cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7041599
title: Automatically Reproducing Timing-Dependent Flaky-Test Failures

description: This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake


 


Contents:


 


Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis


Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz


Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz 


Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven)


Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset).


Output from running FlakeRake on each flaky test in the FlakeFlagger dataset:


For bisection mode: results-bis.tgz


For one-by-one mode: results-obo.tgz


Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz
Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz
Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz 


Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz


Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv
description: Version notes: Version 1 excluded order-dependent tests from the dataset, version 2 includes all 811 flaky tests from the FlakeFlagger dataset.
DOI: 10.5281/zenodo.7601133
title: Automatically Reproducing Timing-Dependent Flaky-Test Failures

description: This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake


 


Contents:


 


Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis


Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz


Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz 


Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven)


Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset).


Output from running FlakeRake on each flaky test in the FlakeFlagger dataset:


For bisection mode: results-bis.tgz


For one-by-one mode: results-obo.tgz


Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz
Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz
Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz 


Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz


Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv
description: Version notes: Version 1 excluded order-dependent tests from the dataset, version 2 includes all 811 flaky tests from the FlakeFlagger dataset.
DOI: 10.6084/m9.figshare.25041053
title: Tree Species Masks with high-resolution Orthomosaics

description: <pre>Tree Species Masks with high-resolution Orthomosaics<br><br>One part of this dataset is mainly a subset of the KIT FORTRESS dataset.The other two datasets are from the university of bamberg.<br><br>This dataset contains orthomosaics (TIFF) in the size of one hectare with corresponding mask images (TIFF), in which each pixel represents a specific tree species or only the background, and finally predictions of individual tree crowns of these orthomosaics in a vector format (GPKG).</pre>
DOI: 10.6084/m9.figshare.25041053.v1
title: Tree Species Masks with high-resolution Orthomosaics

description: <pre>Tree Species Masks with high-resolution Orthomosaics<br><br>One part of this dataset is mainly a subset of the KIT FORTRESS dataset.The other two datasets are from the university of bamberg.<br><br>This dataset contains orthomosaics (TIFF) in the size of one hectare with corresponding mask images (TIFF), in which each pixel represents a specific tree species or only the background, and finally predictions of individual tree crowns of these orthomosaics in a vector format (GPKG).</pre>
DOI: 10.34740/kaggle/dsv/7456466
title: Lockdown Dataset
DOI: 10.5281/zenodo.4148788
title: 2019 EMDataResource Model Metrics Challenge Dataset

description: This is the full dataset of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org, challenges.emdataresource.org, model-compare.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 Å) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several new metrics were introduced.


File Descriptions:



	
2019-EMDataResource-Challenge-web.pdf: Archive of News, Goals, Timeline, Targets, Modelling Instructions, Process, FAQ, Submission Instructions, Submission Summary Statistics source from the EMDR Challenges website
	
correlation-images.tar.gz: Pairwise correlation tables for selected metric scores from the EMDR Model Compare website
	
maps.tar.gz: The maps used for Fit-to-Map analyses in the Challenge
	
models.tar.gz: The 63 models submitted by the modelling teams
	
results.tar.gz: The output logs for all of the analysis methods
	
Scores.xlsx: Scores for each model and analysis method, compiled into spreadsheet format
	
targets.tar.gz: The reference models used in the analysis



Post submission correction to the web archive PDF document: The full list of EMDataResource members on the model committee is as follows: Cathy Lawson, Andriy Kryshtafovych, Greg Pintilie, Mike Schmid, Helen Berman, Wah Chiu.
description: EMDataResource is supported by the US National Institutes of Health (NIH)/National Institute of General Medical Science, R01GM079429.
The following additional grants are acknowledged for participant support. 
JSR and CW: NIH/ R35GM131883.
PDA, PVA, L-WH, JSR, TCT and CW: NIH/P01GM063210.
AS: National Science Foundation (NSF)/MCB-1942763 (CAREER), NIH/R01GM095583. The Singharoy team used supercomputing resources of the OLCF at the Oak Ridge National Laboratory, which is supported by the Office of Science at DOE under Contract No. DE-AC05-00OR22725.
DKihara: NIH/R01GM123055, NIH/R01GM133840, NSF/DMS1614777, NSF/CMMI1825941, NSF/MCB1925643, NSF/DBI2003635, Purdue Institute of Drug Discovery.
JSF: NIH/R01GM123159.
MI: Max Planck Society German Research Foundation/IG 109/1-1.
ACV: Max Planck Society German Research Foundation /FOR-1805.
DKumar: NIH/R37AI36040 and Welch Foundation/Q1279 (PI: BVV Prasad).
DS: NSF/DBI2030381.
TB, CMP, MW: Medical Research Council MR/N009614/1.
APJ, MW: Wellcome Trust 208398/Z/17/Z.
KC: Biotechnology and Biological Sciences Research Council BB/P000517/1.
MW: Biotechnology and Biological Sciences Research Council BB/P000975/1.
DOI: 10.5281/zenodo.4148789
title: 2019 EMDataResource Model Metrics Challenge Dataset

