticians. The talk is intended to establish that statistics is not just a set of numerical procedures, but rather a distinctive way of thinking about and solving problems. Real-world examples demonstrate the pitfalls of "procedural" statistics, and that non-statisticians can be successful by approaching statistical challenges in the same way that they do problems in their field of expertise and by leveraging the statistical expertise available at the laboratory as necessary.
How to combine results from randomised clinical trials on the additive scale with real world data to provide predictions on the clinically relevant scale for individual patients
Towards Understanding SE Experiments Replication (ESEM'13 Keynote)Natalia Juristo
To consolidate a body of knowledge built upon evidence, experimental results have to be extensively verified. Experiments need replication at other times and under other conditions before they can produce an established piece of knowledge. Several replications need to be run to strengthen the evidence.
Most SE experiments have not been replicated. If an experiment is not replicated, there is no way to distinguish whether results were produced by chance (the observed event occurred accidentally), results are artifactual (the event occurred because of the experimental configuration but does not exist in reality) or results conform to a pattern existing in reality.
The immaturity of experimental SE knowledge has been an obstacle to replication. Context differences usually oblige SE experimenters to adapt experiments for replication. As key experimental conditions are yet unknown, slight changes in replications have led to differences in the results that prevent verification.
There are still many uncertainties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experimenters, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment?
The aim of replication is to verify results, but different types of replication serve special verification purposes and afford different degrees of change. Each replication type helps to discover particular experimental conditions that might influence the results. We need to learn which types of replications are feasible in SE as well as the acceptable changes for each type and the level of verification provided.
Reproducibility, argument and data in translational medicineTim Clark
Failures in reproducibility and robustness of scientific findings are explored from statistical, historical, and argumentation theory perspectives. The impact of false positives in the literature is connected to failures in T1 and T2 biomedical translation, and is shown to have a significant impact on the costs of therapeutic development and availability of needed treatments to the public. Technological and social approaches to resolve these issues are presented. "Reproducibility" initiatives are critiqued as unsustainable and non-authoritative; improved requirements and methods for scientific communication of findings including data, methods and material are supported as the best approaches for improved reproducibility.
Scientific research in a number of fields is in a state of crisis due to the discovery that many published results are non-reproducible, and applied statistics has been assigned a substantial share of the blame. Proposed solutions range from requiring independent statistical review of results for major journals to abolishing the use of certain methods entirely.
Lennox argues that the problem does not lie with statistical methods, but rather from misleading training for non-statisticians. The talk is intended to establish that statistics is not just a set of numerical procedures, but rather a distinctive way of thinking about and solving problems. Real-world examples demonstrate the pitfalls of "procedural" statistics, and that non-statisticians can be successful by approaching statistical challenges in the same way that they do problems in their field of expertise and by leveraging the statistical expertise available at the laboratory as necessary.
How to combine results from randomised clinical trials on the additive scale with real world data to provide predictions on the clinically relevant scale for individual patients
Towards Understanding SE Experiments Replication (ESEM'13 Keynote)Natalia Juristo
To consolidate a body of knowledge built upon evidence, experimental results have to be extensively verified. Experiments need replication at other times and under other conditions before they can produce an established piece of knowledge. Several replications need to be run to strengthen the evidence.
Most SE experiments have not been replicated. If an experiment is not replicated, there is no way to distinguish whether results were produced by chance (the observed event occurred accidentally), results are artifactual (the event occurred because of the experimental configuration but does not exist in reality) or results conform to a pattern existing in reality.
The immaturity of experimental SE knowledge has been an obstacle to replication. Context differences usually oblige SE experimenters to adapt experiments for replication. As key experimental conditions are yet unknown, slight changes in replications have led to differences in the results that prevent verification.
There are still many uncertainties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experimenters, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment?
The aim of replication is to verify results, but different types of replication serve special verification purposes and afford different degrees of change. Each replication type helps to discover particular experimental conditions that might influence the results. We need to learn which types of replications are feasible in SE as well as the acceptable changes for each type and the level of verification provided.
