Find research datasets worth reusing

Figure 15. Apical lunule grading system. 0 in A morphological and mtDNA analysis of the badlands tiger beetle, Cicindela (s. str.) decemnotata Say, 1817 (Coleoptera: Carabidae: Cicindelinae) with the description of three new subspecies C. Barry Knisley

Figure 15. Apical lunule grading system. 0 to 3 refers to increasing size of apical lunule: A. 0 (absent). B. 1. C. 2. D. 3 (largest).

Figure 14. Middle band grading system. 1 in A morphological and mtDNA analysis of the badlands tiger beetle, Cicindela (s. str.) decemnotata Say, 1817 (Coleoptera: Carabidae: Cicindelinae) with the description of three new subspecies C. Barry Knisley

Figure 14. Middle band grading system. 1 to 7 refers to increasing size and completeness of middle band: A. 0 (absent). B. 1. C. 2. D. 3. E. 4. F. 5. G. 6. H. 7 (largest).

Figure 13. Humeral lunule grading system. 0 in A morphological and mtDNA analysis of the badlands tiger beetle, Cicindela (s. str.) decemnotata Say, 1817 (Coleoptera: Carabidae: Cicindelinae) with the description of three new subspecies C. Barry Knisley

Figure 13. Humeral lunule grading system. 0 to 3 refers to increasing size of humeral lunule: A. 0 (absent). B. 1. C. 2. D. 3 (largest).

Figure 16. Connection between subapical and apical dots grading system. 0 in A morphological and mtDNA analysis of the badlands tiger beetle, Cicindela (s. str.) decemnotata Say, 1817 (Coleoptera: Carabidae: Cicindelinae) with the description of three new subspecies C. Barry Knisley

Figure 16. Connection between subapical and apical dots grading system. 0 to 3 refers to connection between sub apical dot and apical dot: A. 0 (widely separate). B. 1 (separate). C. 2 (thin connection). D. 3 (broadly connected).

opencc-by-4.0Dec 2023View details →

Immunogenomic analyses of high-grade serous ovarian cancer (HGSOC)

High-grade serous ovarian cancer (HGSOC) remains the most lethal gynecologic malignancy despite new therapeutic concepts, including poly-ADP-ribose polymerase inhibitors (PARPis) and antiangiogenic therapy. In this study, we have analyzed homologous recombination repair deficiency (HRD) or BRCAness and the composition of the tumor microenvironment, which appear to play a critical role in determining the therapeutic response of combination immunotherapy with PARPis. The used datasets include RNA sequencing data and clinical information from patients of a new HGSOC cohort of the Medical University of Innsbruck (MUI) (n=60) and analyzed data from The Cancer Genome Atlas (TCGA) (n=226).

opencc-by-4.0Aug 2023View details →

Processing, Spectroscopic and Laboratory Testing Data from a Medical Grade Hot-Melt Extrusion Process

This dataset contains a collection of raw processing data, spectroscopic data, and laboratory test results of medical-grade polymer extrusion experiments. The data was collected in several experiments conducted in a hot-melt extrusion process.  The process involved extruding PLA through a slit die and drawing the extruded strands onto spools to obtain the desired dimensional and mechanical properties. The strands were later knitted to form the final medical implant. Throughout the experiments, the extrusion process and equipment were upgraded and refined.  Various operational scenarios were simulated under different nozzle configurations. The experiments start using a single-screw extruder and later progress to a double-screw extruder. Medical Grade PURASORB PLA (PLDLA 96/4) material was used when the hardware upgrades were complete. This dataset contains many variations in experimental conditions. However, enough overlap exists to derive working datasets from this compiled raw data.   Two working datasets have been derived from this compiled raw data. Using a double-screw extruder, both working Datasets investigate polymer degradation in the hot-melt extrusion process. Both derived datasets are included in this collection.   Two Jupyter notebooks are included in this data collection. The first notebook gives an example of how an initial dataset can be derived from the raw data using data science techniques. The second notebook gives an example of how a final dataset can be created from the initial dataset.

opencc-by-4.0Mar 2024View details →

On the use of a consumer-grade 360-degree camera as a radiometer for scientific applications: calibration dataset

