cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/3f2c7291-408e-4121-bc6c-94e20f36d85a
title: Measurements belonging to the publication: High-frequency gas effusion through nanopores in suspended graphene

description: This folder consist of the measurements of the Pore dependence. The samples are:BLG_12-C : 16 pores of 25 nm diameterBLG_11-C : 8 pores of 50 nm diameterBLG_6-C : 4 pores of 100 nm diameterBLG_8-C : 2 pores of 200 nm diameterBLG_4-C : 1 pores of 400 nm diameterBLG_9-C : 3 pores of 200 nm diameterBLG_10-C : 4 pores of 200 nm diameterBLG_2-C : 1 pores of 200 nm diameter<br><br>And inside each folder there are two subfolders, Data and Results. The Data folder contains the raw data in txt and dat.The Results folder consist on several things: -Images of the results obtained for the sample. -Element folder in which the measurements are ploted by elements. -Pressure folder where the measurements are ploted by pressures. -ResonanceFit folder with the data of the resonance frequency fit, where the first value is A, the second is the resonance, and the third is q-factor. -Model4 folder eith the data of the fit to the model4. This data can have three options: *if it has 2 values: first value a, second value t_th *if it has 5 values, the values are a, t_th, b, t_g, c. *if it has 8 values, the resonace fit values are added at the beginning, so the values are A, w_0, q, a, t_th, b, t_g, c.<br>
DOI: 10.4121/3f2c7291-408e-4121-bc6c-94e20f36d85a.v1
title: Measurements belonging to the publication: High-frequency gas effusion through nanopores in suspended graphene

description: This folder consist of the measurements of the Pore dependence. The samples are:BLG_12-C : 16 pores of 25 nm diameterBLG_11-C : 8 pores of 50 nm diameterBLG_6-C : 4 pores of 100 nm diameterBLG_8-C : 2 pores of 200 nm diameterBLG_4-C : 1 pores of 400 nm diameterBLG_9-C : 3 pores of 200 nm diameterBLG_10-C : 4 pores of 200 nm diameterBLG_2-C : 1 pores of 200 nm diameter<br><br>And inside each folder there are two subfolders, Data and Results. The Data folder contains the raw data in txt and dat.The Results folder consist on several things: -Images of the results obtained for the sample. -Element folder in which the measurements are ploted by elements. -Pressure folder where the measurements are ploted by pressures. -ResonanceFit folder with the data of the resonance frequency fit, where the first value is A, the second is the resonance, and the third is q-factor. -Model4 folder eith the data of the fit to the model4. This data can have three options: *if it has 2 values: first value a, second value t_th *if it has 5 values, the values are a, t_th, b, t_g, c. *if it has 8 values, the resonace fit values are added at the beginning, so the values are A, w_0, q, a, t_th, b, t_g, c.<br>
DOI: 10.4121/384b7cf5-94da-4275-a6d6-a868eca8fec2
title: Dataset underlying the publication: Probing nanomotion of single bacteria with graphene drums

description: Bacteria nanomotion dataset before and after antibiotics. Data are ordered in a MATLAB table where each row represents a single measurement, and the columns contain the following information: raw data, date, antibiotic, antibiotic concentration (0 if no antibiotic is present), drum label. Drum label can be used to identify matching measurements on the same drum before and after antibiotic.
DOI: 10.4121/384b7cf5-94da-4275-a6d6-a868eca8fec2.v1
title: Dataset underlying the publication: Probing nanomotion of single bacteria with graphene drums

description: Bacteria nanomotion dataset before and after antibiotics. Data are ordered in a MATLAB table where each row represents a single measurement, and the columns contain the following information: raw data, date, antibiotic, antibiotic concentration (0 if no antibiotic is present), drum label. Drum label can be used to identify matching measurements on the same drum before and after antibiotic.
DOI: 10.5517/ccdc.csd.cc2c65rj
title: CCDC 2180687: Experimental Crystal Structure Determination

