Why do we perform research?
What exactly is research?
How to perform research?
How to perform natural science?
How to perform design science?
How to design research?
This is a presentation that aims to help PhD students (in management research or related fields) to connect their research questions with the research method that could fit better.
The material is a combination of presentations from other colleagues, credit is explicitly stated in the slides. The presentation also contains material from research papers that are strongly suggested as follow-on readings.
The overlaps between Action Research and Design ResearchSandeep Purao
Cole, R. , Purao, S., Rossi, M., Sein, M. 2005. Being Proactive: Where Action Research meets Design Research. International Conference on Information Systems. (ICIS) Las Vegas, NV, December 11-14. Originally presented at ICIS.
This is a presentation that aims to help PhD students (in management research or related fields) to connect their research questions with the research method that could fit better.
The material is a combination of presentations from other colleagues, credit is explicitly stated in the slides. The presentation also contains material from research papers that are strongly suggested as follow-on readings.
The overlaps between Action Research and Design ResearchSandeep Purao
Cole, R. , Purao, S., Rossi, M., Sein, M. 2005. Being Proactive: Where Action Research meets Design Research. International Conference on Information Systems. (ICIS) Las Vegas, NV, December 11-14. Originally presented at ICIS.
A brief introduction to Design Science for Information Systems by Paul Johannesson at KTH/Stockholm University. The presentation builds on the work by Alan Hevner and others.
This ppt was used as part of Dr. Darci Harland's WIP-5 grant workshop. Topics discussed this day were tips for developing a unit and lesson plan for R&D, difference between descriptive and inferential statistics, helping students interpret the data. The ADDIE model of curriculum design was described.
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
A research paper writing is a problem for every newcomer in the research field. This slide deck explains research writing in simple words and examples.
A brief introduction to Design Science for Information Systems by Paul Johannesson at KTH/Stockholm University. The presentation builds on the work by Alan Hevner and others.
This ppt was used as part of Dr. Darci Harland's WIP-5 grant workshop. Topics discussed this day were tips for developing a unit and lesson plan for R&D, difference between descriptive and inferential statistics, helping students interpret the data. The ADDIE model of curriculum design was described.
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
A research paper writing is a problem for every newcomer in the research field. This slide deck explains research writing in simple words and examples.
FROM BRAINSTORMING TO C-SKETCH TO PRINCIPLES OF HISTORICAL INNOVATORS: IDEATI...FaelXC
This Paper is Submitted to Fulfill The English 2 Task Study Program Software Engineering 4th Semester Buddhi Dharma University, Tangerang. Lecturer: Dra. Harisa Mardiana, M.Pd.
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.it/.
http://www.ivanomalavolta.com
The presentation from the Workshop on Enterprise Interoperability Science Base - Coventry UK - April 2010
To cite this publication, use:
Charalabidis Y., Goncalves R.J., Popplewell K.: “Developing a Science Base for Enterprise Interoperability”, Interoperability of Enterprise Systems and Applications Conference, i-ESA 2008 (IFIP), 12-15 April 2010, Coventry, UK.
Theories in Empirical Software EngineeringDaniel Mendez
Slides from the International Advanced School on Empirical Software Engineering 2015, held as part of the Empirical Software Engineering International Week in Beijing. The slides are posted with the permission of the main organiser Roel Wieringa.
1How to Perform ExperimentsBasic Concepts CSCI .docxdrennanmicah
1
How to Perform Experiments:
Basic Concepts
CSCI 783: Empirical Software Engineering
2
Empirical Software Engineering: How to use empirical research in software engineering?
Repetition of empirical studies is necessary!
Definition
Planning and Design
Execution
Analysis
Packaging
Definition: Determine study goal(s)
Design: and research hypothesis(es). Select type of empirical study to be employed Operationalize study goal(s) and hypotheses. Make study plan: what needs to be done by whom and when. Prepare material required to conduct the study
Execution: Run study according to plan and collect required data
Analysis: Analyze collected data to answer operationalized study goals and hypotheses
Packaging: Report your studies
3
Empiricism in Software Engineering
Confirmation
Evaluation
Identification
Validation
Understanding
Guidance / Control
Of more or less accepted hypotheses:
For example: object-orientation is good for reuse
Of Methods:
For example: Whether Java produces higher quality code than C++
Of Relationships:
For example: Find a relationship between fault prone components and design concepts
Of Models and Measures:
For example: Validate a specific cost estimate model
Of Methods, Techniques and Models:
For example: To understand the relationship between inspections and testing
to help in Management:
For example: as input to personnel to software inspections
To support Decision- Making with respect to Changes:
For example: Whether or not to introduce a new development tool
C
Change / Improve
Experimentation in software engineering
4
Experiment Objective
Cause
Construct
Effect
Construct
Cause-effect
Construct
Theory
Treatment
Outcome
Treatment - Outcome
Construct
Observation
Experiment Operation
Independent variable
Dependent variable
5
What is Empirical Software Engineering Research
What kinds of questions are "interesting"?
What kinds of results help to answer these questions, and what research methods can produce these results?
What kinds of evidence can demonstrate the validity of a result, and how to distinguish good results from bad ones?