Reproducibility, argument and data in translational medicineTim Clark
Failures in reproducibility and robustness of scientific findings are explored from statistical, historical, and argumentation theory perspectives. The impact of false positives in the literature is connected to failures in T1 and T2 biomedical translation, and is shown to have a significant impact on the costs of therapeutic development and availability of needed treatments to the public. Technological and social approaches to resolve these issues are presented. "Reproducibility" initiatives are critiqued as unsustainable and non-authoritative; improved requirements and methods for scientific communication of findings including data, methods and material are supported as the best approaches for improved reproducibility.
Data collection methods to improve reproducibilityDigital Science
"Reproducibility, data collection, and laboratory management technologies" - Louis Culot, CEO of Biodata
Slides from Shaking It Up: Challenges and Solutions in Scholarly Information Management, San Francisco, April 22, 2015
There’s More Than One Way to Conduct a Replication StudyBey.docxrhetttrevannion
There’s More Than One Way to Conduct a Replication Study:
Beyond Statistical Significance
Samantha F. Anderson and Scott E. Maxwell
University of Notre Dame
As the field of psychology struggles to trust published findings, replication research has begun to become
more of a priority to both scientists and journals. With this increasing emphasis placed on reproducibility,
it is essential that replication studies be capable of advancing the field. However, we argue that many
researchers have been only narrowly interpreting the meaning of replication, with studies being designed
with a simple statistically significant or nonsignificant results framework in mind. Although this
interpretation may be desirable in some cases, we develop a variety of additional “replication goals” that
researchers could consider when planning studies. Even if researchers are aware of these goals, we show
that they are rarely used in practice—as results are typically analyzed in a manner only appropriate to a
simple significance test. We discuss each goal conceptually, explain appropriate analysis procedures, and
provide 1 or more examples to illustrate these analyses in practice. We hope that these various goals will
allow researchers to develop a more nuanced understanding of replication that can be flexible enough to
answer the various questions that researchers might seek to understand.
Keywords: replication, data analysis, confidence interval, effect size, equivalence test
Replication, a once largely ignored premise, has recently be-
come a defining precept for the future of psychology. Reproduc-
ibility has been referred to as the “cornerstone” (Simons, 2014, p.
76) and “Supreme Court” (Collins, 1985, p. 19) of science, and as
“the best and possibly the only believable evidence for the reli-
ability of an effect” (Simons, 2014, p. 76). In fact, “findings that
do not replicate are worse than fairy tales” (Wagenmakers, Wet-
zels, Borsboom, van der Maas, & Kievit, 2012, p. 633).
The idea of replication is not new. Even prior to Sir Ronald
Fisher and the advent of modern experimental design (circa 1935),
the field of agriculture used replication to assess accuracy and
reliability (Yates, 1964). In fact, Fisher himself emphasized the
importance of replication, believing that experimental findings are
only established if “a properly designed experiment rarely fails to
give . . . significance” (Fisher, 1926, p. 504). In 1969, Tukey noted
that “confirmation comes from repetition” and that ignoring the
need for replication would “lend[s] itself to failure and more
probably destruction” (Tukey, 1969, p. 84). However, replications
were rarely conducted due to lack of incentive and rarely published
due to lack of novelty (Nosek & Lakens, 2014). This lack of
incentive gradually started to change when concerns about “the
reliability of research findings in the field” began to emerge
(Pashler & Wagenmakers, 2012, p. 528). The field has been amid
“a crisis of confidence” (P.
A comment in Nature, signed by over 800 researchers, called for a rise up against statistical significance. This was followed by a special issue of The American Statistician aimed at halting the use of the term "statistically significant", and new guidelines for statistical reporting in the New England Journal of Medicine. These slides discuss the broader context of the "p-value crisis" and alternatives for communicating the conclusions after statistical analyses.
Target audience: Medical researchers; Scientists involved in conducting or interpreting analyses and communicating the results of scientific research, as well as readers of scientific publications.
Learning objectives:
To understand the context of the reproducibility crisis in medical research.
To learn about problems with p-values and alternatives to report findings.
To understand how (not) to interpret significant and insignificant findings.
To learn how to communicate research findings in a modest, thoughtful, and transparent way.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
This lecture looks at:
- An explanation of each of the steps in the research process flowchart
- Types of data
- Generating and testing theories
- Measurement error
- Validity
- Reliability
Published Research, Flawed, Misleading, Nefarious - Use of Reporting Guidelin...John Hoey
Much published health sciences literature is misleading and biased
Efforts to correct this include use of reporting guidelines- criteria for doing science and reporting the results properly
Also discussion of conflicts of interest - how to report them.
