Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 4 for limb-nadir application

Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 4 (only SO2 files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG.

opencc-by-4.0Sep 2022View details →

Data for: A spatial framework for prioritizing biochar application to arable land: a case study for Sweden

The uploaded data is related to the publication: A spatial framework for prioritizing biochar application to arable land: a case study for Sweden, and contains the following: (I) Raster files for three different biochar prioritization narratives. (II) High-resolution biochar use indication maps (in JPEG) for different prioritization narratives.

opencc-by-4.0Jun 2024View details →

Improving the application of Important Plant Areas to conserve threatened habitats: a case study of Uganda

This data set relates to the publication: Richards, S. L., Kalema, J., Ojelel, S., Williams, J., & Darbyshire, I. (2024). Improving the application of Important Plant Areas to conserve threatened habitats: A case study of Uganda. Conservation Science and Practice, e13246. https://doi.org/10.1111/csp2.13246 Abstract: Important Plant Areas (IPAs) are a successful method of identifying priority areas for plant conservation. Assessment of IPAs, however, often relies on criteria related to species, while incorporation of habitats has been less consistent. Using Uganda as a case study, we test the application of the threatened habitat criterion – criterion C. We identified nationally threatened habitats using Red List of Ecosystems criteria and assess, for the first time, how differing application of thresholds under IPA criterion C can influence IPA network outcomes. Eleven threatened habitats were identified, with declines switching from predominantly forest to savanna after the mid-20th century. Significantly, we found current IPA guidance on use of criterion C needlessly limits the number of sites that qualify as IPAs. The “five best sites” IPA threshold is reserved for countries where quantitative data is unavailable, however, the application of the relevant numerical thresholds (site contains ≥10% of national resource or site is among the best quality examples required to collectively prioritisie up to 20% of the national resource) to quantitative data largely generated fewer than five IPAs, comparably limiting conservation opportunities identified. We recommend, therefore, that the “five best” threshold is available for application on both qualitative and quantitative data. This will bolster the value of IPAs in conserving and restoring threatened and ecologically important habitats under the Kunming-Montreal Global Biodiversity Framework. Dataset: Within this dataset is a shapefile of the estimated extent of threatened habitats in Uganda. Each polygon represents a single "site" for each threatened habitat, with methodology for site identification given in the manuscript. Feature area and percentage national resource are given for each site, enabling users to identify those that trigger the different IPA criterion C thresholds. In this study, we have preliminarily identified the threatened habitats and IPAs for Uganda. However, it is important to seek the expertise and views of stakeholders, consider other IPA criteria met and any complementarity between sites when identifying IPAs. In addition, ground-truthing or more localised data could validate the threat status of a vegetation type as well as identifying which sites are best to conserve these habitats.

opencc-by-4.0Apr 2018View details →

A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 3. Screenshots of the new VC app

The user will be able to stay logged in inside the app and to receive different types of notifications. The application will have the ability to be set up for working offline, while users have the possibility to change the application language, which can be useful for foreign or Erasmus students. Additionally, users will be able to customize the app based on some preferences available in a settings screen, check their grades, send direct messages to other students or professors and also save important documents under “My files” section. During the beta phase of development, a focus group with teachers will be organized in order to present the new apps and to understand what new features are needed to support the courses facilitation on the go.

opencc-by-4.0Apr 2018View details →

A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 2. Screenshots of the Virtual Campus mobile application (d, e)

The VC app was downloaded and used by 350 students, representing a percentage of 5% of the total number of the students enrolled on the platform courses. The application has features corresponding to the desktop version of the platform. In developing it, there were applied principles related to mobile learning usability and design, trying to provide enhanced possibilities for learning on-the-go and also specific notifications (Machun et al., 2012; Harrison et al., 2013; Mocofan, 2017). Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.

opencc-by-4.0Apr 2018View details →

A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figire 1. Screenshots of the Virtual Campus mobile application (a, b, c)

Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.

Figure 4 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 4. The SVM structure used in application one approach. The weights determined during the superoised part of the training are {w0, w1, ..., wX̅1}. The output element linearly combines the outputs of the hidden layer with the weights.

Figure 2. The paraconsistent plane where the axes G1 and G2 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 2. The paraconsistent plane where the axes G1 and G2 represent the degrees of certainty and contradiction, respectioely. P = (G1, G2) = (α ̅ β, α + β ̅ 1), drawn in blue just to exemplify, is an important element for our analysis: The closer it is to the corner (1,0), the weaker the classifier associated with the features oector can be. The oalues of α and β are derioed from intra-class and inter-class analyses, respectioely, as detailed in [17].

Figure 1 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 1. Quesada gigas. On the left, male emitting acoustic signals. On the right, lateral oiew of male resting.

opencc-by-4.0Nov 2023View details →

CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 3 for limb-nadir application

Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 3 (only CO and PAN files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG.

opencc-by-4.0Jul 2021View details →

Data and script for binomial GLMs in "Lithic Analysis of Andean Sedentary Societies, a Case Study from the Chachapoyas Region, Peru, and Potential Applications." (Pratt & Guengerich)

This submission contains data and R-script that enable to reproduce the binomial generalized linear models in the paper “Lithic Analysis of Andean Sedentary Societies, a Case Study from the Chachapoyas Region, Peru, and Potential Applications” by Lauren V. Pratt & Anna Guengerich. Please cite the above paper if you use the files included in this Zenodo record in your work.

opencc-by-4.0Aug 2019View details →

Assessing the Resilience of Software Systems by Application of Chaos Engineering - A Case Study

This repo contains all supplementary data sets that I have created and used throughout my bachelor's thesis. In particular, it contains - JMeter configuration file - CSV files of measured JMeter runs - Chaos Experiments declared in JSON - Created boxplots and ecdf plots based on the CSV files - Kubernetes deployments - Bash scripts

Figure 8 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 8. Cross-oalidation algorithm.

Figure 6 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 6. The experimental setup for the proposed application two.

Figure 5 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 5. Examples of images used: high, low, and zero density, respectioely.

Figure 3 in A Case Study of Wavelets and SVM Application in Coffee Agriculture: Detecting Cicadas Based on Their Acoustic and Image Patterns

Figure 3. The experimental setup for the proposed application one.