Studying the geometric distribution of the light field in terms of absolute radiometry required expensive and complex instruments. New types of compact 360-degree cameras have recently appeared on the consumer technology market. Some of these allow users to access raw imagery, offering sensor-level data that can be directly exploited for absolute light quantification. This paves the way for easy-to-use, inexpensive and accessible radiance cameras that can be operated in a wide range of natural environments.  This dataset presents raw format images captured with the camera Insta360 ONE for its calibration and characterization. These experiments include geometric calibration, relative illumination evaluation, spectral response determination, absolute spectral radiance calibration, as well as linearity and dark frame analysis. In addition, we are providing data from a calibration validation experiment based on co-located measurements of the sky's downward radiance using a 360-degree calibrated camera and a scientific radiometer: the Compact Optical Profiling System (C-OPS, Biospherical Instruments Inc.). This repository contains raw files as taken by the camera's imaging sensor. In most cases, no data processing has been carried out. The entire data set is contained in a .zip file which includes the following sub-folders (in alphabetical order): <ul> <li>absolute-radiance: data (.dng, .tsv , .hdf5) acquired for absolute spectral radiance calibration</li> <li>darkframe: DNG raw images taken for dark frame analysis</li> <li>geometric: DNG images used for the geometric calibration</li> <li>immersion-factor: DNG images for the calculation of the immersion factor</li> <li>linearity: DNG images for linearity assessment (gain and exposure time)</li> <li>relative-illumination: DNG images for roll-off (relative illumination) characterization</li> <li>relative-spectral-response: data (.asc, .dng) taken for relative spectral response characterization</li> <li>verification&validation: time series data (.tsv, .dng) of the calibration validation experiment </li> </ul> Each folder contains a README file explaining the additional subfolders and their files. As you may notice, some of the subfolders are named "lensclose" or "lensfar". They refers to the data acquired with the fish-eye optic assembly that is closer or farther from the top of the 360-degree camera respectively. Some calibrations were performed both in water and in air (geometric calibration, relative-illumination). In that regard, the images were placed in folders refering to "air" and "water". The routines (coded in python) for the data processing can be found in the following <a href="https://github.com/RaphaelLarouche/radiance_camera_insta360/tree/master_v01">Github repository</a> (master_v01) or the <a href="../records/4660994">Zenodo stored version</a>. The useful scripts are located in the calibration directory, and the README for each folder points to the revelant code for analysis of the files they contain. For additional information, all the methodologies are described in the <a href="https://arxiv.org/abs/2305.07103">arXiv preprint</a>.

