cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8093553
title: Nutrient concentration and Discharge for streams of Krycklan Catchment Study (2008-2019)

description: This dataset DB.KCS.Q&Nut comprises all data for discharge (l/s) and nutrient concentration: DOC (ppm), TDN (ppm), DIN (ppb), NO3 (ppb), NH4 (ppb) and PO4 (ppb) for 12 streams of the Kryclan Catchment Study for the period of 2008-2019 used in the manuscript: "Concentration-discharge patterns reveal catchment controls over the stoichiometry of carbon and nutrient supply to boreal streams", to be submitted to a scientific journal shortly after the publication date of this dataset.<br>
description: Steps to Reproduce: We compiled a combination of hydrological and biogeochemical data from 12 streams of the KCS regular monitoring program for the period of 2008-2019 available at https://data.krycklan.se/. Stream water levels were continuously measured using automatic stage loggers at all sites and discharge was estimated using site-specific rating curves. Rating curves were created from salt dilution velocity-area, and time-volume flow measurements covering most of the observed flow range (Karlsen, Grabs, et al. 2016). For nutrient analysis, all samples were collected in acid-washed high-density polyethylene (HDPE) bottles, stored in cold conditions and filtered (0.45 µm) in the lab within 24-72 hours. Filtered subsamples were frozen (-20°C) immediately after subsampling and stored for later analysis of NO3-N, NH4-N, and PO4-P, samples for DOC and TDN were refrigerated (+4°C) and analyzed within 10 days after field collection. The analytical methods for DOC, TDN, NO3- and NH4+ have been described in detail by Blackburn, et.al., (2017). Dissolved inorganic nitrogen (DIN) was calculated as the sum of NO3 (including nitrite) and NH4, while dissolved organic nitrogen (DON) was calculated as the difference between TDN and DIN. Dissolved inorganic phosphorus (DIP) was analyzed as PO4 on a Seal Analytical Autoanalyzer 3 HR using method G-297-03.
DOI: 10.5281/zenodo.8093513
title: Application of AlphaFold on metamorphic proteins - dataset

description: The dataset comprises a set of five structures of metamorphic proteins used for the study.
DOI: 10.6084/m9.figshare.23599101
title: Clinostat data

description: These files contain data about acceleration measure and vibration measurements of clinostat, material property measurements, and cell experiment analysis.
DOI: 10.5281/zenodo.8093512
title: Application of AlphaFold on metamorphic proteins - dataset

description: The dataset comprises a set of five structures of metamorphic proteins used for the study.
DOI: 10.6084/m9.figshare.22829108.v2
title: Supplemental data for the manuscript: Fusion Oncogenes associated Nodal Metastasis are Age Dependent in Young Adult Patients with Thyroid Cancers

description: Supplemental data for the manuscript: Fusion Oncogenes associated Nodal Metastasis are Age Dependent in Young Adult Patients with Thyroid Cancers
DOI: 10.5281/zenodo.8088262
title: Data from the Knowledge Rights 21 Project Retain Institutional Survey 2022

description: Project Retain is a one-year project led by SPARC Europe as part of The Knowledge Rights 21 (KR21) Programme, funded by Arcadia fund (https://www.arcadiafund.org.uk/) which aims to promote change in European copyright, in this case, to support Open Access. Research on rights retention and open licensing in Europe aims to underpin a campaign to develop copyright policy amongst publishers, funders and institutions to support authors. The results of the project Retain are published in the "Opening Knowledge: Retaining Rights and Licensing in Europe 2023" report - https://doi.org/10.5281/zenodo.8084051. Within the project, the survey was conducted with the aim to understand the policies and support research institutions have put in place in a number of areas affecting their researchers.<br> The survey asks about the following topics: 1. Copyright of scientific publications - how intellectual property that protects original works of authorship applies to the written outputs of research.<br> Specifically: Whether the initial ownership of copyright is with authors or their employing organisation What rights are transfered from authors to publisher 2. Author rights retention - the extent to which researchers retain rather than transferring control of their published research 3. Reuse of scientific publications - ensuring that the written outputs of research can be reused, by the community as a whole but also by the original author in new contexts 4. Open licensing - the promotion of licenses which grant permission to access, re-use and redistribute. The full survey questionnaire file is included in appended to the dataset. The survey was disseminated widely across Europe by SPARC Europe and its partner organisations through newsletters, email lists and social media channels in the autumn of 2022. The survey received 238 unique responses with a completion rate of 71%. A total of 92 responses were excluded from the final analysis because: they provided extremely limited or no detail in their responses they answered ‘no’ when asked if they agreed to take part in the survey their response was identifiably a duplicate A total of 146 unique responses were included in the final dataset and analysed. The deposited dataset is anonymised: the personal data and the names and URLs of the institutions are removed or redacted.<br>
DOI: 10.5281/zenodo.8088261
title: Data from the Knowledge Rights 21 Project Retain Institutional Survey 2022