description: This is the full dataset of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org, challenges.emdataresource.org, model-compare.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 Å) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several new metrics were introduced.


File Descriptions:



	
2019-EMDataResource-Challenge-web.pdf: Archive of News, Goals, Timeline, Targets, Modelling Instructions, Process, FAQ, Submission Instructions, Submission Summary Statistics source from the EMDR Challenges website
	
correlation-images.tar.gz: Pairwise correlation tables for selected metric scores from the EMDR Model Compare website
	
maps.tar.gz: The maps used for Fit-to-Map analyses in the Challenge
	
models.tar.gz: The 63 models submitted by the modelling teams
	
results.tar.gz: The output logs for all of the analysis methods
	
Scores.xlsx: Scores for each model and analysis method, compiled into spreadsheet format
	
targets.tar.gz: The reference models used in the analysis



Post submission correction to the web archive PDF document: The full list of EMDataResource members on the model committee is as follows: Cathy Lawson, Andriy Kryshtafovych, Greg Pintilie, Mike Schmid, Helen Berman, Wah Chiu.
description: EMDataResource is supported by the US National Institutes of Health (NIH)/National Institute of General Medical Science, R01GM079429.
The following additional grants are acknowledged for participant support. 
JSR and CW: NIH/ R35GM131883.
PDA, PVA, L-WH, JSR, TCT and CW: NIH/P01GM063210.
AS: National Science Foundation (NSF)/MCB-1942763 (CAREER), NIH/R01GM095583. The Singharoy team used supercomputing resources of the OLCF at the Oak Ridge National Laboratory, which is supported by the Office of Science at DOE under Contract No. DE-AC05-00OR22725.
DKihara: NIH/R01GM123055, NIH/R01GM133840, NSF/DMS1614777, NSF/CMMI1825941, NSF/MCB1925643, NSF/DBI2003635, Purdue Institute of Drug Discovery.
JSF: NIH/R01GM123159.
MI: Max Planck Society German Research Foundation/IG 109/1-1.
ACV: Max Planck Society German Research Foundation /FOR-1805.
DKumar: NIH/R37AI36040 and Welch Foundation/Q1279 (PI: BVV Prasad).
DS: NSF/DBI2030381.
TB, CMP, MW: Medical Research Council MR/N009614/1.
APJ, MW: Wellcome Trust 208398/Z/17/Z.
KC: Biotechnology and Biological Sciences Research Council BB/P000517/1.
MW: Biotechnology and Biological Sciences Research Council BB/P000975/1.
DOI: 10.5281/zenodo.10477275
title: Weather data

description: Weather data related to PV Designing
DOI: 10.5281/zenodo.10519651
title: Weather_data

description: Dataset containing information about Weather data Includes air_temp, Irradiation, wind_speed
DOI: 10.5281/zenodo.10500952
title: Weather_data

description: Dataset containing information about Weather data
DOI: 10.5281/zenodo.10514533
title: Weather_data

description: Dataset containing information about Weather data Includes air_temp, Irradiation, wind_speed
DOI: 10.5281/zenodo.10521670
title: Weather_data

description: Dataset containing information about Weather data Includes air_temp, Irradiation, wind_speed
DOI: 10.5281/zenodo.10551262
title: Weather_data

description: Dataset containing information about Weather data Includes air_temp, Irradiation, wind_speed
DOI: 10.6084/m9.figshare.25041560
title: Supplementary Material for: The combination of SHOX2 and RASSF1A DNA methylation had a diagnostic value in pulmonary nodules and early lung cancer

