cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23721/100/1504315
title: Contagio Malware Dump

description: Contagio is a collection of the latest malware samples, threats, observations, and analyses. Clean documents are collected from various open sources. Clean files in EXE, XLS(X), DOC(X), RTF, ZIP, 7Z, RAR, JAR, PDF, MACH-O, and ELF file formats. Malicious files in PDF, RTF, XLS, MACH-O, ELF, and JAR file formats.
DOI: 10.15468/4r0kkr
title: Global Register of Introduced and Invasive Species- Nepal

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified checklists (inventories) of introduced (alien) and invasive alien species at the country level. Phase 1 of the project has focused on developing validated and verified checklists (inventories) of countries that are Party to the Convention on Biological Diversity. This annotated checklist (inventory) is of introduced (alien) and invasive alien species known to occur in Nepal.
DOI: 10.13026/7b2j-nq93
title: Smartphone-Captured Chest X-Ray Photographs

description: Recent research has applied deep learning imaging techniques to detect
pulmonary pathology in chest X-rays (CXR) and achieved performance comparable
with radiologists on specific lung abnormalities. Industry has managed to
deploy such artificial intelligence (AI) solutions in on mobile devices to
help clinicians improve their workflow. However, the feasibility of applying
these modern AI algorithms to CXR photographs has yet to be evaluated. In this
project, 6,453 smartphone photographs were taken from the frontal-view CXR
images from two publicly available databases, MIMIC-CXR and CheXpert. We
constructed four derivative CXR photograph datasets ( _Photo-MMC,  Photo-CXP,
Photo-MED, and Photo-DEV_) including photographs taken by several resident
doctors and photographs taken with different devices.
DOI: 10.5281/zenodo.5497446
title: PRASTOF - Results

description: The PRASTOF (PRospective Aluminium STOck and Flows) open-source Python software aims to generate scenarios for future stocks and flows of aluminium alloys and alloying elements in line with the Shared socioeconomic pathways (SSP) framework in order to understanding the stock dynamics of this resource and enhance a better management of it towards a more sustainable and circular economy. A bottom-up approach that considers each final use sector individually was chosen to better depict the details regarding sectorial material demand, lifetimes, and alloys composition. This data set groups all results of PRASTOF. Data and code used to calculate the scenarios are available here: https://github.com/jpedneault/PRASTOF For the article associated with those results, see: Pedneault, J., Majeau-Bettez, G., Pauliuk, S., Margni, M. (2022). Sector-specific scenarios for future stocks and flows of aluminium – An analysis based on Shared Socioeconomic Pathways. Journal of Industrial Ecology.
DOI: 10.6084/m9.figshare.20410992.v1
title: Supporting information: Hawkes et al. (2022) Huge spring migrations of insects from the Middle East to Europe

description: From: Hawkes et al. (2022) Huge spring migrations of insects from the Middle East to Europe: quantifying the migratory assemblage and ecosystem services
DOI: 10.5281/zenodo.7032738
title: Martian atmospheric spectral end-members retrieval from ExoMars Thermal Infrared TIRVIM data