description: Project Retain is a one-year project led by SPARC Europe as part of The Knowledge Rights 21 (KR21) Programme, funded by Arcadia fund (https://www.arcadiafund.org.uk/) which aims to promote change in European copyright, in this case, to support Open Access. Research on rights retention and open licensing in Europe aims to underpin a campaign to develop copyright policy amongst publishers, funders and institutions to support authors. The results of the project Retain are published in the "Opening Knowledge: Retaining Rights and Licensing in Europe 2023" report - https://doi.org/10.5281/zenodo.8084051. Within the project, the survey was conducted with the aim to understand the policies and support research institutions have put in place in a number of areas affecting their researchers.<br> The survey asks about the following topics: 1. Copyright of scientific publications - how intellectual property that protects original works of authorship applies to the written outputs of research.<br> Specifically: Whether the initial ownership of copyright is with authors or their employing organisation What rights are transfered from authors to publisher 2. Author rights retention - the extent to which researchers retain rather than transferring control of their published research 3. Reuse of scientific publications - ensuring that the written outputs of research can be reused, by the community as a whole but also by the original author in new contexts 4. Open licensing - the promotion of licenses which grant permission to access, re-use and redistribute. The full survey questionnaire file is included in appended to the dataset. The survey was disseminated widely across Europe by SPARC Europe and its partner organisations through newsletters, email lists and social media channels in the autumn of 2022. The survey received 238 unique responses with a completion rate of 71%. A total of 92 responses were excluded from the final analysis because: they provided extremely limited or no detail in their responses they answered ‘no’ when asked if they agreed to take part in the survey their response was identifiably a duplicate A total of 146 unique responses were included in the final dataset and analysed. The deposited dataset is anonymised: the personal data and the names and URLs of the institutions are removed or redacted.<br>
DOI: 10.6084/m9.figshare.19317644
title: Supplementary data.zip

description: Raw data on transmittance, emissivity, heat dissipation performance and electrical efficiency of optimal radiative cooling films
DOI: 10.6084/m9.figshare.19317644.v1
title: Supplementary data.zip

description: Raw data on transmittance, emissivity, heat dissipation performance and electrical efficiency of optimal radiative cooling films
DOI: 10.5517/ccdc.csd.cc20q05y
title: CCDC 1868189: Experimental Crystal Structure Determination

description: Related Article: Yanhui Guo, Yunyun Liu, Jie-Ping Wan|2022|Chin.Chem.Lett.|33|855|doi:10.1016/j.cclet.2021.08.003
DOI: 10.5281/zenodo.6327426
title: PROBA-V Super-Resolution dataset

description: The PROBA-V Super-Resolution dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "PROBA-V Super Resolution"</strong>. It contains satellite data from 74 hand-selected regions around the globe at different points in time. The data is composed of radiometrically and geometrically corrected Top-Of-Atmosphere (TOA) reflectances for the RED and NIR spectral bands at <strong>300m</strong> and <strong>100m</strong> resolution in Plate Carrée projection. The <strong>300m</strong> resolution data is delivered as <strong>128x128</strong> grey-scale pixel images, the <strong>100m</strong> resolution data as <strong>384x384</strong> grey-scale pixel images. Additionally, a quality map is provided for each pixel, indicating whether the pixels are concealed (i.e. by clouads, ice, water, missing information, etc.). The goal of the challenge can be described as <strong>Multi-Image Super-resolution</strong>: Construct a single high-resolution image out of a series of more frequent low resolution images. Detailed information about the related competition can be found at https://kelvins.esa.int/proba-v-super-resolution. A publication about the generation of this dataset exists as well: <strong>Märtens M., Izzo D., Krzic A. and Cox D.</strong> "Super-resolution of PROBA-V images using convolutional neural networks." Astrodynamics 3.4 (2019): 387-402. (arxiv version)
description: {"references": ["M\u00e4rtens M., Izzo D., Krzic A. and Cox D. \"Super-resolution of PROBA-V images using convolutional neural networks.\" Astrodynamics 3.4 (2019): 387-402."]}
DOI: 10.5281/zenodo.6327425
title: PROBA-V Super-Resolution dataset