INVITED EDITORIAL Lets Do It Again A Call for Replications TatianaMajor22
INVITED EDITORIAL: Let's Do It Again: A Call for Replications
in Psi Chi J o u rn a l o f Psychological Research
J o h n E. E d lu n d
R o c h e s te r In s titu te o fT e c h n o lo g y
S
c ie n c e is said to b e su ffe rin g fro m a crisis
o f r e p li c a b i li t y ( I o a n n i d is , 2 0 0 5 ). T h is
crisis o c c u rs w h e n s c ie n tific s tu d ie s fail
to b e s u p p o r te d by s u b s e q u e n t re s e a r c h . T h e
challenges posed by th e replication crisis address
the fundam ental n atu re o f science an d the p ublic’s
u n d e r s t a n d i n g o f it. N u m e r o u s c o n t r i b u t i n g
reasons for th e rep licatio n crisis have b e e n n o ted
in c lu d in g d a ta fa lsific a tio n (S te e n , 2 0 1 1 ), th e
pressures o f ten u re an d p ro m o tio n (Varian, 1998),
questionable research practices (Simmons, Nelson,
& Sim onsohn, 2011), th e tendency o f jo u rn a ls to
w ant to publish particularly novel papers (Steen,
2011), and the p reference for publishing significant
results (de W inter & H appee, 2013). T hese factors
all in c re a se th e o d d s o f in a c c u ra te in fo rm a tio n
b e in g p u b lish e d , w hich in tu rn is in c o rp o r a te d
in to tex ts, as h a p p e n e d w ith th e d e ta ils o f th e
original investigation in th e Kitty G enovese case,
which led to th e fam ous bystander apathy studies
(Griggs, 2015). T he p rim ary goal o f this editorial is
to briefly discuss th e factors th at have co n trib u ted
to th e re p lic atio n crisis, tech n iq u e s em ployed by
various jo u rn a ls in th e field to deal with the crisis,
and how Psi Chi Journal o f Psychological Research (PCf)
is responding.
Build U p to th e R eplication Crisis
Perhaps th e biggest indication o f the psychological
re p lic atio n crisis was a series o f p ap e rs th a t were
com pletely fabricated by several differen t authors
(L e v elt C o m m itte e , 2 0 1 2 ). In th e s e cases, th e
au th o rs in question were discovered to have com
pletely fabricated th e ir d ata based o n a n u m b e r o f
factors ra n g in g from th e inability o f coauthors to
get access to data to a statistical analysis o f raw data
from the papers suggesting th at the d ata was faked
(S im onsohn, 2013).
Some have looked at the data fabrication crisis
as a serie s o f u n r e la te d a n d iso la te d in c id e n ts,
perhaps driven by personal flaws o r am bition. O th
ers, however, have looked at systemic features in aca
dem ia as a potential influence on this p h e n o m e n a
(Nosek, Spies, & Motyl, 2012). For instance, it has
long been noted that ten u re an d prom otion in aca
dem ia is driven largely by the n u m b er an d quality of
publications (Varian, 1998). Early career research
ers in the field (graduate students, post-docs, an d
assistant faculty) are pressured to publish early and
often, an d this can lead ...
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Data collection methods to improve reproducibilityDigital Science
"Reproducibility, data collection, and laboratory management technologies" - Louis Culot, CEO of Biodata
Slides from Shaking It Up: Challenges and Solutions in Scholarly Information Management, San Francisco, April 22, 2015
There’s More Than One Way to Conduct a Replication StudyBey.docxrhetttrevannion
There’s More Than One Way to Conduct a Replication Study:
Beyond Statistical Significance
Samantha F. Anderson and Scott E. Maxwell
University of Notre Dame
As the field of psychology struggles to trust published findings, replication research has begun to become
more of a priority to both scientists and journals. With this increasing emphasis placed on reproducibility,
it is essential that replication studies be capable of advancing the field. However, we argue that many
researchers have been only narrowly interpreting the meaning of replication, with studies being designed
with a simple statistically significant or nonsignificant results framework in mind. Although this
interpretation may be desirable in some cases, we develop a variety of additional “replication goals” that
researchers could consider when planning studies. Even if researchers are aware of these goals, we show
that they are rarely used in practice—as results are typically analyzed in a manner only appropriate to a
simple significance test. We discuss each goal conceptually, explain appropriate analysis procedures, and
provide 1 or more examples to illustrate these analyses in practice. We hope that these various goals will
allow researchers to develop a more nuanced understanding of replication that can be flexible enough to
answer the various questions that researchers might seek to understand.
