Related Experiment Video
Updated: Jan 21, 2026
09:15
Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader
Published on: July 27, 2022
2.6K
A Link is not Enough - Reproducibility of Data.
Mateusz Pawlik1, Thomas Hütter1, Daniel Kocher1
11Database Research Group, University of Salzburg, Salzburg, Austria.
Datenbank-Spektrum : Zeitschrift Fur Datenbanktechnologie : Organ Der Fachgruppe Datenbanken Der Gesellschaft Fur Informatik E.V
|August 13, 2019
Summary
Reproducing empirical results in database research is difficult, even with available code. This study shows that data preparation details significantly impact experimental outcomes, emphasizing the need for more than just raw data access.
Area of Science:
- Computer Science
- Data Management
- Experimental Evaluation
Background:
- Database research often uses open data, but replicating empirical results is challenging.
- Even with accessible source code, reproducibility issues persist due to unaddressed factors.
Purpose of the Study:
- To highlight the critical role of data preparation in experimental reproducibility.
- To introduce a model and levels for assessing data reproducibility.
- To provide best practices for enhancing data reproducibility in research.
Main Methods:
- Analyzing the impact of data preparation on experimental results.
- Developing a data reproducibility model.
- Identifying distinct levels of data reproducibility.
Main Results:
- Subtle differences in data preparation significantly alter runtime results.
- A three-level framework for data reproducibility was established.
- Practical experiences and best practices were documented.
Conclusions:
- Raw datasets alone are insufficient for ensuring reproducibility.
- Detailed documentation and standardization of data preparation are crucial.
- The proposed model aids in achieving higher data reproducibility in database experiments.
Related Concept Videos
02:04Covalently Linked Protein Regulators
8.7K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
These groups modify specific amino acids in a protein....
8.7K
01:19X-linked Traits
58.3K
In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
58.3K
01:43Sex-linked Disorders
108.4K
Like autosomes, sex chromosomes contain a variety of genes necessary for normal body function. When a mutation in one of these genes results in biological deficits, the disorder is considered sex-linked.
108.4K
01:00Enzyme-linked Receptors
86.1K
Enzyme-linked receptors are proteins that act as both receptor and enzyme, activating multiple intracellular signals. This is a large group of receptors that include the receptor tyrosine kinase (RTK) family. Many growth factors and hormones bind to and activate the RTKs.
Neurotrophin (NT) receptors are a family of RTKs, including trkA, trkB, and trkC (tropomyosin-related kinase) receptors. TrkA is specific for nerve growth factor (NGF), neurotrophin-6, and neurotrophin-7. TrkB binds...
Neurotrophin (NT) receptors are a family of RTKs, including trkA, trkB, and trkC (tropomyosin-related kinase) receptors. TrkA is specific for nerve growth factor (NGF), neurotrophin-6, and neurotrophin-7. TrkB binds...
86.1K
00:59How Data are Classified: Numerical Data
36.8K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
36.8K
01:11How Data are Classified: Categorical Data
42.9K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
42.9K