description: Here we investigated new thermal infrared data from the TIRVIM instrument of the ExoMars TGO with the main goal of carefully identifying Martian atmospheric dust and water ice clouds components. A methodology based on principal component and target transformation factor analysis techniques has been applied. Based on our results, this methodology can correctly recover both atmospheric dust and water ice aerosols spectral shapes and their abundances in the Martian atmosphere. The processed TIRVIM data and retrievals used in our work are available here. <br> <br> <strong><em>File uploaded:</em></strong> <strong>Alemanno_2022_Martian_atmospheric_spectral_end-members_retrieval_from_ExoMars_Thermal_Infrared_TIRVIM_data.zip </strong> containing: <strong>Data Set S1</strong> - Alemanno_2022_Martian_atmospheric_spectral_end-members_retrieval_from_ExoMars_Thermal_Infrared_TIRVIM_data <strong>Text S1</strong> - Alemanno_2022_Martian_atmospheric_spectral_end-members_retrieval_from_ExoMars_Thermal_Infrared_TIRVIM_data.README <strong>Data Set S2</strong> - PCA_components <strong>Data Set S3</strong> - PCA_eigenvalues_variance <strong>Data Set S4</strong> - TES_endmembers_orginal_target-transform <strong>Description:</strong> <strong>Data Set S1</strong> – Martian atmospheric spectral end-members retrieved from ExoMars Thermal Infrared TIRVIM data <strong>Text S1</strong> – description of the data contained in Dataset S1 <strong>Data Set S2</strong> – PCA_components retrieved from TIRVIM data <strong>Data Set S3</strong> – PCA eigenvalues variance for each retrieved component of the dataset S2 <strong>Data Set S4</strong> – smoothed TES endmembers target-transformed <strong>If you use this data set in your own work, please cite this DOI:</strong> <strong>10.5281/zenodo.7032738</strong> <strong>Please also cite this work:</strong> <strong>Alemanno G. et al. (2022), Spectral Atmospheric End-Members Retrieval from ExoMars Thermal InfraRed (TIRVIM) Data. 53rd Lunar and Planetary Science Conference, held 7-11 March, 2022 at The Woodlands, Texas. LPI Contribution No. 2678, 2022, id.1885.</strong> <strong>Alemanno G. et al. (2022), Martian atmospheric spectral end-members retrieval from ExoMars Thermal Infrared (TIRVIM) data, JGR Planets, doi: 10.1029/2022JE007429</strong>
DOI: 10.5281/zenodo.7032737
title: Martian atmospheric spectral end-members retrieval from ExoMars Thermal Infrared TIRVIM data

description: Here we investigated new thermal infrared data from the TIRVIM instrument of the ExoMars TGO with the main goal of carefully identifying Martian atmospheric dust and water ice clouds components. A methodology based on principal component and target transformation factor analysis techniques has been applied. Based on our results, this methodology can correctly recover both atmospheric dust and water ice aerosols spectral shapes and their abundances in the Martian atmosphere. The processed TIRVIM data and retrievals used in our work are available here. <br> <br> <strong><em>File uploaded:</em></strong> <strong>Alemanno_2022_Martian_atmospheric_spectral_end-members_retrieval_from_ExoMars_Thermal_Infrared_TIRVIM_data.zip </strong> containing: <strong>Data Set S1</strong> - Alemanno_2022_Martian_atmospheric_spectral_end-members_retrieval_from_ExoMars_Thermal_Infrared_TIRVIM_data <strong>Text S1</strong> - Alemanno_2022_Martian_atmospheric_spectral_end-members_retrieval_from_ExoMars_Thermal_Infrared_TIRVIM_data.README <strong>Data Set S2</strong> - PCA_components <strong>Data Set S3</strong> - PCA_eigenvalues_variance <strong>Data Set S4</strong> - TES_endmembers_orginal_target-transform <strong>Description:</strong> <strong>Data Set S1</strong> – Martian atmospheric spectral end-members retrieved from ExoMars Thermal Infrared TIRVIM data <strong>Text S1</strong> – description of the data contained in Dataset S1 <strong>Data Set S2</strong> – PCA_components retrieved from TIRVIM data <strong>Data Set S3</strong> – PCA eigenvalues variance for each retrieved component of the dataset S2 <strong>Data Set S4</strong> – smoothed TES endmembers target-transformed <strong>If you use this data set in your own work, please cite this DOI:</strong> <strong>10.5281/zenodo.7032738</strong> <strong>Please also cite this work:</strong> <strong>Alemanno G. et al. (2022), Spectral Atmospheric End-Members Retrieval from ExoMars Thermal InfraRed (TIRVIM) Data. 53rd Lunar and Planetary Science Conference, held 7-11 March, 2022 at The Woodlands, Texas. LPI Contribution No. 2678, 2022, id.1885.</strong> <strong>Alemanno G. et al. (2022), Martian atmospheric spectral end-members retrieval from ExoMars Thermal Infrared (TIRVIM) data, JGR Planets, doi: 10.1029/2022JE007429</strong>
DOI: 10.15454/t6ambc/soqznp
title: Citation of the table of Tissues data.txt

