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FIGURE 7 in Illustrated and online catalog of type specimens of freshwater fishes in the Colección de Peces Dulceacuícolas of Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-P), Colombia
FIGURE 7. Paratype of Leporinus boehlkei, IAvH-P 10562, 70.2 mm SL (scale bar = 1 cm). Photograph by M. H. Sabaj Pérez.
FIGURE 19 in Illustrated and online catalog of type specimens of freshwater fishes in the Colección de Peces Dulceacuícolas of Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-P), Colombia
FIGURE 19. Paratype of Nemadoras cristinae, IAvH-P 9943, 53.1 mm SL (scale bar = 1 cm). Photograph by M. H. Sabaj Pérez.
FIGURE 15 in Illustrated and online catalog of type specimens of freshwater fishes in the Colección de Peces Dulceacuícolas of Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-P), Colombia
FIGURE 15. Paratype of Astroblepus nettoferreirai, IAvH-P 13209, 78.3 mm SL (scale bar = 1 cm). Photograph by C. DoNascimiento.
Hora: Architecture-aware online failure prediction
<p>Complex software systems experience failures at runtime even though a lot of effort is put into the development and operation. Reactive approaches detect these failures after they have occurred and already caused serious consequences. In order to execute proactive actions, the goal of online failure prediction is to detect these failures in advance by monitoring the quality of service or the system events. Current failure prediction approaches look at the system or individual components as a monolith without considering the architecture of the system. They disregard the fact that the failure in one component can propagate through the system and cause problems in other components. In this paper, we propose a hierarchical online failure prediction approach, called HORA , which combines component failure predictors with architectural knowledge. The failure propagation is modeled using Bayesian networks which incorporate both prediction results and component dependencies extracted from the architectural models. Our approach is evaluated using Netflix’s server-side distributed RSS reader application to predict failures caused by three representative types of faults: memory leak, system overload, and sudden node crash. We compare HORA to a monolithic approach and the results show that our approach can improve the area under the ROC curve by 9.9%.</p>
Supporting data for "KAPow: High-accuracy, Low-overhead Online Per-module Power Estimation for FPGA Designs"
<p>Supporting data for "KAPow: High-accuracy, Low-overhead Online Per-module Power Estimation for FPGA Designs"</p>
An Online Paleoclimate Data Assimilation with a Deep Learning-based Network
<p>OnlinePDA_zenodo.rar (files compressed with RAR compression) includes:</p> <p>1. Directory 'Code' contains the <em>LIM</em>.<em>m</em> and <em>NET</em>.<em>m</em> for the examination of predictive skills of the surrogate models; the <em>Exp_LIM</em>.<em>m</em> and <em>Exp_NET</em>.<em>m</em> for the reconstruction of SAT during the preindustrial period (851-1850 CE) and the instrumental period(1880-2000CE), for both pseudoproxy experiments and real proxy experiments; </p> <p>2. Directory 'Data' contains the necessary data in the <strong>model</strong>, <strong>prior</strong>, <strong>proxy</strong>, and <strong>obs</strong> directory used to perform the resconstruction. e.g. surrogate models, prior samples, pseudoproxy and real proxy, and instrumental data; <em>Figure</em>*.<em>m and</em> <em>CERMSE</em>*.<em>mat</em> provide the necessary code and data for plotting Figures 3-10</p>
Online appendix C
<p>Online appendix C for article in <em>Capital Markets Law Journal</em> entitled "Spoofing in U.S. Futures Markets: An Interdisciplinary Approach".</p>
Online repository for Paper "AgentFL: Scaling LLM-based Fault Localization to Project-Level Context"
