cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5647833
title: Porting DB12 to Python3: Analysis of the scores

description: DB12 has been originally conceived with Python2 to estimate the power of a given CPU to run HEP applications. However, since January 2020, Python2 is no longer maintained and we decided to port the code to Python3, which contains several optimizations. In October 2021, we effectively ported DB12 to Python3.9, but the optimizations brought by the language generated discrepancies in the norm score, which is a critical component to evaluate the power of CPUs. We build an analysis tool to mitigate these discrepancies. <strong>Current situation</strong> The current analysis assess the impact of the changes on the norm score and propose 3 different solutions to resolve the issue: Find a constant value that would transform a python3 score into a python2 one: 1.18 seems fine. Pros: simple Cons: not accurate. does fit well with scores computed on Intel, not so well with scores computed on AMD Find one constant value per processor type (Intel/AMD): 1.16 fits well with scores computed on Intel, 1.4 also fits well with scores computed on AMD . Pros: accurate Cons: need to maintain a table and update it with new types of processors, and maybe also with new versions of python Compute a simple linear regression: Pros: accurate Cons: need to run it with many examples to get an accurate model To keep it simple and accurate, we chose to apply the second solution: one constant value per processor type.<br> These constants are part of the code and are located in <em>src/db12/factors.json</em>. These values will need to be updated through time, according to the evolution of the CPUs and Python.<br> If you need to get an accurate DB12 norm score using a Python version or a CPU that has not be taken into account,<br> then you have to run the analysis with new data following the next steps. <strong>Run the analysis</strong> Execute the Jupyter Notebook: <pre><code class="language-bash">jupyter notebook DB12Analysis.ipynb</code></pre> <strong>Include new data using DIRAC</strong> Install a DIRAC client: <pre><code class="language-bash">lb-dirac lhcb-proxy-init</code></pre> Go to <em>resources/tools</em> and submit jobs: <pre><code class="language-bash">./submit.sh &lt;number of jobs&gt; &lt;list of sites&gt;</code></pre> Once the jobs are done, add their IDs in `jobIDs.csv` to get the results: <pre><code class="language-bash">python getDB12Scores.py</code></pre> You will obtain `results.json`, you can sort the file to get a better look at it with `jq`: <pre><code class="language-bash">cat results.json | jq . &gt; results_sorted_&lt;date&gt;.json</code></pre> You can finally remove the jobs that are still pending in the queues
DOI: 10.5517/ccdc.csd.cc2czd5y
title: CCDC 2203950: Experimental Crystal Structure Determination

description: Related Article: Jin Zhongxin|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2czd6z
title: CCDC 2203951: Experimental Crystal Structure Determination

description: Related Article: Jin Zhongxin|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15454/mkm1p4/j8wjes
title: lactation_stages_4654_proteins_Delosiere.tab

description: The “lactation stages” dataset informs on proteins detected during a specific or several lactation stages.
DOI: 10.15454/mkm1p4/tjg5jf
title: Understanding the table of Lactation stages.txt

description: Information to re-use the table on Lactation stages
DOI: 10.15454/mkm1p4/uu6gfd
title: lactation_stages_4654_proteins_Delosiere2.tab

description: The “lactation stages” dataset informs on proteins detected during a specific or several lactation stages.
DOI: 10.15454/sujjsq/1lv4yq
title: milk_fractions_4654_proteins_Delosiere.tab

description: The “milk fractions” dataset informs on protein location in a specific or several milk fractions.
DOI: 10.15454/sujjsq/2a2cf1
title: milk_fractions_4654_proteins_Delosiere2.tab

description: The “milk fractions” dataset informs on protein location in a specific or several milk fractions.
DOI: 10.15454/sujjsq/mgrm3o
title: Understanding the table of Milk fractions.txt

description: Information to re-use the table of Milk fractions
DOI: 10.15454/ligcqe/hkayfl
title: NAF_Table S1 _ metabolites_content_FW.csv

description: Metabolomic data - tomato fruit (wild type and GMO) - units per fresh weight of pericarp powder
DOI: 10.15454/ligcqe/mzolwd
title: NAF_Table S1 _enzyme_activity.csv

