cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8121089
title: TRIBE AGROset - An agricola visual dataset for object detection

description: This a computer vision dataset for object detection of fruits and trunks in agriculture. In its current stage, the dataset has 22 classes, such as: background arugula_minus9 (arugula in an early stage of development with less than 9 leaves) arugula_plus9 (arugula in an advanced stage of development with more than 9 leaves) carrot carrot_small_leaves coriander coriander_small_leaves coty (lettuce in the starting stage of development, with about two very small leaves) grape_bunch (bunches of full-size grapes) grape_bunch_small (bunches of pee-size grapes) lettuce_minus9 (lettuce in an early stage of development with less than 9 leaves) lettuce_plus9 (lettuce in an advanced stage of development with more than 9 leaves) lettuce_ready (lettuce ready for being harvested) radish_root radish_small_leaves radish_big_leaves spinach spinach_big swiss_chard tomato trunk (vine and forestry trunk) turnip turnip_small_leaves All the images were self-acquired in other datasets, under real-world conditions and using typical visual sensors for agricultural robots and IoT. This dataset is the result of the merging of some other datasets after fixing some annotations. Magalhães, Sandro A. ‘Dataset of Tomato inside Greenhouses for Object Detection in Pascal VOC’. INESC TEC, 2021. https://doi.org/10.25747/pc1e-nk92. Aguiar, André Silva, and Sandro Magalhães. ‘Grape Bunch and Vine Trunk Dataset for Deep Learning Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5139598. Rodrigues, Leandro, Francisco Terra, Sandro Magalhães, Filipe Santos, Pedro Moura, and Mário Cunha. ‘PixelCropRobot Dataset: Images of Vegetables Crops in Different Phenological Stages Taken in Greenhouses’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.7433286. Moreira, Germano, Sandro Augusto Magalhães, Tiago Padilha, Filipe Neves dos Santos, and Mário Cunha. ‘RpiTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596363. Sarmento, José, Filipe Neves Dos Santos, and André Silva Aguiar. ‘VinePoint’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5038646. Magalhães, Sandro Augusto, Germano Moreira, Filipe Neves dos Santos, and Mário Cunha. ‘AgRobTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596799. Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDet -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5213825. Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDetV2 -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection (Augmented Version)’. Zenodo, 2022. https://doi.org/10.5281/ZENODO.7186052.
DOI: 10.5281/zenodo.8121088
title: TRIBE AGROset - An agricola visual dataset for object detection

description: This a computer vision dataset for object detection of fruits and trunks in agriculture. In its current stage, the dataset has 22 classes, such as: background arugula_minus9 (arugula in an early stage of development with less than 9 leaves) arugula_plus9 (arugula in an advanced stage of development with more than 9 leaves) carrot carrot_small_leaves coriander coriander_small_leaves coty (lettuce in the starting stage of development, with about two very small leaves) grape_bunch (bunches of full-size grapes) grape_bunch_small (bunches of pee-size grapes) lettuce_minus9 (lettuce in an early stage of development with less than 9 leaves) lettuce_plus9 (lettuce in an advanced stage of development with more than 9 leaves) lettuce_ready (lettuce ready for being harvested) radish_root radish_small_leaves radish_big_leaves spinach spinach_big swiss_chard tomato trunk (vine and forestry trunk) turnip turnip_small_leaves All the images were self-acquired in other datasets, under real-world conditions and using typical visual sensors for agricultural robots and IoT. This dataset is the result of the merging of some other datasets after fixing some annotations. Magalhães, Sandro A. ‘Dataset of Tomato inside Greenhouses for Object Detection in Pascal VOC’. INESC TEC, 2021. https://doi.org/10.25747/pc1e-nk92. Aguiar, André Silva, and Sandro Magalhães. ‘Grape Bunch and Vine Trunk Dataset for Deep Learning Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5139598. Rodrigues, Leandro, Francisco Terra, Sandro Magalhães, Filipe Santos, Pedro Moura, and Mário Cunha. ‘PixelCropRobot Dataset: Images of Vegetables Crops in Different Phenological Stages Taken in Greenhouses’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.7433286. Moreira, Germano, Sandro Augusto Magalhães, Tiago Padilha, Filipe Neves dos Santos, and Mário Cunha. ‘RpiTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596363. Sarmento, José, Filipe Neves Dos Santos, and André Silva Aguiar. ‘VinePoint’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5038646. Magalhães, Sandro Augusto, Germano Moreira, Filipe Neves dos Santos, and Mário Cunha. ‘AgRobTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596799. Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDet -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5213825. Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDetV2 -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection (Augmented Version)’. Zenodo, 2022. https://doi.org/10.5281/ZENODO.7186052.
DOI: 10.25625/xzj4ui
title: Replication Data for: "Statistical analysis of ENDOR spectra"

description: Reproducion data for the publication "Statistical analysis of ENDOR spectra" available at https://rdr.ucl.ac.uk/articles/software/ENDOR_263/14217068
DOI: 10.25625/roxemh
title: Replication Data for: "Gibbsian polar slice sampling"

description: Replication data for the publiation "Gibbsian Polar Slice Sampling" available at https://github.com/microscopic-image-analysis/Gibbsian_Polar_Slice_Sampling
DOI: 10.25625/416kwf
title: Replication Data for: "Geometric convergence of elliptical slice sampling"