description: :unav
DOI: 10.15454/t6ambc/5qbrle
title: Tissues_bovine_Delosiere_2020.tab
DOI: 10.5281/zenodo.6976941
title: Dataset in support of the study "Getting the leaves right matters for estimating temperature extremes"

description: Dataset in support of the study “Getting the leaves right matters for estimating temperature extremes"
DOI: 10.5281/zenodo.7040177
title: VENuS cloud mask training dataset

description: Training dataset for cloud masking for VENμS satellite images. Samples are pixel-wise labelled scenes over the land of Israel, with a special focus on the southern, arid area. Contains three classes: * 0: Cloud-less pixels<br> * 1: Thick clouds<br> * 2: Thin clouds
DOI: 10.5281/zenodo.7040176
title: VENuS cloud mask training dataset

description: Training dataset for cloud masking for VENμS satellite images. Samples are pixel-wise labelled scenes over the land of Israel, with a special focus on the southern, arid area. Contains three classes: * 0: Cloud-less pixels<br> * 1: Thick clouds<br> * 2: Thin clouds
DOI: 10.25574/56750
title: Chandra X-ray Observatory ObsId 56750
DOI: 10.25574/56752
title: Chandra X-ray Observatory ObsId 56752
DOI: 10.25574/56754
title: Chandra X-ray Observatory ObsId 56754
DOI: 10.25574/56756
title: Chandra X-ray Observatory ObsId 56756
DOI: 10.25574/56758
title: Chandra X-ray Observatory ObsId 56758
DOI: 10.25574/56762
title: Chandra X-ray Observatory ObsId 56762
DOI: 10.25574/56765
title: Chandra X-ray Observatory ObsId 56765
DOI: 10.5281/zenodo.7040202
title: Data depository - "Quantifying the effect of wind on volcanic plumes: implications for plume modelling"