opencc-by-4.0Jun 2020View details →

Combining dynamic and static analysis for automated grading SQL statements

Introduction Our experiment was conducted in an undergraduate Relational Database course at the Australian National University. The experiment was conducted on August 10th 2018 when students enrolled in the Relational Database course started to learn relational data model and SQL. The experiment was carried out fully online for three weeks and a total of 393 students were enrolled. The students were asked to login in an online assessment platform and complete 15 exercises. This platform provided an SQLite environment in students browsers by compiling the SQLite C code with Emscripten. Students were allowed to submit and execute their answers in the form of SQL statements. If the execution result of the statement submitted by the student is the same as that of the reference statement, the online assessment platforms will return a feedback message indicating that the execution result is correct. During the interaction with the assessment platform, statements submitted by students were recorded and archived. Overall, our experiment had collected 12,899 statements submitted by students. To create a benchmark dataset that can be used to evaluate different grading approaches, we randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we average the scores provided by the three assistants and take it as the final score of each statement. The dataset collected in this experiment is ready for public release. All experimental data are stored in Submission.sqlite, which is an SQLite database file. It is recommended to use software such as DB browser or SQLite expert to explore the database.   Datatable description   exercises_result This datatable stores the statements submitted by students. Based on the execution result of statement, statements were divided into three categories. <ul> <li>noninterpretable: the statement is non-executable.</li> <li>partially correct: the execution result of statement is different from the expected result.</li> <li>correct: the execution result of the SQL statement is the same as the expected result.</li> </ul> After analyzing the correct statements, we found that the correct set contains some statements carefully constructed by students to deceive the examination system. Take exercise 1 as an example, the task is to answer the following questions using SQL statements. Question: Assume persons who were born in the same year are the same age and there is only one youngest person (with no ties/draws) in this database, who is/are the second youngest person(s) in the database? List the id(s) of the person(s). The reference statement to this exercise is: <pre><code class="language-sql">SELECT p.id FROM person p WHERE p.year_born = (SELECT MAX(year_born) FROM person WHERE year_born < (SELECT MAX(year_born) FROM person)); </code></pre> By exploring the database or trying to execute different statements, some students found that the ID of the person who met the conditions was '00000842', so the following statement was submitted. <pre><code class="language-sql">select id from person where id ='00000842'; </code></pre> The execution result of the above code was correct, but it was obviously not what the tutor expected. Therefore, we identified such statements as 'cheating'. Table 1 Description of exercises_result table. <table> <thead> <tr> <th> field </th> <th> desc </th> <th> datatype </th> </tr> </thead> <tbody> <tr> <td> submission_id </td> <td> Submission ID </td> <td> INT </td> </tr> <tr> <td> submitted_answer </td> <td> statement submitted by student </td> <td> TEXT </td> </tr> <tr> <td> submission_time </td> <td> Submission time </td> <td> NUM </td> </tr> <tr> <td> exercise_id </td> <td> Exercise ID </td> <td> INT </td> </tr> <tr> <td> is_correct </td> <td> Mark whether the statement is correct </td> <td> INT </td> </tr> <tr> <td> student_id </td> <td> Student ID </td> <td> INT </td> </tr> <tr> <td> category </td> <td> categories of statement </td> <td> TEXT </td> </tr> </tbody> </table>   exercises_benchmark This datatable stores the scores provided by different assistants. We randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we averaged the scores provided by the three assistants as the final score of each statement. Table 2 Description of exercises_benchmark table. <table> <thead> <tr> <th> Field </th> <th> comment </th> <th> datatype </th> </tr> </thead> <tbody> <tr> <td> Submission_id </td> <td> Submission ID </td> <td> INT </td> </tr> <tr> <td> grade </td> <td> grade provided by tutor </td> <td> REAL </td> </tr> <tr> <td> tutor </td> <td> tutor </td> <td> TEXT </td> </tr> </tbody> </table>   exercises_exercise This datatable stores the exercises provided by tutor. Table 3 Description of exercises_exercise table. <table> <thead> <tr> <th> Field </th> <th> comment </th> <th> datatype </th> </tr> </thead> <tbody> <tr> <td> id </td> <td> Exercise ID </td> <td> INT </td> </tr> <tr> <td> title </td> <td> Title of exercise </td> <td> TEXT </td> </tr> <tr> <td> preamble </td> <td> Description of exercise </td> <td> TEXT </td> </tr> <tr> <td> difficulty </td> <td> Coefficient of difficulty </td> <td> integer </td> </tr> <tr> <td> ref </td> <td> Reference statement </td> <td> integer </td> </tr> </tbody> </table>   database schema Please refer to db_schema.pdf for the database schema used in the experiment.   BibTex if you want to cite our paper:   <blockquote> <pre>@article{wang2020combining, title={Combining dynamic and static analysis for automated grading SQL statements}, author={Wang, Jinshui and Zhao, Yunpeng and Tang, Zhengyi and Xing, Zhenchang}, journal={J Netw Intell}, volume={5}, number={4}, pages={179--190}, year={2020} }</pre> </blockquote>

opencc-by-4.0Oct 2022View details →

Datasets for Service-Like Creep-Fatigue Experiments on Grade P92 Steel

The dataset contains experimental mechanical data from complex service-like creep-fatigue experiments performed isothermally at 620 °C and a low strain amplitude of 0.2 % on tempered martensite-ferritic grade P92 steel. The data sets in text file format provide cyclic deformation (min. and max. stresses) and the total (hysteresis) data of all recorded fatigue cycles for three different creep-fatigue experiments: 1) a standard relaxation fatigue (RF) test with symmetrical dwell times of three minutes introduced at minimum and maximum strain, 2) a fully strain-controlled service-like relaxation (SLR) test combining these three-minute peak strain dwells with a 30-minute dwell in between at zero strain, and 3) a partly stress-controlled service-like creep (SLC) test combining the three-minute peak strain dwells with 30-minute dwells at constant stress. Further information on data and data acquisition, analysis, and experimental details are given in “Experimental Data from Service-Like Creep-Fatigue Experiments on Grade P92 Steel”, submitted to <a href="https://www.sciencedirect.com/journal/data-in-brief">Data in Brief</a>. Additional analyses of these datasets, as well as experimental findings and discussions are presented in “Creep-Fatigue of P92 in Service-Like Tests with Combined Stress- and Strain-Controlled Dwell Times”, submitted to <a href="https://www.sciencedirect.com/journal/international-journal-of-fatigue">International Journal of Fatigue</a>.