description: Enzymatic data - tomato fruit (wild type and GMO)
DOI: 10.15454/ligcqe/c5u9zu
title: NAF_Table S1 _ metabolites_content_DW.csv

description: Metabolomic data - tomato fruit (wild type and GMO) - units per dry weight of pericarp powder
DOI: 10.6084/m9.figshare.20710870
title: results

description: Methylation data
DOI: 10.6084/m9.figshare.20710870.v1
title: results

description: Methylation data
DOI: 10.6084/m9.figshare.20710870.v2
title: results

description: Methylation data
DOI: 10.15454/ku1p28/pjpdmh
title: runtests.jl

description: :unav
DOI: 10.15454/ku1p28/xkl1hn
title: README.md

description: :unav
DOI: 10.15454/ku1p28/neifjh
title: extract.jl

description: :unav
DOI: 10.15454/ku1p28/51ajco
title: msscans.jl

description: :unav
DOI: 10.15454/ku1p28/maakcd
title: types.jl

description: :unav
DOI: 10.15454/ku1p28/krhykd
title: plots.jl

description: :unav
DOI: 10.15454/ku1p28/6t83ir
title: isotopes.jl

description: :unav
DOI: 10.15454/ku1p28/2b8lya
title: Project.toml

description: :unav
DOI: 10.15454/ku1p28/q069sf
title: isotopes-data.jl

description: :unav
DOI: 10.15454/ku1p28/mhwris
title: utilities.jl

description: :unav
DOI: 10.15454/ku1p28/n4fxys
title: mzxml.jl

description: :unav
DOI: 10.15454/ku1p28/los9zl
title: process.jl

description: :unav
DOI: 10.15454/ku1p28/pp52ly
title: Io.jl

description: :unav
DOI: 10.15454/ku1p28/tkktbr
title: MassJ.jl

description: :unav
DOI: 10.6084/m9.figshare.20710957.v1
title: Whole-transcriptomic sequencing data of esophageal squamous cell carcinoma patients in Kazakhstan

description: All tables
DOI: 10.5281/zenodo.6362616
title: Dataset- Barking up the right tree: Using tree bark to track airborne particles in school environment and link science to society

description: Experimental data regarding magnetic properties measured in bio-sensors and air filters. Bio-sensors include tree bark samples, either collected in situ (courtyard tree bark) and exposed indoors-outdoors (bio-sensors) in the schools. Bio-sensors had their magnetic properties measured. PM1, EC and OC concentrations and SIRM measured on air filters.
DOI: 10.5281/zenodo.6545509
title: BATS dataset

description: <pre>Paper: Predicting Patch Correctness Based on the Similarity of Failing Test Cases Collected patches</pre>
DOI: 10.5281/zenodo.6545510
title: BATS dataset

description: <pre>Paper: Predicting Patch Correctness Based on the Similarity of Failing Test Cases Collected patches</pre>
DOI: 10.5281/zenodo.7030964
title: Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback

description: This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files. Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article.
DOI: 10.5281/zenodo.7030963
title: Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback

description: This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files. Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article.
DOI: 10.15468/hg5zzh
title: Checklist of lepidopteran of the Dvorichanskyi National Nature Park

description: List of lepidopteran species found in the territory of the Dvorichanskyi National Nature Park
DOI: 10.15468/d8sn6s
title: Checklist of lichens and lichenicolous fungi of Provalskyi steppe (Luhansk Nature Reserve of NASU, Ukraine)

description: List of species of lichens and lichenicolous fungi that are found in the territory of the Provalskyi steppe (Luhansk Nature Reserve of NASU, Ukraine)
DOI: 10.15468/pfk55k
title: Checklist of lichens and lichenicolous fungi of Tryokhizbenskyi steppe (Luhansk Nature Reserve of NASU, Ukraine)

description: List of species of lichens and lichenicolous fungi that are found in the territory of the Tryokhizbenskyi steppe (Luhansk Nature Reserve of NASU, Ukraine)
DOI: 10.15468/8qcacg
title: Checklist of species of animals of territory of Chornobyl Radiation and Ecological Biosphere Reserve