description: This depository contains all data to understand, evaluate, and build upon the research reported in the manuscript: "Quantifying the effect of wind on volcanic plumes: implications for plume modelling", submitted to Journal of Geophysical Research. <strong>Abstract</strong> The considerable effects that wind can have on estimates of mass eruption rates (MERs) in explosive eruptions based on volcanic plume height are well known but difficult to quantify rigorously. Many explicitly wind-affected plume models have the additional difficulty that they require the use of centerline heights of bent-over plumes, a parameter not easily obtained directly from observational data. We tested two such models by using the time series of varying plume heights and wind speeds of the 2010 Eyjafjallajökull eruption. The mapped fallout and photos taken during this eruption allow us to estimate the plume geometry and to empirically constrain input parameters for the two models tested. Two strategies are presented to correct the difference in maximum plume height and centerline height: (i) based on plume radius, and (ii) by using the plume type parameter ∏, which quantifies the relative influence of buoyancy and cross-wind on the plume dynamics, to discriminate weak, intermediate and strong plumes. The results indicate that it may be more appropriate to classify plumes as either wind-dominated, intermediate or buoyancy-dominated, where the relative effects of both wind and MER define the type. The analysis of the Eyjafjallajökull data shows that the MER estimates from both models are considerably improved when a plume-type dependent centreline-correction is applied and the wind entrainment coefficient <em>β</em> is refined. For this particular eruption, we find that the best value for <em>β</em> lies between 0.22 and 0.34, unlike previous suggestions that set this parameter to 0.50.
description: When using the provided data or scripts in any form, please cite our corresponding article.
DOI: 10.25574/56781
title: Chandra X-ray Observatory ObsId 56781
DOI: 10.25574/56786
title: Chandra X-ray Observatory ObsId 56786
DOI: 10.25574/56790
title: Chandra X-ray Observatory ObsId 56790
DOI: 10.25574/56792
title: Chandra X-ray Observatory ObsId 56792
DOI: 10.25574/56795
title: Chandra X-ray Observatory ObsId 56795
DOI: 10.25574/56798
title: Chandra X-ray Observatory ObsId 56798
DOI: 10.25574/56802
title: Chandra X-ray Observatory ObsId 56802
DOI: 10.25574/56805
title: Chandra X-ray Observatory ObsId 56805
DOI: 10.25574/56809
title: Chandra X-ray Observatory ObsId 56809
DOI: 10.25574/56810
title: Chandra X-ray Observatory ObsId 56810
DOI: 10.25574/56813
title: Chandra X-ray Observatory ObsId 56813
DOI: 10.25574/56816
title: Chandra X-ray Observatory ObsId 56816
DOI: 10.25574/56818
title: Chandra X-ray Observatory ObsId 56818
DOI: 10.25574/56820
title: Chandra X-ray Observatory ObsId 56820
DOI: 10.25574/56824
title: Chandra X-ray Observatory ObsId 56824
DOI: 10.25574/56826
title: Chandra X-ray Observatory ObsId 56826
DOI: 10.25574/56829
title: Chandra X-ray Observatory ObsId 56829
DOI: 10.25574/56831
title: Chandra X-ray Observatory ObsId 56831
DOI: 10.25574/56834
title: Chandra X-ray Observatory ObsId 56834
DOI: 10.25574/56837
title: Chandra X-ray Observatory ObsId 56837
DOI: 10.25574/56840
title: Chandra X-ray Observatory ObsId 56840
DOI: 10.25574/56842
title: Chandra X-ray Observatory ObsId 56842
DOI: 10.25574/56845
title: Chandra X-ray Observatory ObsId 56845
DOI: 10.25574/56851
title: Chandra X-ray Observatory ObsId 56851
DOI: 10.25574/56853
title: Chandra X-ray Observatory ObsId 56853
DOI: 10.25574/56858
title: Chandra X-ray Observatory ObsId 56858
DOI: 10.25574/56860
title: Chandra X-ray Observatory ObsId 56860
DOI: 10.25574/56864
title: Chandra X-ray Observatory ObsId 56864
DOI: 10.6084/m9.figshare.20765002
title: August 2022 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>August, 2022</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.20765002.v1
title: August 2022 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>August, 2022</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.25574/56866
title: Chandra X-ray Observatory ObsId 56866
DOI: 10.25574/56871
title: Chandra X-ray Observatory ObsId 56871
DOI: 10.25574/56873
title: Chandra X-ray Observatory ObsId 56873
DOI: 10.25574/56876
title: Chandra X-ray Observatory ObsId 56876
DOI: 10.25574/56878
title: Chandra X-ray Observatory ObsId 56878
DOI: 10.25574/56881
title: Chandra X-ray Observatory ObsId 56881
DOI: 10.25574/56883
title: Chandra X-ray Observatory ObsId 56883
DOI: 10.25574/56886
title: Chandra X-ray Observatory ObsId 56886
DOI: 10.25574/56887
title: Chandra X-ray Observatory ObsId 56887
DOI: 10.25574/56890
title: Chandra X-ray Observatory ObsId 56890
DOI: 10.25574/56892
title: Chandra X-ray Observatory ObsId 56892
DOI: 10.25574/56897
title: Chandra X-ray Observatory ObsId 56897
DOI: 10.25574/56899
title: Chandra X-ray Observatory ObsId 56899
DOI: 10.25574/56902
title: Chandra X-ray Observatory ObsId 56902
DOI: 10.25574/56904
title: Chandra X-ray Observatory ObsId 56904
DOI: 10.25574/56907
title: Chandra X-ray Observatory ObsId 56907
DOI: 10.25574/56909
title: Chandra X-ray Observatory ObsId 56909
DOI: 10.25574/56913
title: Chandra X-ray Observatory ObsId 56913
DOI: 10.25574/56915
title: Chandra X-ray Observatory ObsId 56915
DOI: 10.25574/56917
title: Chandra X-ray Observatory ObsId 56917
DOI: 10.25574/56920
title: Chandra X-ray Observatory ObsId 56920
DOI: 10.25574/56924
title: Chandra X-ray Observatory ObsId 56924
DOI: 10.25574/56928
title: Chandra X-ray Observatory ObsId 56928
DOI: 10.25574/56933
title: Chandra X-ray Observatory ObsId 56933
DOI: 10.25574/56935
title: Chandra X-ray Observatory ObsId 56935
DOI: 10.25574/56938
title: Chandra X-ray Observatory ObsId 56938
DOI: 10.25574/56941
title: Chandra X-ray Observatory ObsId 56941
DOI: 10.25574/56943
title: Chandra X-ray Observatory ObsId 56943
DOI: 10.25574/56946
title: Chandra X-ray Observatory ObsId 56946
DOI: 10.25574/56948
title: Chandra X-ray Observatory ObsId 56948
DOI: 10.6084/m9.figshare.20764990
title: Statistical grainsize distribution data