opencc-by-4.0Oct 2010View details →

Table 5: Model Fit Summary (b) for Grade Three

The model summary in Table 5 reports the strength of the relationship between the model and the dependent variable, i.e., ECA exam. The multiple correlation coefficient, R, which is the linear correlation between the observed and the model-predicted values of the dependent variable is .551. R Square, which is also called the coefficient of determination, is .303 showing that approximately 30 percent of the variation in ECA exam is explained by the model. Its interpretation is that 30 percent of the variation in the ECA test scores is common with the vocabulary scores. The Standard Error of the Estimate of the model is approximately 3.16 out of a total of 30, meaning that the prediction model produces an error range between ± [3.16]. Therefore, the prediction formula must be rewritten as (AVERAGE VOCAB × 0.713) + 2.871± [3.16].

opencc-by-4.0Jan 2018View details →

Table 2: Reliability Indexes of the Measures Used in Grade Three

The procedures of lexical instruction and vocabulary testing continued up to the end of the course when the book content was exhausted. Lastly, near to the end of the academic year, the students took the End of the Course Achievement (ECA) exams (See Appendix 3) the major objectives of which were to make a holistic assessment concerning the achievement of the prespecified course objectives. These tests are usually designed, standardized, and administered either directly by the central offices of the Ministry of Education, or indirectly by the teachers at the local schools. However, no matter who develops them, they follow a uniform scheme or format which is mandated by the officials of the Ministry of Education. The reliability indexes obtained from the batteries utilized in the study are presented in Tables 2

opencc-by-4.0Oct 2010View details →

Figure 1. Scatter plot of ECA exam by average vocabulary in Grade Three

The mean score of every learner on the vocabulary tests was calculated. Then, through the statistical procedure of regression analysis, the data from the mean scores and the ECA exam scores were analyzed to derive a model which could reliably predict the learners’ performance on the ECA exam. To check whether the two variables of the ECA exam and the average vocabulary were suitable for linear regression, its scatter plot was examined. The resulting scatter plot (Figure 1) seemed to be sufficient for linear regression.

opencc-by-4.0Oct 2010View details →

Table 4: ANOVA (b) for Grade Three

The ANOVA table below (Table 4) tests the acceptability of the model from a statistical perspective. The amount of regression sums of squares is122.385 indicating that about 33 percent of the total variation is explained by the model and the amount of residual sums of squares is 281.134 showing that about 67 percent of the variation is due to some factors other than the average vocabulary variable. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance.

Dataset: iShares Broad USD Investment Grade Corporate Bond ETF (USIG) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Dataset: iShares 0-5 Year Investment Grade Corporate Bond ETF (SLQD) Stock Performance

Dataset: iShares 5-10 Year Investment Grade Corporate Bond ETF (IGIB) Stock Performance

Dataset: iShares 1-5 Year Investment Grade Corporate Bond ETF (IGSB) Stock Performance

opencc-by-4.0Jun 2017View details →

Fig. 7 in Morphology of the female reproductive system and physiological age-grading of Megamelus scutellaris (Hemiptera: Delphacidae), a biological control agent of water hyacinth

Fig. 7. Number of eggs ovulated by adult females of Megamelus scutellaris correlated by a) age (days) and b) collar length (mm). The solid line represents the linear relationship between variables and the dashed lines is the 95% confidence interval (n = 15; P = 0.001 and r = 0.778).

opencc-by-4.0Jun 2017View details →

Fig. 6. The 3 in Morphology of the female reproductive system and physiological age-grading of Megamelus scutellaris (Hemiptera: Delphacidae), a biological control agent of water hyacinth

Fig. 6. The 3 parous classes of Megamelus scutellaris. The P1 class (a and b) is characterized by the presence of follicular relics, which may not be present in some or all ovarioles, may be light in coloration and may or may not encircle the base of the ovariole. The follicular relics do not occur at high enough densities to cause an expansion or bulging. The collar may or may not be visible and does not extend past the follicular relic accumulation area. In the P2 class (c and d) follicular relics are present in all ovarioles and at high enough densities to cause bulging. They are distinctly yellow in coloration and relatively darker in comparison to those found in the P1 class. The collar is easily seen and typically extends past the follicular accumulation area. In the P3 class (e and f) follicular relics are variable, may or may not be in high enough densities to cause bulging, and typically completely encircle the base. The collar length easily surpasses the follicular relic accumulation area.