Keywords: replication, data analysis, confidence interval, effect size, equivalence test
Replication, a once largely ignored premise, has recently be-
come a defining precept for the future of psychology. Reproduc-
ibility has been referred to as the “cornerstone” (Simons, 2014, p.
76) and “Supreme Court” (Collins, 1985, p. 19) of science, and as
“the best and possibly the only believable evidence for the reli-
ability of an effect” (Simons, 2014, p. 76). In fact, “findings that
do not replicate are worse than fairy tales” (Wagenmakers, Wet-
zels, Borsboom, van der Maas, & Kievit, 2012, p. 633).
The idea of replication is not new. Even prior to Sir Ronald
Fisher and the advent of modern experimental design (circa 1935),
the field of agriculture used replication to assess accuracy and
reliability (Yates, 1964). In fact, Fisher himself emphasized the
importance of replication, believing that experimental findings are
only established if “a properly designed experiment rarely fails to
give . . . significance” (Fisher, 1926, p. 504). In 1969, Tukey noted
that “confirmation comes from repetition” and that ignoring the
need for replication would “lend[s] itself to failure and more
probably destruction” (Tukey, 1969, p. 84). However, replications
were rarely conducted due to lack of incentive and rarely published
due to lack of novelty (Nosek & Lakens, 2014). This lack of
incentive gradually started to change when concerns about “the
reliability of research findings in the field” began to emerge
(Pashler & Wagenmakers, 2012, p. 528). The field has been amid
“a crisis of confidence” (P.
A comment in Nature, signed by over 800 researchers, called for a rise up against statistical significance. This was followed by a special issue of The American Statistician aimed at halting the use of the term "statistically significant", and new guidelines for statistical reporting in the New England Journal of Medicine. These slides discuss the broader context of the "p-value crisis" and alternatives for communicating the conclusions after statistical analyses.
Target audience: Medical researchers; Scientists involved in conducting or interpreting analyses and communicating the results of scientific research, as well as readers of scientific publications.
Learning objectives:
To understand the context of the reproducibility crisis in medical research.
To learn about problems with p-values and alternatives to report findings.
To understand how (not) to interpret significant and insignificant findings.
To learn how to communicate research findings in a modest, thoughtful, and transparent way.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
This lecture looks at:
- An explanation of each of the steps in the research process flowchart
- Types of data
- Generating and testing theories
- Measurement error
- Validity
- Reliability
Published Research, Flawed, Misleading, Nefarious - Use of Reporting Guidelin...John Hoey
Much published health sciences literature is misleading and biased
Efforts to correct this include use of reporting guidelines- criteria for doing science and reporting the results properly
Also discussion of conflicts of interest - how to report them.
INVITED EDITORIAL Lets Do It Again A Call for Replications TatianaMajor22
INVITED EDITORIAL: Let's Do It Again: A Call for Replications
in Psi Chi J o u rn a l o f Psychological Research
J o h n E. E d lu n d
R o c h e s te r In s titu te o fT e c h n o lo g y
S
c ie n c e is said to b e su ffe rin g fro m a crisis
o f r e p li c a b i li t y ( I o a n n i d is , 2 0 0 5 ). T h is
crisis o c c u rs w h e n s c ie n tific s tu d ie s fail
to b e s u p p o r te d by s u b s e q u e n t re s e a r c h . T h e
challenges posed by th e replication crisis address
the fundam ental n atu re o f science an d the p ublic’s
u n d e r s t a n d i n g o f it. N u m e r o u s c o n t r i b u t i n g
reasons for th e rep licatio n crisis have b e e n n o ted
in c lu d in g d a ta fa lsific a tio n (S te e n , 2 0 1 1 ), th e
pressures o f ten u re an d p ro m o tio n (Varian, 1998),
questionable research practices (Simmons, Nelson,
& Sim onsohn, 2011), th e tendency o f jo u rn a ls to
w ant to publish particularly novel papers (Steen,
2011), and the p reference for publishing significant
results (de W inter & H appee, 2013). T hese factors
all in c re a se th e o d d s o f in a c c u ra te in fo rm a tio n
b e in g p u b lish e d , w hich in tu rn is in c o rp o r a te d
in to tex ts, as h a p p e n e d w ith th e d e ta ils o f th e
original investigation in th e Kitty G enovese case,
which led to th e fam ous bystander apathy studies
(Griggs, 2015). T he p rim ary goal o f this editorial is
to briefly discuss th e factors th at have co n trib u ted
to th e re p lic atio n crisis, tech n iq u e s em ployed by
various jo u rn a ls in th e field to deal with the crisis,
and how Psi Chi Journal o f Psychological Research (PCf)
is responding.