description: An excel table with grainsize distribution data and statistics, in micrometres and phi. 13 samples. Data measured with laser diffraction.
DOI: 10.25574/56950
title: Chandra X-ray Observatory ObsId 56950
DOI: 10.25574/56953
title: Chandra X-ray Observatory ObsId 56953
DOI: 10.25574/56956
title: Chandra X-ray Observatory ObsId 56956
DOI: 10.25574/56959
title: Chandra X-ray Observatory ObsId 56959
DOI: 10.25574/56961
title: Chandra X-ray Observatory ObsId 56961
DOI: 10.25574/56964
title: Chandra X-ray Observatory ObsId 56964
DOI: 10.25574/56967
title: Chandra X-ray Observatory ObsId 56967
DOI: 10.25574/56981
title: Chandra X-ray Observatory ObsId 56981
DOI: 10.25574/56987
title: Chandra X-ray Observatory ObsId 56987
DOI: 10.25574/56989
title: Chandra X-ray Observatory ObsId 56989
DOI: 10.25574/56992
title: Chandra X-ray Observatory ObsId 56992
DOI: 10.25574/56995
title: Chandra X-ray Observatory ObsId 56995
DOI: 10.25574/56997
title: Chandra X-ray Observatory ObsId 56997
DOI: 10.25574/56999
title: Chandra X-ray Observatory ObsId 56999
DOI: 10.17909/qj5k-mm71
title: data used for creating the research note on Stephan's Quintet
DOI: 10.5281/zenodo.6918392
title: Modern pollen data from the East Asian Pollen Database (EAPD): pollen, vegetation and climate relationship

description: This is a modern pollen dataset of eastern Asia, in which a total of 1756 sample sites is selected from the original database EAPD (East Asian Pollen Database) which consists of 2858 samples. The sample types are mainly surface soil, moss, sediment top (lake, delta, peatland, river basin, reservoir and so on), and dust capture. The pollen data are mostly count numbers, but a few was originally given in percentage (marked with TRUE for proportion or percentage). We have checked pollen taxonomic nomenclature and combined some synonym pollen types from different original sources. This dataset includes only the samples collected in the areas under natural vegetation or land cover with low human disturbance, that the sites located in the agriculture areas or strong human intervention have been excluded. This screening procedure makes the pollen data readily available for biome and climate reconstructions. The contributors' original research concerning pollen-climate relationship from EAPD sources have been published in Zheng, et al. (2014 and 2008), which have revealed that pollen taxa in the dataset have significant relationship with climate variables. This dataset is potentially useful for multiscale paleovegetation and paleoclimate reconstruction studies in Asia. EAPD is developed and maintained by the Laboratory of Quaternary Science and Palynology in the School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai, China.
description: The data are only opened for non-profit-making organizations and for scientific research.
description: {"references": ["Zheng, Z., Wei, J., Huang, K., et al., (2014). East Asian pollen database: modern pollen distribution and its quantitative relationship with vegetation and climate. Journal of Biogeography (J. Biogeogr.) (2014) 41, 1819\u20131832", "Zheng Z., Huang K., Xu Q., et al. (2008). Comparison of climatic threshold of geographical distribution between dominant plants and surface pollen in China. Science in China, Series D: Earth Sciences, 2008, 51(8): 1107-1120.", "Zheng, Z., Cour, P., Huang, C., et al. (2007). Dust pollen distribution on a continental scale and its relation to present-day vegetation along north-south transects in East China. Science in China. Science China, Series D: Earth Sciences, 50 (2) 236-246"]}
DOI: 10.5281/zenodo.7040406
title: Selected boulders at Copernicus central peak from L. Sun and P. Lucey Lunar boulder study work