description: Checklist of species of animals of territory of Chornobyl Radiation and Ecological Biosphere Reserve
DOI: 10.15468/buqtak
title: Checklist of lichens and lichenicolous fungi of Stanychno-Luhanske compartment (Luhansk Nature Reserve of NASU, Ukraine)

description: List of species of lichens and lichenicolous fungi that are found in the territory of the Stanychno-Luhanske compartment (Luhansk Nature Reserve of NASU, Ukraine)
DOI: 10.15468/gsmxqb
title: Сhecklist of flora of the territory of Chornobyl Radiation and Ecological Biosphere Reserve

description: Сhecklist data of flora of the territory of Chornobyl Radiation and Ecological Biosphere Reserve
DOI: 10.5281/zenodo.4679159
title: Maps and R code from: Marginality indices for biodiversity conservation in forest trees

description: This dataset provides the raster map of marginality indices for eight European tree species as described in the article "Marginality indices for biodiversity conservation in forest tree". It also provided the R code to compute the marginality indices. The eight species are the following: <em>Abies alba</em> Mill. (silver fir) <em>Fagus sylvatica</em> L. (European beech) <em>Picea abies</em> (L.) H.Karst. (Norway spruce) <em>Pinus halepensis</em> Mill. (Aleppo pine) <em>Pinus nigra</em> J.F.Arnold (black pine) <em>Pinus pinaster</em> Aiton (maritime pine) <em>Pinus pinea</em> L. (stone pine) <em>Pinus sylvestris</em> L. (Scots pine) For each species, a 7z archive of a multi-layered image of the raster maps of the marginality indices is given. Each image has 12 layers: Layer 1: distribution map of the species Layer 2: map of the probability of being marginal according to the Maxent model using eight marginality indices and countries as predictors Layer 3: map of the environmental marginality index, defined as the z-transform of the suitability of each location as predicted by a species distribution model using climatic variables as predictors Layer 4: map of the segmented distribution according to morphological spatial pattern analysis Layer 5: map of the area index Layer 6: map of the gravity index Layer 7: map of the centroid index Layer 8: map of the edge index Layer 9: map of the isolation index Layer 10: map of the second nearest core index Layer 11: map of the north/south index Layer 12: map of the east/west index
description: This data is based upon work from COST Action FP1202 "Strengthening conservation: a key issue for adaptation of marginal/peripheral populations of forest trees to climate change in Europe" (MaP-FGR), supported by COST (European Cooperation in Science and Technology).
DOI: 10.5281/zenodo.4679158
title: Maps and R code from: Marginality indices for biodiversity conservation in forest trees

description: This dataset provides the raster map of marginality indices for eight European tree species as described in the article "Marginality indices for biodiversity conservation in forest tree". It also provided the R code to compute the marginality indices. The eight species are the following: <em>Abies alba</em> Mill. (silver fir) <em>Fagus sylvatica</em> L. (European beech) <em>Picea abies</em> (L.) H.Karst. (Norway spruce) <em>Pinus halepensis</em> Mill. (Aleppo pine) <em>Pinus nigra</em> J.F.Arnold (black pine) <em>Pinus pinaster</em> Aiton (maritime pine) <em>Pinus pinea</em> L. (stone pine) <em>Pinus sylvestris</em> L. (Scots pine) For each species, a 7z archive of a multi-layered image of the raster maps of the marginality indices is given. Each image has 12 layers: Layer 1: distribution map of the species Layer 2: map of the probability of being marginal according to the Maxent model using eight marginality indices and countries as predictors Layer 3: map of the environmental marginality index, defined as the z-transform of the suitability of each location as predicted by a species distribution model using climatic variables as predictors Layer 4: map of the segmented distribution according to morphological spatial pattern analysis Layer 5: map of the area index Layer 6: map of the gravity index Layer 7: map of the centroid index Layer 8: map of the edge index Layer 9: map of the isolation index Layer 10: map of the second nearest core index Layer 11: map of the north/south index Layer 12: map of the east/west index
description: This data is based upon work from COST Action FP1202 "Strengthening conservation: a key issue for adaptation of marginal/peripheral populations of forest trees to climate change in Europe" (MaP-FGR), supported by COST (European Cooperation in Science and Technology).
DOI: 10.5281/zenodo.7031099
title: Data and script for: "Chronic refined carbohydrate consumption and variation in cognitive performance in healthy people"