description: Related Article: Sandeep Kumar Thakur, Mandeep Kaur, Krishna Kumar Manar, Manu Adhikari, Angshuman Roy Choudhury, Sanjay Singh|2022|Chem.-Eur.J.|28|e202202237|doi:10.1002/chem.202202237
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: 10.12754/data-2023-0003-01
title: Year inflows statistics

description: The year.inflows_statistics.csv provides some information about the detected inflows. Inflow's number,first_date,last_date,duration [days],total_transport [10^9 kg O2]
DOI: 10.12754/data-2023-0003
title: Oxygen budget and intrusion experiments with tagging of different inflows

description: Purpose: This data allow reproducing the analysis performed in the Naumov et al. "Limited ventilation of the central Baltic Sea due to elevated oxygen consumption" paper.
description: Publication "Limited ventilation of the central Baltic Sea due to elevated oxygen consumption" by Lev Naumov, Thomas Neumann, Hagen Radtke, and H. M. Markus Meier is primarily based on the monthly mean MOM-ERGOM model data, which were generated during the study. The authors studied sources and sinks of oxygen and hydrogen sulfide and their dynamics from 1948 to 2018. Central Baltic Sea’s ventilation by the biggest 29 inflows of the North Sea water was also studied. The overall deoxygenation of the central Baltic Sea was observed. It manifested as a shift from sedimentary to water column oxygen consumption. Overall deoxygenation reduced the
description: README: The dataset is organized as follows:There are 71 separate folders, each representing a specific year (the last four symbols of the folder's name indicate the year).Each folder contains the following files:* rregion_ocean_day3d_monthly.nc - contains monthly mean 3-dimensional fileds;* rregion_ocean_snap3d_monthly.nc - contains the snapshots (not the average values) at the first day of the each month;* ocean_trps_monthly.nc - contains the monthly mean transports (and other terms needed to construct the budget) for selected variables (temperature, salinity, oxygen, hydrogen sulfide);The appearance of the following files depends on the amount of tracked oxygen inflows happened during a specific year:* ocean_colored_reduced_monthly.nc - monthly mean fields representing the marked oxygen tracers (number of fileds depends on number of inflows happened during that year);* ocean_colored_trps_inflow_monthly.nc - monthly mean transports for the marked oxygen from a specific inflow (indicated by ). The number of such files may vary from folder to folder depending on the number of tracked inflows during the year.inflows_statistics.csv provides some information about the detected inflows.
description: PointOfContact: Lev Naumov (lev.naumov@io-warnemuende.de)
DOI: 10.5281/zenodo.7882861
title: Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)

description: Classify video clips with natural scenes of actions performed by people visible in the videos. See the UCF101 Dataset web page: https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101 This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: https://doi.org/10.5281/zenodo.7924745 . Based on this code: https://keras.io/examples/vision/video_classification/ (needs to be updated, if has not yet been already; see the issue: https://github.com/keras-team/keras-io/issues/1342). Testing if data can be downloaded from figshare with `wget`, see: https://github.com/mojaveazure/angsd-wrapper/issues/10 For generating the subset, see this notebook: https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example). I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. Cite this dataset as: Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. https://doi.org/10.48550/arXiv.1212.0402 To download the dataset via the command line, please use: <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre>
description: Subset (subsampling) of UCF101 Action dataset -- top 10 videos
description: {"references": ["Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402."]}
DOI: 10.5281/zenodo.7882860
title: Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)

description: Classify video clips with natural scenes of actions performed by people visible in the videos. See the UCF101 Dataset web page: https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101 This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: https://doi.org/10.5281/zenodo.7924745 . Based on this code: https://keras.io/examples/vision/video_classification/ (needs to be updated, if has not yet been already; see the issue: https://github.com/keras-team/keras-io/issues/1342). Testing if data can be downloaded from figshare with `wget`, see: https://github.com/mojaveazure/angsd-wrapper/issues/10 For generating the subset, see this notebook: https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example). I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. Cite this dataset as: Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. https://doi.org/10.48550/arXiv.1212.0402 To download the dataset via the command line, please use: <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre>
description: Subset (subsampling) of UCF101 Action dataset -- top 10 videos
description: {"references": ["Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402."]}
DOI: 10.25345/c5v40k87n
title: MassIVE MSV000091916 - Post-transcriptional characterization of the vertebrate aging brain sheds light on the origin of protein-transcript decoupling
title: Proteome-wide variation in subcellular localization using LOPIT-DC in killifish brain aging.
DOI: 10.5281/zenodo.7924745
title: Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)