Build U p to th e R eplication Crisis
Perhaps th e biggest indication o f the psychological
re p lic atio n crisis was a series o f p ap e rs th a t were
com pletely fabricated by several differen t authors
(L e v elt C o m m itte e , 2 0 1 2 ). In th e s e cases, th e
au th o rs in question were discovered to have com
pletely fabricated th e ir d ata based o n a n u m b e r o f
factors ra n g in g from th e inability o f coauthors to
get access to data to a statistical analysis o f raw data
from the papers suggesting th at the d ata was faked
(S im onsohn, 2013).
Some have looked at the data fabrication crisis
as a serie s o f u n r e la te d a n d iso la te d in c id e n ts,
perhaps driven by personal flaws o r am bition. O th
ers, however, have looked at systemic features in aca
dem ia as a potential influence on this p h e n o m e n a
(Nosek, Spies, & Motyl, 2012). For instance, it has
long been noted that ten u re an d prom otion in aca
dem ia is driven largely by the n u m b er an d quality of
publications (Varian, 1998). Early career research
ers in the field (graduate students, post-docs, an d
assistant faculty) are pressured to publish early and
often, an d this can lead ...
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
5. Reproducibility is one of the cornerstones
of science.
Robert Boyle (1627-1691)
New Experiments Physico-Mechanical,
Touching The Spring of the Air, and its
Effects; Made, for the most part, in a New
Pneumatical Engine (1660)
6. ‚ .. non-reproducible single occurrences
are of no significance to science …‘
The Logic of Scientific Discovery (1934)
Sir Karl Popper
(1902-1994)
‘We do not take even our own
observations quite seriously, or accept
them as scientific observations, until we
have repeated and tested them. Only by
such repetitions can we convince ourselves
that we are not dealing with a mere
isolated ‘coincidence’, but with events
which, on account of their regularity and
reproducibility, are in principle inter-
subjectively testable.’
7. Reproduction is in the 'DNA' of the
scientific process
'Differentielle Reproduktion von
Experimentalsystemen'
‘An experimental system owes its
temporal coherence to its
reproduction, and its development
depends on whether one manages to
produce differences without
destroying its reproductive coherence.’
Hans-Jörg Rheinberger
(*1946)
Experiment-Differenz-Schrift. Zur
Geschichte epistemischer Dinge), Marburg
an der Lahn: Basilisken-Presse, 1992.
8. The lexicon of reproducibility
Methods reproducibility: Same data, same tools, same
results? Adds no additional evidence!
Results reproducibility (aka „replication“): Technically
competent repetition, i.e. a new study. Could be strict:
identical conditions: or conceptual: altered conditions (does
causal claim extend to previously unsampled settings?)
Inferential reproducibility: Same conclusions from study
replication or re-analysis? Not all scientists come to the
same conclusions from same results, or may make different
analytic choices. What is concluded or recommended from
a study is often the only thing that matters!
Adapted from Goodman et al. Sci Transl Med. 2016;8:341ps12.
9. What do we mean by 'reproducible'?
Significance and P values: Evaluating replication effect against null
hypothesis of no effect
Evaluating replication effect against original effect size: Is the
original effect size within the 95% CI of the effect size estimate
from the replication. Alternatively: Comparing original and
replication effect sizes
Meta-analysis combining original and replication effects:
Combining original and replication effect sizes for cumulative
evidence
Subjective assessment of “Did it replicate?”