description: Data and script for statistical analyses. Data: data.txt Script: script.Rmd
DOI: 10.5281/zenodo.7031098
title: Data and script for: "Chronic refined carbohydrate consumption and variation in cognitive performance in healthy people"

description: Data and script for statistical analyses. Data: data.txt Script: script.Rmd
DOI: 10.15468/bz7c6s
title: Checklist of fish species of the National Nature Park “Pyriatynskyi”

description: List of fish species found in the territory of the National Nature Park “Pyriatynskyi”
DOI: 10.15468/hvn9bv
title: Checklist of lichens and lichenicolous fungi of Striltsivsky steppe (Luhansk Nature Reserve of NASU, Ukraine)

description: List of species of lichens and lichenicolous fungi that are found in the territory of the Striltsivskyi steppe (Luhansk Nature Reserve of NASU, Ukraine)
DOI: 10.15454/52j8ys/zxykmr
title: Table1.tab

description: This table is linked to the Dataset 1. For each cow, it indicates whether the cow received a non-steroidian anti-inflammatory drug (Ketofen® 10%, 3 mg / kg; CEVA Santé Animale, Libourne, France) (NSAID), or a physiological serum (0,9% NaCl, 3 mL / 100 kg, BIOLUZ, St Jean de Luz, France) (CONTROL). Cow 6664 is present in the Dataset 1, but it did not received LPS as it was lame at the time of the LPS challenge, is then noted as ‘NONE’ in the table.
DOI: 10.15454/52j8ys/m53c95
title: dataset1.tab

description: This dataset contains data for 28 cows monitored for 6 months (5124 cow * day). Due to some missing data, the dataset only contains 107,665 cow*hour observations (12.4% of missing data). Dataset1 comes from INRAE Herbipôle experimental farm (DOI: https://doi.org/10.15454/1.5572318050509348E12). The cows were milked at fixed times twice a day. The food was delivered in the morning then pushed back close to the feeding gates three times in the afternoon. The cows were administered lipopolysaccharide (LPS) in the mammary gland on one day to induce inflammation. This dataset contains events of oestrus, calving, lameness, mastitis, LPS, other disease, disturbance and mixing, but no occurrences of acidosis, accidents or marginal management changes.
DOI: 10.15454/52j8ys/m6ytna
title: dataset4.tab

description: This dataset contains data for 109,800 cow * day with 300 cows for year. Due to some missing data, the dataset only contains 2,177,207 cow*hour observations (17.3% of missing data). Dataset 4 comes from a commercial farm with 300 cows monitored for 12 months. The farm was equipped with an automatic milking system. The food was delivered in the morning and regularly pushed back by a robot. This dataset contains events of oestrus, calving, lameness, mastitis, other disease, accidents, disturbance and marginal management changes, but no occurrences of LPS, acidosis or mixing.
DOI: 10.15454/52j8ys/hdkegy
title: dataset2.tab

description: This dataset contains data for 28 cows monitored during 2 months (60days) (1680 cow * day). Due to some missing data, the dataset only contains 40,246 cow*hour observations (0.2% of missing data). Dataset 2 comes from INRAE Herbipôle experimental farm (DOI: https://doi.org/10.15454/1.5572318050509348E12). The cows were milked at fixed times twice a day, and fed twice a day. Half of the cows received a high-starch diet during 1 month to induce sub-acute ruminal acidosis. This dataset contains events of oestrus, lameness, mastitis, acidosis, other disease, disturbance and marginal management changes, but no occurrences of calving, LPS, accidents or mixing.
DOI: 10.15454/52j8ys/fmo6a8
title: dataset3.tab