<h3>Summary</h3> <p>This is the online repository for the arXiv paper "AgentFL: Scaling LLM-based Fault Localization to Project-Level Context".</p> <p>We also provide the results for the TSE'25 paper "SOAPFL: A Standard Operating Procedure for LLM-based Method-Level Fault Localization".</p> <h3>Environment</h3> <ul> <li><a href="https://github.com/rjust/defects4j/tree/v1.4.0">Defects4J-V1.4.0</a> (Note that the buggy items in V1.4.0 is identical with V1.2.0, we use V1.4.0 to avoid some problems in V1.2.0)</li> <li><a href="https://github.com/rjust/defects4j/tree/v2.0.0">Defects4J-V2.0.0</a></li> <li>Python version >= 3.8.5</li> </ul> <h3>Defects4J Mod</h3> <p>Before running AgentFL, please apply the files under the <code>AgentFL/Defects4J_mod</code> directory to modify your Defects4J V1.4.0/V2.0.0.</p> <h3>Run AgentFL</h3> <p>Set your own OpenAI API key in <code>AgentFL/camel/model_backend.py</code></p> <p>It's easy to run AgentFL for localizing a bug with the following command:</p> <p><code>python3 run.py --config <CONFIG_DIR> --version <D4J_VERSION> --project <PROJECT> --bugID <BUG_ID> --model <GPT_MODEL_NAME></code></p> <p>For example:</p> <p><code>python3 run.py --config Default --version 1.4.0 --project Closure --bugID 26 --model GPT_3_5_TURBO</code></p> <p>More configs can be seen under the directory <code>AgentFL/Config</code></p> <h3>Results</h3> <p>We release all of the results of AgentFL in the <code>AgentFL/Results</code> directory, including the evaluation results on Defects4J V1.4.0/V2.0.0 and the ablation study result.</p> <p>For each bug, we record all of the prompts, responses, and intermediate outputs.</p> <blockquote> <p>NEW: We have released the newest results for TSE'25 paper "SOAPFL: A Standard Operating Procedure for LLM-based Method-Level Fault Localization". The results can be found in the `<a href="https://zenodo.org/api/records/16938304/draft/files/SoapFL_results.zip/content" target="_blank" rel="noopener noreferrer">SoapFL_results.zip</a>` file!</p> </blockquote> <h3>Human Evaluation Results</h3> <p>The human evaluation results can be found in the file <code>AgentFL/EvaluationResult/DebugResult_d4j140_GPT35_human.xlsx</code></p> <h3>System Messages for Agents</h3> <ul> <li>Test Code Reviewer:</li> </ul> <blockquote> <p>You are a Test Code Reviewer. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You can examine the test code and the initialized classes to analyze the similar behavior of the failed tests within the test suite. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Source Code Reviewer</li> </ul> <blockquote> <p>You are a Source Code Reviewer. we are both working at DebugDev. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. Your main responsibilities is to generate a comment for each covered method base on the method call relationship. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Software Test Engineer</li> </ul> <blockquote> <p>You are a Software Test Engineer. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You main responsibilities include examining the information of the failed tests to analyze the possible causes of the test failures, and determining the method that need to be fixed. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Software Architect</li> </ul> <blockquote> <p>You are a Software Architect. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You are very familiar with the architecture of the software, the functions of each class and method in the software. You main responsibilities include examining the given information to locate the possible buggy classes and buggy methods. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote>
Online Companion: A Preference Informed Energy Sharing Framework for a Centralized Energy Community
<p>This manuscript serves as an electronic companion to [1]. We present the complementary input data, including prosumers target demand, PV power generation, and electricity market prices.</p><p> </p><p> </p>
DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification
<p><strong>The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potentialfor advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology. </strong></p>
Dataset with determinants or factors influencing graduate economics student preparation and success in an online environment