From the Open Science Collaboration, Psychology Replication, Science. 2015 ;349(6251):aac4716
11. The emptiness of failed replication
Mitchell J (2014) On the evidentiary evidence of failed replication
http://jasonmitchell.fas.harvard.edu/Papers/Mitchell_failed_science_2014.pdf
12. The emptiness of failed replication
Does a failure to replicate mean that the original
result was a false positive? Or was the failed
replication a false negative?
Does successful replication mean that the original
result was correct? Or are both results false positives?
13. Hidden moderators - Contextual
sensitivity – Tacit knowledge
‚We analyzed 100 replication attempts in psychology and found that the
extent to which the research topic was likely to be contextually sensitive
(varying in time, culture, or location) was associated with replication
success. This relationship remained a significant predictor of replication
success even after adjusting for characteristics of the original and
replication studies that previously had been associated with replication
success (e.g., effect size, statistical power).‘
Proc Natl Acad Sci. 2016;113:6454-9.
15. The stigma of nonreplication -
The stigma of the replicator
Dirnagl U (2018) Can (Non)-Replication be a Sin?
https://dirnagl.com/2018/05/16/can-non-replication-be-a-sin/
16. p = 0.049 (p< α = 0.05)
Assume that the experimental result is correct, i.e.
measured difference equals (unknown) treatment effect.
Repeat experiment under identical conditions (i.e. 'strict
replication').
What is the probability to reproduce the significant
findings?
50 %!
How likely is strict replication ?
17. Nonreproducibility as an indicator of
cutting edge research?
Dirnagl (2017) How likely are your hypotheses, really?
https://dirnagl.com/2017/04/13/how-original-are-your-scientific-hypotheses-really/
18. Confirmation – weeding out the false
positives of exploration
Jonathan
Kimmelman
PLoS Biol. (2014) 12:e1001863.
20. Resolving the tension:
Discovery & Replication
Suggested reading:
Wagenmakers EJ, Dutilh G, Sarafoglou A.
Perspect Psychol Sci. 2018 Jul;13(4):418-427
Chang and Eng Bunker circa 1865. Foto Hulton/Getty
21. No scientific progress without
nonreproducibility
To boldly go where no man…
Exploration at low base rate
Innovation
‚Paradigm shift‘
Incompetence
Bad designs
Tacit knowledge (bad reporting)
Low validity (bias)
Misconduct
The Good The Bad
Essential non-reproducibility
(Kuhn)
Detrimental nonreproducibility
(Popper)
The first to stress the importance of reproducibility in science was the Irish chemist Robert Boyle, in England in the 17th century. Boyle's air pump was designed to generate and study vacuum, which at the time was a very controversial concept. Indeed, distinguished philosophers such as René Descartes and Thomas Hobbes denied the very possibility of vacuum existence. Historians of science e.g. Steven Shapin and Simon Schaffer, in their 1985 book Leviathan and the Air-Pump, describe the debate between Boyle and Hobbes, ostensibly over the nature of vacuum, as fundamentally an argument about how useful knowledge should be gained. Boyle, a pioneer of the experimental method, maintained that the foundations of knowledge should be constituted by experimentally produced facts, which can be made believable to a scientific community by their reproducibility. By repeating the same experiment over and over again, Boyle argued, the certainty of fact will emerge.
https://scientistseessquirrel.wordpress.com/2015/02/27/reproducibility-your-methods-section-and-400-years-of-angst/
The famous physicist Robert Boyle grappled with this question in the middle of the 1600s, and his answer had three elements (Shapin 1984). First, Boyle gave exhaustive detail of equipment, material, and procedures, so that readers could (at least in principle) reproduce his experiments. Second, he argued for “communal witnessing”: if results were to have authority, experiments should be witnessed – so Boyle conducted many of his key experiments in public, and published the names and qualifications of witnessing scientists along with his results. Third, Boyle described in exhaustive detail not just his methods, but his experiments’ circumstances and settings, his false starts and failures, and much else. For example, to accompany his reports of experiments using his famous vacuum pump, he provided an illustration (above) of the pump. Not, importantly, of a vacuum pump, but of the vacuum pump he used, complete with irregularities, dents, and dings. The point of all this description was to make readers feel as if they had been there – to recruit readers as “virtual witnesses”
Kant was perhaps the first to realize that the objectivity of scientific statements is closely connected with the construction of theories — with the use of hypotheses and universal statements. Only when certain events recur in accordance with rules or regularities, as is the case with repeatable experiments, can our observations be tested — in principle — by anyone. We do not take even our own observations quite seriously, or accept them as scientific observations, until we have repeated and tested them. Only by such repetitions can we convince ourselves that we are not dealing with a mere isolated ‘coincidence’, but with events which, on account of their regularity and reproducibility, are in principle inter-subjectively testable.