description: The PROBA-V Super-Resolution dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "PROBA-V Super Resolution"</strong>. It contains satellite data from 74 hand-selected regions around the globe at different points in time. The data is composed of radiometrically and geometrically corrected Top-Of-Atmosphere (TOA) reflectances for the RED and NIR spectral bands at <strong>300m</strong> and <strong>100m</strong> resolution in Plate Carrée projection. The <strong>300m</strong> resolution data is delivered as <strong>128x128</strong> grey-scale pixel images, the <strong>100m</strong> resolution data as <strong>384x384</strong> grey-scale pixel images. Additionally, a quality map is provided for each pixel, indicating whether the pixels are concealed (i.e. by clouads, ice, water, missing information, etc.). The goal of the challenge can be described as <strong>Multi-Image Super-resolution</strong>: Construct a single high-resolution image out of a series of more frequent low resolution images. Detailed information about the related competition can be found at https://kelvins.esa.int/proba-v-super-resolution. A publication about the generation of this dataset exists as well: <strong>Märtens M., Izzo D., Krzic A. and Cox D.</strong> "Super-resolution of PROBA-V images using convolutional neural networks." Astrodynamics 3.4 (2019): 387-402. (arxiv version)
description: {"references": ["M\u00e4rtens M., Izzo D., Krzic A. and Cox D. \"Super-resolution of PROBA-V images using convolutional neural networks.\" Astrodynamics 3.4 (2019): 387-402."]}
DOI: 10.5281/zenodo.6327547
title: Spacecraft Pose Estimation Dataset (SPEED)

description: The SPEED dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>"Pose Estimation challenge" </strong>in collaboration<strong> with Stanford Universitiy's Space Rendezvous Lab (SLAB)</strong>. It features images and poses of the Tango spacecraft (PRISMA mission), 12000 of them generated by SLAB's Optical Simulator using a high fidelity texture model and 300 images from the TRON facility, using a physical mock-up model of Tango. The goal of the competition was estimate the relative pose (distance and orientation) from pixel images only. Detailed information about the original competition can be found at https://kelvins.esa.int/satellite-pose-estimation-challenge/ A follow-up competition with a larger and improved dataset <strong>(SPEED+)</strong> is available on Zenodo as well: https://zenodo.org/record/5588480 A publication about the results of the pose estimation challenge has been published as Kisantal, Mate, et al. "Satellite pose estimation challenge: Dataset, competition design, and results." <em>IEEE Transactions on Aerospace and Electronic Systems</em> 56.5 (2020): 4083-4098.
description: {"references": ["Kisantal, Mate, et al. \"Satellite pose estimation challenge: Dataset, competition design, and results.\" IEEE Transactions on Aerospace and Electronic Systems 56.5 (2020): 4083-4098."]}
DOI: 10.5281/zenodo.6327546
title: Spacecraft Pose Estimation Dataset (SPEED)

description: The SPEED dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>"Pose Estimation challenge" </strong>in collaboration<strong> with Stanford Universitiy's Space Rendezvous Lab (SLAB)</strong>. It features images and poses of the Tango spacecraft (PRISMA mission), 12000 of them generated by SLAB's Optical Simulator using a high fidelity texture model and 300 images from the TRON facility, using a physical mock-up model of Tango. The goal of the competition was estimate the relative pose (distance and orientation) from pixel images only. Detailed information about the original competition can be found at https://kelvins.esa.int/satellite-pose-estimation-challenge/ A follow-up competition with a larger and improved dataset <strong>(SPEED+)</strong> is available on Zenodo as well: https://zenodo.org/record/5588480 A publication about the results of the pose estimation challenge has been published as Kisantal, Mate, et al. "Satellite pose estimation challenge: Dataset, competition design, and results." <em>IEEE Transactions on Aerospace and Electronic Systems</em> 56.5 (2020): 4083-4098.
description: {"references": ["Kisantal, Mate, et al. \"Satellite pose estimation challenge: Dataset, competition design, and results.\" IEEE Transactions on Aerospace and Electronic Systems 56.5 (2020): 4083-4098."]}
DOI: 10.5281/zenodo.6327379
title: Mars Express Power Challenge dataset