<p>The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at <a href="https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY">https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY</a>. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed. </p> <p>The study reported in this paper employed the mixed methods approach comprising a quantitative and qualitative analysis. The quantitative and econometric analysis of the dependent variable, namely, the final marks for the research report and the independent variables that explain it. The results show significance in terms of the assignments and existing knowledge marks in terms of their bachelor's average mark. We extended the analysis to a qualitative and quantitative survey, which indicated that the mean statistical feedback was above average and therefore strongly agreed/agreed except for library use by the student. Students, therefore, need more guidance in terms of library use and the open questions showed a need for a research methods course in the future. Furthermore, supervision tends to be a significant determinant in all cases. It is also here where supervisors can use social media instruments such as WhatsApp and Facebook to inform students further. This study contributes as the first to investigate the preparation and research skills of students for master's and doctoral studies during the COVID-19 pandemic in an online environment.</p>
Online appendix to State of the CArt: Evaluating Covering Array generators at scale
<p>This dataset includes the input models, figures, and resulting output of all CA generation tools evaluated in the context of the article "State of the CArt: Evaluating Covering Array generators at scale".</p><p>The file `models.tar.gz` includes the 295 input parameter models in four different dialects (ACTS, CTWedge/pMEDICI, PICT, and CASA).</p><p>The file `figures.zip` contains the underlying data of the figures included in the article and the resulting figures (as well as a number of additional visualizations that were not used in the published work).</p><p>The files beginning with `evaluation` contain the output, timing, and error messages produced by each of the competing tools, in one folder per strength (named after a timestamp in ascending order of strength).<br>In each of these folders, files conform to the following syntax:[toolname]_[model_filename]_[strength]_[iteration].[suffix]<br>where "toolname" is identical to the current folder,<br>"model_filename" is the exact name of the model file (including suffix),<br>"strength" is the combinatorial strength of the resulting CA (between 2 and 5),<br>"iteration" takes values between 1 and 3 and refers to the round of execution (at lower strengths, tools were tested using 3 iterations; at higher strengths, some tools required too much execution time and were only run once),<br>"suffix" is one of the following: "time" for the output of GNU time in verbose mode; "out" for the resulting CA (in headerless CSV form); "err" for the standard output (usually empty). Note that additional intermediate files may exist for some tools (e.g. when their original output is not strictly a headerless CSV).</p><p>All tools were executed in Docker containers based on the official OpenJDK image, which is itself based on Debian Buster (Docker identifier: openjdk:14-buster). The experiments were performed on a machine using a Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz (16 physical cores, 32 logical cores) with 256 GB RAM.</p><p>CASAConverterCLI.jar is a standalone Java program that converts CTWedge models to CASA models. It accepts the path to a CTWedge model and the strength as its parameters.</p><p>ct-eval-framework.tar.bz2 contains the evaluation framework itself. It can be used to replicate our results and evaluate your own CA generator. Setup instructions are provided in the README.md file wihtin the archive.</p><p>wca-gcc-11-fix.patch is a patch that enables the compilation of WCA under newer versions of GCC.</p>
Awareness of Lung Cancer Risk Factors and Symptoms in Syria: An Online Cross-Sectional Study
<p><strong>Background:</strong> Globally, lung cancer is the leading cause of cancer fatalities and the second most frequent cancer. Population knowledge of the features of lung cancer is a crucial strategy for early diagnosis and decreasing the mortality rate of lung cancer patients. In this study, we aim to assess the Syrian population's knowledge of lung cancer and its risk factors and to measure awareness of symptoms related to lung cancer. <strong>Methods:</strong> This national cross-sectional study was conducted between October 12 to November 21, 2022 in Syria. We included Syrian people above 18 years from all Syrian governorates. The questionnaire consisted of three categories of questions: sociodemographic information, awareness of lung cancer symptoms, and awareness of lung cancer risk factors. <strong>Results:</strong> Overall, 2251 participants were involved in this research; almost half of them (47.3%) were aged between 21-30 years, and 30.9% indicated they