Every experimental physicist knows those surprising and inexplicable apparent ‘effects’ which in his laboratory can perhaps even be reproduced for some time, but which finally disappear without trace. Of course, no physicist would say in such a case that he had made a scientific discovery (though he might try to rearrange his experiments so as to make the effect reproducible). Indeed the scientifically significant physical effect may be defined as that which can be regularly reproduced by anyone who carries out the appropriate experiment in the way prescribed. No serious physicist would offer for publication, as a scientific discovery, any such ‘occult effect,’ as I propose to call it — one for whose reproduction he could give no instructions. The ‘discovery’ would be only too soon rejected as chimerical, simply because attempts to test it would lead to negative results. (It follows that any controversy over the question whether events which are in principle unrepeatable and unique ever do occur cannot be decided by science: it would be a metaphysical controversy.)
– Karl Popper (1959/2002), The Logic of Scientific Discovery, pp. 23-24.
An experimental system owes its temporal coherence to its reproduction, and its development depends on whether one manages to produce differences without destroying its reproductive coherence. Together, these two factors make up its differential reproduction.
The construction process is dominated by a kind of probing movement which with regard to the scientific object can be described as a “jeu des possibles”24 or a “‘game’ of difference.”25 I would like to suggest that it is precisely the way in which it is “falling prey to its own work” that makes the scientific enterprise similar in a certain sense to what Derrida called “the enterprise of deconstruction.”26 To play this game productively requires “Erfahrenheit”27 on the part of the experimenter, something that can perhaps best be paraphrased using the paradoxical expression ‘acquired intuition’.28 We can conclude from what has just been said that one never knows exactly where an experimental system will lead. As soon as one knows exactly what it produces it is no longer a research system. An experimental system in which a scientific object gradually takes on contours in the sense that certain signals can be handled in a reproducible way, has to simultaneously open windows in which new signals are visible. Once it is stabilised in one respect, it can and must be destabilised in another in order to arrive at new ‘results’.29 Stabilisation and destabilisation are interdependent. In order to remain productive, an experimental set-up has to be sufficiently open to produce unforeseeable signals and to let new technologies, instruments, and model substances seep in.
• Recent hand-wringing over failed replications in social psychology is largely
pointless, because unsuccessful experiments have no meaningful scientific
value.
• Because experiments can be undermined by a vast number of practical mistakes,
the likeliest explanation for any failed replication will always be that the replicator
bungled something along the way. Unless direct replications are conducted
by flawless experimenters, nothing interesting can be learned from them.
• Three standard rejoinders to this critique are considered and rejected. Despite
claims to the contrary, failed replications do not provide meaningful
information if they closely follow original methodology; they do not necessarily
identify effects that may be too small or flimsy to be worth studying; and they
cannot contribute to a cumulative understanding of scientific phenomena.
• Replication efforts appear to reflect strong prior expectations that published
findings are not reliable, and as such, do not constitute scientific output.
• The field of social psychology can be improved, but not by the publication of
negative findings. Experimenters should be encouraged to restrict their
“degrees of freedom,” for example, by specifying designs in advance.
• Whether they mean to or not, authors and editors of failed replications are
publicly impugning the scientific integrity of their colleagues. Targets of failed
replications are justifiably upset, particularly given the inadequate basis for
replicators’ extraordinary claims.
to be in the pillory - am pranger stehen
power irrelevant, as experiment reproduced under identical conditions
The Amazing American Story of the Original Siamese Twins
Few newcomers to the U.S. have crossed more daunting barriers than Chang and Eng Bunker