description: The Mars Express Power Challenge dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "Mars Express Power Challenge"</strong>. It consists of a training set of 3 Martian Year composed of context and electric current measurements data of ESA's Mars Express spacecraft. Goal of the competition was to predict the average electric current per hour. A test set of a fourth Martian year is provided for this purpose. Detailed information about the related competition can be found at https://kelvins.esa.int/mars-express-power-challenge/home/.
DOI: 10.5281/zenodo.6327378
title: Mars Express Power Challenge dataset

description: The Mars Express Power Challenge dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "Mars Express Power Challenge"</strong>. It consists of a training set of 3 Martian Year composed of context and electric current measurements data of ESA's Mars Express spacecraft. Goal of the competition was to predict the average electric current per hour. A test set of a fourth Martian year is provided for this purpose. Detailed information about the related competition can be found at https://kelvins.esa.int/mars-express-power-challenge/home/.
DOI: 10.6084/m9.figshare.19317677.v1
title: Ribo-seq - more data

description: Just a tad more ribo-seq data for Arabidopsis thaliana.
DOI: 10.6084/m9.figshare.19317677
title: Ribo-seq - more data

description: Just a tad more ribo-seq data for Arabidopsis thaliana.
DOI: 10.5281/zenodo.3891249
title: Datasets for "Automatic Design of Mechanical Metamaterial Actuators"

description: <strong>Publication</strong> arXiv preprint: https://arxiv.org/abs/2002.03032 journal version: [add link to journal] <strong>Code</strong> https://github.com/ComplexityBiosystems/metamech https://github.com/complexitybiosystems/metamech_datasets <strong>Datasets</strong> Figure 2: <em>antiparallel_{human,machine}_design.stl</em>: STL files for human design and machine design configurations performing antiparallel movement. Figure 3: <em>crane_scaling_dataframe.csv: </em>raw efficiency values and execution times used to generate Figure 3 Figure 4: <em>eta_random.dat</em>, <em>eta_triang.dat, plot_distrib.gnupot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel d <em><em>eta_vs_displ_random.dat</em>, <em>eta_vs_displ_triang.dat</em></em><em>, eta_vs_displ.gnuplot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel c <em>structures.tar.bz2</em>: all the structures relative to Figure 4 panel d, in a format suitable for visualization with ovito software Figure 5: data files for human and machine generated pairs of pliers in a format suitable for visualization with ovito software. "*.agr" and "*.data" files can be opened with any text editor to directly visualize the raw data. <em>*_rest.data:</em> rest configurations which are represented in grey in Fig. 5a and Fig. 5b. <em>*_F0.01.data</em>: displaced configurations with input force Fext = 0.01 and output spring stiffness kext = 0.01, also reported in Fig. 5a and Fig. 5b. <em>*.agr</em>: dataset for Figure 5c and 5d in xmgrace format. Figure 6: <em>Fig6/Fig6b/*.csv</em>: raw values of efficiency used to generate Figure 6 panel b <em>Fig6/</em><em>Fig6c-training-data/*/*.png: </em>a total of 1.163.733 PNG images, of size 192 x 168 pixels, as described in the methods section of the manuscript, organized in directories corresponding to different runs. <em>Fig6/Fig6c-training-data/metadata.csv: </em>maps each image path to its associated efficiency value <em>Fig6/Fig6c-efficiency-data/eta_{CNN,DEM}.txt</em>: raw values of efficiency used to generate Figure 6 panel c. Figure 7: <em>Fig7/transfer_learning_regression_values.csv: full raw data, including regression coefficients, p-values, slopes and intercepts corresponding to Figure 7 panels i and j</em> <em>Figure S3:</em> <em>orthogonal_{human,machine}_design.stl: STL files for human design and machine design configurations performing orthogonal movement.</em>
DOI: 10.5281/zenodo.3891248
title: Datasets for "Automatic Design of Mechanical Metamaterial Actuators"