6
Types of Research Questions
What kinds of questions are "interesting"?
Types of Research Questions
Method or means of development
Method for analysis
Design, evaluation, or analysis of a particular instance
Generalization or characterization
Feasibility
How can we do/create (or automate doing) X?
What is a better way to do/create X?
How can I evaluate the quality/correctness of X?
How do I choose between X and Y?
What is a (better) design or implementation for application X?
What is property X of artifact/method Y?
How does X compare to Y?
What is the current state of X / practice of Y?
Given X, what will Y (necessarily) be?
What, exactly, do we mean by X?
What are the important characteristics of X?
What is a good formal/empirical model for X?
What are the varieties of X, how are they related?
Is it possible to accomplis.
In this study, the effect of combining variables from the different data sources for student academic performance prediction was examined using three state-of-the–art classifiers: Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The study examined the use of heterogeneous multi-model ensemble techniques to predict student academic performance based on the combination of these classifiers and three different data sources. A quantitative approach was used to develop the various base classifier models while the ensemble models were developed using stacked generalisation ensemble method in order to overcome the individual weaknesses of the different models. Variables were extracted from the institution’s Student Record System and Learning Management System (Moodle) and from a structured student questionnaire. At present, negligible work has been done using this integrated approach and ensemble techniques especially with aggregated learner data in performance prediction in HE. The empirical results obtained show that the ensemble models.........................
Effectiveness of multistakeholder platforms in delivering development outcomesILRI
Presented by Murat Sartas at the Blended Learning Course for Facilitators, Monitors, Organizers and Researchers of Innovation Platforms, Hanoi, Vietnam, 9-11 November 2015
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
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.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
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.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
March ST, Smith GF (1995) Design and natural science research on information technology. Decis. Support Syst. 15(4):251–266.
Wieringa RJ, Heerkens JMG (2006) The methodological soundness of requirements engineering papers: a conceptual framework and two case studies. Requir. Eng. 11(4):295–307.
Wieringa RJ (2009) Design science as nested problem solving. Proc. 4th Int. Conf. Des. Sci. Res. Inf. Syst. Technol. - DESRIST ’09. (ACM Press, New York, New York, USA).
Gregor S (2006) The nature of theory in information systems. MIS Q. 30(3):611–642.
Examples
Descriptive theory: Claes J, Vanderfeesten I, Reijers HA, Pinggera J, Weidlich M, Zugal S, Fahland D, Weber B, Mendling J, Poels G (2012) Tying process model quality to the modeling process: The impact of structuring, movement, and speed. Barros A, Gal A, Kindler E, eds. Proc. 10th Int. Conf. Bus. Process Manag. (BPM ’12), Tallinn, Est. Sept. 3, 2012. (LNCS 7481, Springer), 33–48.
Explanatory theory: Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2015) The Structured Process Modeling Theory (SPMT) - A cognitive view on why and how modelers benefit from structuring the process of process modeling. Inf. Syst. Front. 17(6):1401–1425.
Prescriptive theory: Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2017) Towards a structured process modeling method: Building the prescriptive modeling theory (accepted). Proc. BPM 2016 Conf. Work. (LNBIP 281, Springer), 168–179.
(Method): Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2017) The Structured Process Modeling Method (SPMM) what is the best way for me to construct a process model? Decis. Support Syst. (in press)
Gregor S (2006) The nature of theory in information systems. MIS Q. 30(3):611–642.
Grover V, Lyytinen K, Srinivasan A, Tan BCY (2008) Contributing to rigorous and forward thinking explanatory theory. J. Assoc. Inf. Syst. 9(2):40–47.
Weber R (2012) Evaluating and developing theories in the information systems discipline. J. Assoc. Inf. Syst. 13(1):1–31.
Weick KE (1989) Theory construction as disciplined imagination. Acad. Manag. Rev. 14(4):516–531.
Example: Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2015) The Structured Process Modeling Theory (SPMT) - A cognitive view on why and how modelers benefit from structuring the process of process modeling. Inf. Syst. Front. 17(6):1401–1425.
Hevner ARR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q. 28(1):75–105.
Smith SM, Albaum GS (2005) Fundamentals of Marketing Research (SAGE).
Polančič G, Cegnar B (2016) Complexity metrics for process models - A systematic literature review. Comput. Stand. Interfaces 51(July 2016):104–117.
Moody DL (2009) The “physics” of notations: Toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6):756–779.
Example: Claes J, Vanderfeesten I, Pinggera J, Reijers HA, Weber B, Poels G (2015) A visual analysis of the process of process modeling. Inf. Syst. E-bus. Manag. 13(1):147–190.
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3):319–340.
Moody DL (2003) The method evaluation model: A theoretical model for validating information systems design methods. In: Ciborra CU, Mercurio R, Marco M de, Martinez M, Carignani A, eds. Proc. 11th Eur. Conf. Inf. Syst. (ECIS ’03). (AIS Electronic Library, Naples, Italy), 1327–1336.
Example: Claes J, Poels G (2014) Merging Event Logs for Process Mining: A Rule Based Merging Method and Rule Suggestion Algorithm. Expert Syst. Appl. 41(16):7291–7306.