description: Classify video clips with natural scenes of actions performed by people visible in the videos. See the UCF101 Dataset web page: https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101 This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: https://doi.org/10.5281/zenodo.7882861 . Based on this code: https://keras.io/examples/vision/video_classification/ (needs to be updated, if has not yet been already; see the issue: https://github.com/keras-team/keras-io/issues/1342). Testing if data can be downloaded from figshare with `wget`, see: https://github.com/mojaveazure/angsd-wrapper/issues/10 For generating the subset, see this notebook: https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example). I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. Cite this dataset as: Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. https://doi.org/10.48550/arXiv.1212.0402 To download the dataset via the command line, please use: <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
description: Subset (subsampling) of UCF101 Action dataset -- top 5 videos
description: {"references": ["Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402."]}
DOI: 10.5281/zenodo.7924744
title: Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)

description: Classify video clips with natural scenes of actions performed by people visible in the videos. See the UCF101 Dataset web page: https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101 This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: https://doi.org/10.5281/zenodo.7882861 . Based on this code: https://keras.io/examples/vision/video_classification/ (needs to be updated, if has not yet been already; see the issue: https://github.com/keras-team/keras-io/issues/1342). Testing if data can be downloaded from figshare with `wget`, see: https://github.com/mojaveazure/angsd-wrapper/issues/10 For generating the subset, see this notebook: https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example). I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. Cite this dataset as: Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. https://doi.org/10.48550/arXiv.1212.0402 To download the dataset via the command line, please use: <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
description: Subset (subsampling) of UCF101 Action dataset -- top 5 videos
description: {"references": ["Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402."]}
DOI: 10.35078/3cfqpd
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Diamantina | Município: Monjolos

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.34740/kaggle/dsv/5663335
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.22643428.v1
title: Supplementary table 3.

description: Supplementary table 3. Comprehensive information of all identified lipid metabolites.
DOI: 10.6084/m9.figshare.22656418.v1
title: Supplementary table 2.

description: Supplementary table 2. Detailed information on single-copy orthologous PCGs across 15 vertebrate species.
DOI: 10.5281/zenodo.7924893
title: Java Compiler Performance Evaluation

description: A data set containing 7 Java projects for evaluating performance of Java compilers. Included are OracleJDK version 8.0.351, 9.0.4, 10.0.2 and 11.0.17 as well as ExtendJ version 8, 9, 10 and 11. ExtendJ is an open-source Java compiler, more information can be found here: https://extendj.org/ The included scripts measure the memory use and compilation times for these compilers when compiling the projects. Creating this data set was part of a master's thesis at Lund University.
DOI: 10.5281/zenodo.7924892
title: Java Compiler Performance Evaluation

description: A data set containing 7 Java projects for evaluating performance of Java compilers. Included are OracleJDK version 8.0.351, 9.0.4, 10.0.2 and 11.0.17 as well as ExtendJ version 8, 9, 10 and 11. ExtendJ is an open-source Java compiler, more information can be found here: https://extendj.org/ The included scripts measure the memory use and compilation times for these compilers when compiling the projects. Creating this data set was part of a master's thesis at Lund University.
DOI: 10.57967/hf/0638
title: assisted_generation
DOI: 10.35078/jsfihj
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Diamantina | Município: Contagem