description: This file contains the location of 54 boulders and soils on Copernicus central peak that were studied by Sun L. and Lucey P. G. in their lunar boulder study paper. This file can be uploaded to the LROC Quickmap website (https://quickmap.lroc.asu.edu/) for checking the location of each boulder and their nearby soils.
DOI: 10.5281/zenodo.7040405
title: Selected boulders at Copernicus central peak from L. Sun and P. Lucey Lunar boulder study work

description: This file contains the location of 54 boulders and soils on Copernicus central peak that were studied by Sun L. and Lucey P. G. in their lunar boulder study paper. This file can be uploaded to the LROC Quickmap website (https://quickmap.lroc.asu.edu/) for checking the location of each boulder and their nearby soils.
DOI: 10.5519/qd.02l1pig0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (636 records)
DOI: 10.48420/17112062
title: MD FEL IN Graphene.csv

description: Radius of gyration and RMSD for QueF-WT/Y5Y over 200ns MD simulation
DOI: 10.48420/17112062.v1
title: MD FEL IN Graphene.csv

description: Radius of gyration and RMSD for QueF-WT/Y5Y over 200ns MD simulation
DOI: 10.48420/17112059
title: QueF-Y5Y and WT with preQ0 GFET.csv

description: Isd of BnCN + QueF-Y5Y reaction. Mean and standard deviation of channels shown.
DOI: 10.48420/17112059.v1
title: QueF-Y5Y and WT with preQ0 GFET.csv

description: Isd of BnCN + QueF-Y5Y reaction. Mean and standard deviation of channels shown.
DOI: 10.48420/17112053.v1
title: BnCN_QueF-WT_GFET.csv

description: Isd of BnCN + QueF-WT reaction. Mean and standard deviation of channels shown.<br>
DOI: 10.48420/17112053
title: BnCN_QueF-WT_GFET.csv

description: Isd of BnCN + QueF-WT reaction. Mean and standard deviation of channels shown.<br>
DOI: 10.48420/17112056.v1
title: R3RC peptide on GFET.csv

description: Isd of peptide with BnCN reaction. Mean and standard deviation of channels shown.
DOI: 10.48420/17112056
title: R3RC peptide on GFET.csv

description: Isd of peptide with BnCN reaction. Mean and standard deviation of channels shown.
DOI: 10.5281/zenodo.7040339
title: SafeExtractorRequiredProjects4RP

description: SafeExtractor replication package required projects
DOI: 10.5281/zenodo.7040338
title: SafeExtractorRequiredProjects4RP

description: SafeExtractor replication package required projects
DOI: 10.6084/m9.figshare.20765173
title: Frontiers_table2.docx

description: Serum data
DOI: 10.6084/m9.figshare.20765173.v1
title: Frontiers_table2.docx

description: Serum data
DOI: 10.48420/17112050
title: BnCN_QueF-Y5Y_GFET.csv

description: Isd of BnCN + QueF-Y5Y reaction. Mean and standard deviation of channels shown.