description: Introduction: The study explored the effects of SHOX2 and RASSF1A DNA methylation in lung cancer (LC). 
Method: Bronchoalveolar lavage fluid (BALF) samples as well as LC and normal adjacent tissues were collected from 72 LC patients and 35 patients with benign pulmonary nodules. Quantitative analysis of SHOX2 and RASSF1A DNA methylation was performed in benign pulmonary nodules and different stages of LC. The diagnostic value of SHOX2 and RASSF1A DNA methylation in LC and benign pulmonary nodules was determined by receiver operating characteristics analysis. Gain/loss-of-function experiments were constructed in LC cells and mouse models of xenograft and pulmonary nodule metastasis. The levels of SHOX2 and transfer-associated genes were tested through qRT-PCR and Western blot. Malignant phenotype of LC cells were assessed by functional experiment. The tumor volume and weight of mice in xenograft models were measured. Pulmonary nodule metastasis was determined through HE staining assay. 5-Azacytidine appeared as a positive control drug.
Result: SHOX2 DNA methylation or RASSF1A DNA methylation had a diagnostic efficiency in pulmonary nodules and early LC, with the two combined had better diagnostic value. SHOX2 expression was upregulated in LC. Similar to 5-Azacytidine, SHOX2 knockdown inhibited LC cell viability, migration and invasion in vitro as well as restrained LC tumorigenesis and pulmonary nodule metastasis in vivo, whereas overexpressed SHOX2 had the opposite effects. 
Conclusion: The combination of SHOX2 and RASSF1A DNA methylation had a diagnostic value in pulmonary nodules and early LC. SHOX2 positively modulated the tumorigenesis and metastasis of LC by regulating DNA methylation processes.
DOI: 10.6084/m9.figshare.25041560.v1
title: Supplementary Material for: The combination of SHOX2 and RASSF1A DNA methylation had a diagnostic value in pulmonary nodules and early lung cancer

description: Introduction: The study explored the effects of SHOX2 and RASSF1A DNA methylation in lung cancer (LC). 
Method: Bronchoalveolar lavage fluid (BALF) samples as well as LC and normal adjacent tissues were collected from 72 LC patients and 35 patients with benign pulmonary nodules. Quantitative analysis of SHOX2 and RASSF1A DNA methylation was performed in benign pulmonary nodules and different stages of LC. The diagnostic value of SHOX2 and RASSF1A DNA methylation in LC and benign pulmonary nodules was determined by receiver operating characteristics analysis. Gain/loss-of-function experiments were constructed in LC cells and mouse models of xenograft and pulmonary nodule metastasis. The levels of SHOX2 and transfer-associated genes were tested through qRT-PCR and Western blot. Malignant phenotype of LC cells were assessed by functional experiment. The tumor volume and weight of mice in xenograft models were measured. Pulmonary nodule metastasis was determined through HE staining assay. 5-Azacytidine appeared as a positive control drug.
Result: SHOX2 DNA methylation or RASSF1A DNA methylation had a diagnostic efficiency in pulmonary nodules and early LC, with the two combined had better diagnostic value. SHOX2 expression was upregulated in LC. Similar to 5-Azacytidine, SHOX2 knockdown inhibited LC cell viability, migration and invasion in vitro as well as restrained LC tumorigenesis and pulmonary nodule metastasis in vivo, whereas overexpressed SHOX2 had the opposite effects. 
Conclusion: The combination of SHOX2 and RASSF1A DNA methylation had a diagnostic value in pulmonary nodules and early LC. SHOX2 positively modulated the tumorigenesis and metastasis of LC by regulating DNA methylation processes.
DOI: 10.17632/f8nwdjvhpf.1
title: Socio-technical interdependencies in urban energy transitions: Rotterdam transitions out of gas towards district heat networks

description: This is an accompanying data folder for the paper entitled "Socio-technical interdependencies in urban energy transitions: Rotterdam transitions out of gas towards district heat networks". This data folder includes the data collection and analysis methods that we have used in our research.
DOI: 10.17632/f8nwdjvhpf.2
title: Systemic enablers and barriers in urban energy transitions: Rotterdam’s substitution of natural gas by district heating

description: This is an accompanying data folder for the paper entitled "Socio-technical interdependencies in urban energy transitions: Rotterdam transitions out of gas towards district heat networks". This data folder includes the data collection and analysis methods that we have used in our research.
DOI: 10.17632/f8nwdjvhpf.3
title: Participative modelling to identify interdependencies in the transition from natural gas to district heating in Rotterdam, the Netherlands