description: This dataset contains data for 30 cows monitored for 40 or 41 days, resulting in 1220 cow * day dataset. Due to some missing data, the dataset only contains 26,224 cow*hour observations (10.4% of missing data). Dataset 3 comes from three commercial farms. On two farms, the cows were milked at fixed times twice or three times a day, and food was delivered twice a day or only once in the morning then pushed back after each milking. The third farm was equipped with an automatic milking system, so the cows had no fixed milking times. Food was delivered in the morning and regularly pushed back by a robot. Milk was sampled daily to analyse its progesterone content; oestrus was identified by a sudden drop in progesterone for at least 3 consecutive days. This dataset contains only events of oestrus and no other types of events.
DOI: 10.6084/m9.figshare.20711392
title: Data_2022_Yao_etal.7z

description: The data here are used for the simulations and plottings of the study Yao et al., 2022, in review. The codes could be found through this link: https://github.com/YiYaoVUB/2022_Yao_etal_JAMES
DOI: 10.5281/zenodo.6998375
title: VeriDark DarkReddit+ Authorship Verification Dataset

description: VeriDark (Authorship Verification in the DarkNet) is a benchmark for evaluating authorship analysis methods in a cybersecurity context, by introducing datasets gathered from the DarkNet marketplace forums or from Darknet-related discussions on Reddit. This benchmark contains three datasets for authorship verification and one dataset for authorship identification.
DOI: 10.5281/zenodo.6998374
title: VeriDark DarkReddit+ Authorship Verification Dataset

description: VeriDark (Authorship Verification in the DarkNet) is a benchmark for evaluating authorship analysis methods in a cybersecurity context, by introducing datasets gathered from the DarkNet marketplace forums or from Darknet-related discussions on Reddit. This benchmark contains three datasets for authorship verification and one dataset for authorship identification.
DOI: 10.15454/2hau8r/ix7zmi
title: README

description: README
DOI: 10.6084/m9.figshare.16834671
title: Benchmarking full version of GureKDDCup, UNSW-NB15, and CIDDS-001 NIDS datasets using rolling-origin resampling

description: Network intrusion detection system (NIDS) is a system that analyses network traffic to flag malicious traffic or suspicious activities. Several recent NIDS datasets have been published, however, the lack of baseline experimental results on the full version of datasets had made it difficult for researchers to perform benchmarking. As the train-test distribution of the datasets has yet to be pre-defined by the creators, this further obstruct the researchers to compare the performance unbiasedly across each of the machine classifiers. Moreover, cross-validation resampling scheme have also been addressed in the literatures to be inappropriate in the domain of NIDS. Thus, rolling-origin – a standard resampling technique which is also known as a common cross-validation scheme in the forecasting domain is employed to allocate the training and testing distributions. In this paper, rigorous experiments are conducted on the full version of the three recent NIDS datasets: GureKDDCup, UNSW-NB15, and CIDDS-001. While the datasets chosen might not be the latest available datasets, we have selected them as they include the essential IP address fields which are usually missing or removed due to some sort of privacy concerns. To deliver the baseline empirical results, 10 well-known classifiers from Weka are utilized.
DOI: 10.6084/m9.figshare.16834671.v2
title: Benchmarking full version of GureKDDCup, UNSW-NB15, and CIDDS-001 NIDS datasets using rolling-origin resampling

description: Network intrusion detection system (NIDS) is a system that analyses network traffic to flag malicious traffic or suspicious activities. Several recent NIDS datasets have been published, however, the lack of baseline experimental results on the full version of datasets had made it difficult for researchers to perform benchmarking. As the train-test distribution of the datasets has yet to be pre-defined by the creators, this further obstruct the researchers to compare the performance unbiasedly across each of the machine classifiers. Moreover, cross-validation resampling scheme have also been addressed in the literatures to be inappropriate in the domain of NIDS. Thus, rolling-origin – a standard resampling technique which is also known as a common cross-validation scheme in the forecasting domain is employed to allocate the training and testing distributions. In this paper, rigorous experiments are conducted on the full version of the three recent NIDS datasets: GureKDDCup, UNSW-NB15, and CIDDS-001. While the datasets chosen might not be the latest available datasets, we have selected them as they include the essential IP address fields which are usually missing or removed due to some sort of privacy concerns. To deliver the baseline empirical results, 10 well-known classifiers from Weka are utilized.