description: <strong>Publication</strong> arXiv preprint: https://arxiv.org/abs/2002.03032 journal version: [add link to journal] <strong>Code</strong> https://github.com/ComplexityBiosystems/metamech https://github.com/complexitybiosystems/metamech_datasets <strong>Datasets</strong> Figure 2: <em>antiparallel_{human,machine}_design.stl</em>: STL files for human design and machine design configurations performing antiparallel movement. Figure 3: <em>crane_scaling_dataframe.csv: </em>raw efficiency values and execution times used to generate Figure 3 Figure 4: <em>eta_random.dat</em>, <em>eta_triang.dat, plot_distrib.gnupot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel d <em><em>eta_vs_displ_random.dat</em>, <em>eta_vs_displ_triang.dat</em></em><em>, eta_vs_displ.gnuplot</em>: raw values of efficiency and gnuplot script used to generate Figure 4 panel c <em>structures.tar.bz2</em>: all the structures relative to Figure 4 panel d, in a format suitable for visualization with ovito software Figure 5: data files for human and machine generated pairs of pliers in a format suitable for visualization with ovito software. "*.agr" and "*.data" files can be opened with any text editor to directly visualize the raw data. <em>*_rest.data:</em> rest configurations which are represented in grey in Fig. 5a and Fig. 5b. <em>*_F0.01.data</em>: displaced configurations with input force Fext = 0.01 and output spring stiffness kext = 0.01, also reported in Fig. 5a and Fig. 5b. <em>*.agr</em>: dataset for Figure 5c and 5d in xmgrace format. Figure 6: <em>Fig6/Fig6b/*.csv</em>: raw values of efficiency used to generate Figure 6 panel b <em>Fig6/</em><em>Fig6c-training-data/*/*.png: </em>a total of 1.163.733 PNG images, of size 192 x 168 pixels, as described in the methods section of the manuscript, organized in directories corresponding to different runs. <em>Fig6/Fig6c-training-data/metadata.csv: </em>maps each image path to its associated efficiency value <em>Fig6/Fig6c-efficiency-data/eta_{CNN,DEM}.txt</em>: raw values of efficiency used to generate Figure 6 panel c. Figure 7: <em>Fig7/transfer_learning_regression_values.csv: full raw data, including regression coefficients, p-values, slopes and intercepts corresponding to Figure 7 panels i and j</em> <em>Figure S3:</em> <em>orthogonal_{human,machine}_design.stl: STL files for human design and machine design configurations performing orthogonal movement.</em>
DOI: 10.25345/c5tm7243w
title: MassIVE MSV000089011 - Clethra fimbrita hexano HPLCQTOFMS NEGATIVO
title: estudio de caracterizacion quimica de extractos obtenidos de clethra fimbirata
DOI: 10.7488/c7085775-8edf-4cca-815a-84149da3fc36
title: Establishing sentinel sero-surveillance to monitor the trend of SARS-nCoV-2 infection transmission in the general population in rural Western India

description: This data collected as a part of sentinel sero-surveillance to monitor the trend of Covid-19 antibodies in population in rural part of Western India.
DOI: 10.25380/iastate.18190784
title: Data for: Impact of cropping system diversification on vegetative and reproductive characteristics of waterhemp (Amaranthus tuberculatus)

description: We examined the relationship between waterhemp’s aboveground mass and fecundity under four crop species’ presence within three crop rotation systems: a 2-year sequence of corn and soybean; a 3-year sequence of corn, soybean, and oat intercropped with red clover; and a 4-year sequence of corn, soybean, oat intercropped with alfalfa, and alfalfa. All the rotation systems were treated with conventional or reduced rates of herbicides. We established eighteen linear equations to predict waterhemp’s fecundity from dried aboveground mass in each crop and associated crop management program since measuring the latter allows for quicker estimation of fecundity compared to counting seeds on each individual plant.<br> <br>
DOI: 10.25380/iastate.18190784.v1
title: Data for: Impact of cropping system diversification on vegetative and reproductive characteristics of waterhemp (Amaranthus tuberculatus)

description: We examined the relationship between waterhemp’s aboveground mass and fecundity under four crop species’ presence within three crop rotation systems: a 2-year sequence of corn and soybean; a 3-year sequence of corn, soybean, and oat intercropped with red clover; and a 4-year sequence of corn, soybean, oat intercropped with alfalfa, and alfalfa. All the rotation systems were treated with conventional or reduced rates of herbicides. We established eighteen linear equations to predict waterhemp’s fecundity from dried aboveground mass in each crop and associated crop management program since measuring the latter allows for quicker estimation of fecundity compared to counting seeds on each individual plant.<br> <br>