are smoking cigarettes. The overall mean score of knowledge regarding closed questions about risk factors of lung cancer was 4.29; however, the mean score of knowledge regarding open questions about symptoms of lung cancer was 1.52. About half of the study sample (51.3%) indicated that unexplained weight loss is a possible symptom of lung cancer. Our findings showed that cigarette smokers have a lower probability of having adequate knowledge toward lung cancer risk factors than a non-smoker (AOR=0.73, COR=0.68, P-value<0.05). We also defined that females have higher statistically significant odds (AOR=1.3, COR=1.38, P-value<0.05) for being knowledgeable about the symptoms of lung cancer compared to the male sample study. Conclusion: According to our findings, there is inadequate knowledge toward lung cancer risk factors and moderate knowledge of lung cancer symptoms. Along with educational programs to raise public knowledge of the dangers of smoking and other LC risk factors, effective tobacco control policy execution is crucial. </p>
The Relational Structure of Emotional Experience: A Novel Paradigm for Characterizing Differences Between Alexithymic and General Online Participants
<p>Youtube: <a href="https://youtu.be/ChKEnoBKwgA">https://youtu.be/ChKEnoBKwgA</a><br><br>In a pioneering nonverbal study exploring emotional experiences, researchers investigate the nature of alexithymia — a phenomenon where individuals struggle to articulate their emotions. Eschewing traditional verbal reports, the study introduced a unique approach, prompting participants to assess emotional similarities between various stimuli, thereby mapping the "relational structure of emotional experiences". Findings challenge prevailing views on alexithymia, suggesting alexithymic individuals may not just lack words for feelings, but indeed possess distinct emotional experience. This innovative methodology promises not only a deeper understanding of emotional experiences across language and cultural divides but also introduces a potential diagnostic tool for alexithymia.</p>
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
Open the record for dataset details and reuse information.
Figure 1 in Figure 5 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 1 The kinship amcnS individuals cf three matrilineal units within trccp WW-1. The ciocles oepoesent cemales and squaoes oepoesent males. Matoilineal units: HL, honglian unit; CM, changmao unit; BD, bandian unit.
Figure 3 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 3 Box and whiskeo ploqs oc canonical scooes coo Indo-Pacicic humpback dolphins. The squaoes, boxes and whiskeos oepoesenq means, sqandaod eoooos and sqandaod desiaqions, oespecqiselv, in (A) and medians, 25 and 75 quaoqiles and minima-maxima, oespecqiselv, in (B). Indo-Pacicic humpback dolphins coom qhe Jiulong Riseo Esquaov + Peaol Riseo Esquaov %JRE + PRE) and Taiwanese waqeos.
Figure 1 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 1 Map oc qhe squdv aoea. Sampling locaqions oc Indo-Pacicic humpback dolphins analvsed in qhis squdv. The goeen shaded aoea oepoesenqs qhe known disqoibuqion oc qhese dolphins, and qhe oed sqao and vellow ciocles show qhe locaqions coom wheoe qhe holoqvpe and paoaqvpe specimens oc qhe Taiwanese humpback dolphin Sousa chinensis taiwanensis weoe collecqed, oespecqiselv. Some baqhvmeqoic conqouos in qhe Taiwan Sqoaiq aoe also shown.
Figure 4 in On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Figure 4 Foequencv disqoibuqions oc canonical scooes oc Indo-Pacicic humpback dolphins. The disqoibuqions coo Indo-Pacicic humpback dolphins coom Taiwanese waqeos and qhe Jiulong Riseo Esquaov + Peaol Riseo Esquaov aoe shown in goeen and vellow, oespecqiselv. The doqqed lines indicaqe qhe locaqions wheoe 75% oc qhe disqoibuqions aoe cound.
Base de datos estudio Discursos de odio y sesgos de género en videojuegos multijugador online (MMO)
<p>Financiación a cargo del proyecto Androcentrismo, discurso de odio y sesgo de género a través de los videojuegos online en Castilla - La Mancha (32BITS). SBPLY/21/180501/000262, financiado por la Junta de Comunidades de Castilla-La Mancha y cofinanciado por la Unión Europea.</p> <p>Participación y colaboración del proyecto HATEMEDIA (PID2020-114584GB-100), financiado por MCIN/AEI/10.13039/501100011033, en el proceso de elaboración del instrumento.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.