description: This is an accompanying data folder for the paper entitled " Participative modelling to identify interdependencies in the transition from natural gas to district heating in Rotterdam, the Netherlands". This data folder includes the data collection and analysis methods that we have used in our research. Urban heating systems operate in densely populated areas, which gives rise to all kinds of socio-technical interdependencies. Such interdependencies influence pathways towards reaching carbon-neutrality goals, and result in policy resistance. Understanding these influences supports researchers and decision-makers to recognize systemic patterns that affect changes in urban energy systems, for example in terms of policy resistance, bottlenecks and delays during urban transitions. This research investigates the lessons learned from a project conducted in Rotterdam: a high-density city in the Netherlands which is in a unique situation as it is already switching from natural gas to district heating. We use qualitative system dynamics models to recognize indirect consequences from policy interdependencies during Rotterdam’s substitution of natural gas with district heating and understand the relation between more technologically oriented and policy-oriented policies. We use participative modeling techniques to support transition governance in cities. Our findings suggest that, on the one hand, the national and urban strategies in the Netherlands can activate mechanisms that can support cities with district heating systems such as Rotterdam. On the other hand, the same strategies could also lead to a potential rivalry between energy efficiency and energy security policies, which are both crucial goals towards carbon-neutral cities. Participative modelling provides policy makers with an analytical tool to detect systemic dependencies via joint qualitative scenario building, which in turn can be used to prevent competition among different energy policy objectives which may prolong the use of carbon-heavy systems and displace investments from energy efficiency and renewable heating systems.
DOI: 10.17632/f8nwdjvhpf.4
title: District heating with complexity: Anticipating unintended consequences in the transition towards a climate-neutral city in the Netherlands

description: This is an accompanying data folder for the paper entitled " District heating with complexity: Anticipating unintended consequences in the transition towards a climate-neutral city in the Netherlands". This data folder includes the data collection and analysis methods that we have used in our research.

Urban heating systems operate in densely populated areas, which gives rise to all kinds of socio-technical interdependencies. Such interdependencies influence pathways towards reaching carbon-neutrality goals, and result in policy resistance. Understanding these influences supports researchers and decision-makers to recognize systemic patterns that affect changes in urban energy systems, for example in terms of policy resistance, bottlenecks and delays during urban transitions. This research investigates the lessons learned from a project conducted in Rotterdam: a high-density city in the Netherlands which is in a unique situation as it is already switching from natural gas to district heating. We use qualitative system dynamics models to recognize indirect consequences from policy interdependencies during Rotterdam’s substitution of natural gas with district heating and understand the relation between more technologically oriented and policy-oriented policies. We use participative modeling techniques to support transition governance in cities. Our findings suggest that, on the one hand, the national and urban strategies in the Netherlands can activate mechanisms that can support cities with district heating systems such as Rotterdam. On the other hand, the same strategies could also lead to a potential rivalry between energy efficiency and energy security policies, which are both crucial goals towards carbon-neutral cities. Participative modelling provides policy makers with an analytical tool to detect systemic dependencies via joint qualitative scenario building, which in turn can be used to prevent competition among different energy policy objectives which may prolong the use of carbon-heavy systems and displace investments from energy efficiency and renewable heating systems.
DOI: 10.17632/rgns3c82gx.1
title: SAAL1 negatively regulates antiviral innate immunity by suppressing MAVS aggregation

description: The mitochondrial antiviral signaling protein (MAVS) is a crucial adaptor for the innate immune receptor retinoic acid inducible gene 1 in recognizing viral RNA. Upon viral infection, MAVS undergoes lysine 63 (K63)-linked polyubiquitination, mediated by tripartite motif-containing 31 (TRIM31) and subsequently forms prion-like aggregates to activate downstream signaling cascades, ultimately leading to the production of type I interferons (IFNs). We identified serum amyloid A-like 1 (SAAL1) as a negative regulator of MAVS. SAAL1 antagonizes the K63-linked polyubiquitination and aggregation of MAVS by impeding TRIM31 for association with MAVS. Mechanistically, SAAL1 is dephosphorylated at threonine 387 and translocated to cytosol upon SeV infection, subsequently exerting a negative regulatory effect on type I IFN signaling. These findings establish SAAL1 as a modulator of MAVS and uncover mechanisms through which SAAL1 regulates antiviral innate immunity.