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.6084/m9.figshare.22798952
title: FateMap Paper datasets #1 (Goyal et al., 2021, Biorxiv)

description: Datasets from: https://www.biorxiv.org/content/10.1101/2021.12.08.471833v1.full <br> FM01: BRAF-mutant melanoma WM989 treated with vemurafenib (provided here) <br> FM02: BRAF-mutant melanoma WM989 treated with vemurafenib, trametenib and their combination <br> FM03: BRAF-mutant melanoma WM989 treated with vemurafenib, including pre-treatment with DOT1Li <br> FM04: Breast Cancer MDA-MB-231 treated with paclitaxel (provided here) <br> FM05: BRAF-mutant melanoma WM983B treated with vemurafenib (provided here) <br> FM06: BRAF-mutant melanoma WM989 untreated (provided here) <br> FM07: BRAF-mutant melanoma WM989 treated with vemurafenib (continuous and discontinuous) (provided here) <br> FM08: Primary human melanocytes <br> FM09: NRAS-mutant melanoma 3451 treated with trametinib (provided here) <br> FM10: NRAS-mutant melanoma 3623 treated with trametinib (provided here) <br> We have also provided all processed files for all FMs. <br> Other FM's barcoded data are provided at the following DOI: <br>
DOI: 10.6084/m9.figshare.21926323
title: Comparative Health Risk Assessment of Heated Tobacco Products versus Conventional Cigarettes

description: Smoking is the largest preventable cause of death in the world with around eight million estimated premature deaths per year. In response to the harmful effects of conventional tobacco products the tobacco industry has launched a new type of products called Heated tobacco products (HTP) and e-cigarettes, which are considered safer for human health than conventional cigarettes. The research was conducted by searching the scientific literature using platforms “Google scholar,” “PubMed” and “Science Direct.” To compare the non-carcinogenic and carcinogenic risk of HTP products and conventional cigarettes, margin of exposure and lifetime excess cancer risk were calculated using data obtained from a scientific literature search. HTP products have shown a reduced risk to human health compared to the conventional cigarettes, although they still contain compounds that can be dangerous to human health. There is not enough data obtained from independent studies that could safely indicate that these reduced amounts of toxic chemical entities in the composition of HTP do not induce any harmful effect. Further research is needed to determine the harmful effects of HTP aerosol, as well as to tighten the legislation that would limit the production, import and distribution of these products worldwide until their safety for human health is confirmed with a sufficient number of transparent and representative results obtained in independent scientific studies.
DOI: 10.6084/m9.figshare.21926323.v1
title: Comparative Health Risk Assessment of Heated Tobacco Products versus Conventional Cigarettes

description: Smoking is the largest preventable cause of death in the world with around eight million estimated premature deaths per year. In response to the harmful effects of conventional tobacco products the tobacco industry has launched a new type of products called Heated tobacco products (HTP) and e-cigarettes, which are considered safer for human health than conventional cigarettes. The research was conducted by searching the scientific literature using platforms “Google scholar,” “PubMed” and “Science Direct.” To compare the non-carcinogenic and carcinogenic risk of HTP products and conventional cigarettes, margin of exposure and lifetime excess cancer risk were calculated using data obtained from a scientific literature search. HTP products have shown a reduced risk to human health compared to the conventional cigarettes, although they still contain compounds that can be dangerous to human health. There is not enough data obtained from independent studies that could safely indicate that these reduced amounts of toxic chemical entities in the composition of HTP do not induce any harmful effect. Further research is needed to determine the harmful effects of HTP aerosol, as well as to tighten the legislation that would limit the production, import and distribution of these products worldwide until their safety for human health is confirmed with a sufficient number of transparent and representative results obtained in independent scientific studies.
DOI: 10.4121/eb7846f3-a08b-48f9-93d7-ec13279189ba
title: Raw data of "Reproductive biology of Psammogobius knysnaensis and Caffrogobius gilchristi in the Sundays Estuary, South Africa"

description: This data gives details on the measurements that were taken to analyse reproduction of the two study species.
DOI: 10.4121/eb7846f3-a08b-48f9-93d7-ec13279189ba.v1
title: Raw data of "Reproductive biology of Psammogobius knysnaensis and Caffrogobius gilchristi in the Sundays Estuary, South Africa"

description: This data gives details on the measurements that were taken to analyse reproduction of the two study species.