description: Replication data for the publication "Geometric convergence of elliptical slice sampling" available at https://github.com/slava-nat/mcmc-comparison
DOI: 10.25625/v8gh5u
title: Replication Data for: "A Variational View on Statistical Multiscale Estimation"

description: Replication data for the publication "A Variational View on Statistical Multiscale Estimation" available at https://github.com/housenli/MIND and https://github.com/housenli/pyMIND
DOI: 10.25625/nfqqqb
title: Replication Data for: "Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration"

description: Replication data for the publication "Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration" available at https://gin.g-node.org/gollischlab/Liu_Gollisch_2021_RGC_spiketrains_spatial_contrast_model
DOI: 10.25625/gbp8lp
title: Replication Data for: "Linear and nonlinear chromatic integration in the mouse retina"

description: Reproduction data for the publication "Linear and nonlinear chromatic integration in the mouse retina" available at https://gin.g-node.org/gollischlab/Khani_and_Gollisch_2021_RGC_spike_trains_chromatic_integration and https://github.com/gollischlab/ChromaticIntegrationAnalysis.
DOI: 10.25625/nui7yt
title: Replication Data for: "Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli"

description: Replication data for the publication "Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli" available at https://gin.g-node.org/gollischlab/Schreyer_and_Gollisch_2021_salamander_retinal_bipolar_cell_recordings
DOI: 10.25625/i0cezn
title: Replication Data for: "Nonlinear Spatial Integration Underlies the Diversity of Retinal Ganglion Cell Responses to Natural Images"

description: Replication data for the publication "Nonlinear Spatial Integration Underlies the Diversity of Retinal Ganglion Cell Responses to Natural Images" available at https://gin.g-node.org/gollischlab/Karamanlis_Gollisch_2021_RGC_spiketrains_natural_image_encoding
DOI: 10.25625/k2a5it
title: Replication Data for: "Cavity-mediated electron-photon pairs"

description: Replication data for the publication Cavity-mediated electron-photon pairs available at https://zenodo.org/record/6639005#.ZFzJdtJBxhE
DOI: 10.25625/kfrr7a
title: Replication Data for: "Integrated photonics enables continuous-beam electron phase modulation"

description: Replication data for the publication Integrated photonics enables continuous-beam electron phase modulation available at https://zenodo.org/record/5575752#.ZEu7Cy9BwrM
DOI: 10.25625/ux9ooe
title: Replication Data for: "Empirical Optimal Transport between Different Measures Adapts to Lower Complexity"

description: Replication data for the publication Empirical Optimal Transport between Different Measures Adapts to Lower Complexity available at https://gitlab.gwdg.de/staudt1/lca
DOI: 10.25625/ouzbrb
title: Replication Data for: "A Variational View on Statistical Multiscale Estimation"

description: Replication data for the publication A Variational View on Statistical Multiscale Estimation available at https://github.com/housenli/MIND and https://github.com/housenli/pyMIND
DOI: 10.25625/s0vzw0
title: Replication Data for: Colocalization for super-resolution microscopy via optimal transport

description: Replication data for the publication Colocalization for super-resolution microscopy via optimal transport available at https://zenodo.org/record/4553856#.ZGGbVC9BwrM and https://github.com/ctameling/OTC
DOI: 10.5281/zenodo.5796218
title: Data - Coherent combining of low-power optical signals based on optically amplified error feedback

description: This dataset contains measurement data and processing code for the results published in "Coherent combining of low-power optical signals based on optically amplified error feedback". Code for the Micro-controllers used in the work is also attached. This work was funded by the Swedish Research Council (grant VR-2015-00535).
DOI: 10.5281/zenodo.5796219
title: Data - Coherent combining of low-power optical signals based on optically amplified error feedback

description: This dataset contains measurement data and processing code for the results published in "Coherent combining of low-power optical signals based on optically amplified error feedback". Code for the Micro-controllers used in the work is also attached. This work was funded by the Swedish Research Council (grant VR-2015-00535).
DOI: 10.17037/data.00003630
title: Data for: "Humanitarian sector (international non-governmental organisations) support to the community in Goma city/DRC during the COVID-19 pandemic period: expectations and reality"

description: These data were collected in Goma city/DRC by a team from Hope Medical Centre in support of LSHTM team (Dr Mateus Kambale Sahani and prof Susannah Mayhew). Collection was done by an administered survey questionnaire to the community members to explore how humanitarian international organizations operating in Goma city have engaged with the community members to support them coping with the COVID-19 pandemic. The dataset has 16 variables which are all categorical except the participant ID. It contains both independent variables which are the participants' general characteristics (such age group, social group, sex, level of education), and dependent variables which are directly related to interventions from humanitarian organisations or judgement by the community members.
description: The questionnaire was administered with minimum interaction with the research team to clarify the questions for the participants. Participants were recruited voluntarily and have signed consent forms before being included in the study. Only adults of 18 years old and above were recruited in Karisimbi Health Zone of Goma city.
DOI: 10.6084/m9.figshare.24125679
title: NV center characteristic spectrum

description: Characteristic NV center spectrum as observed in our lab for initial testing of (ensemble) diamond sample
DOI: 10.6084/m9.figshare.22762226
title: Data and R code - "Specialist fig-consuming Lepidopterans can inflict costs to reproductive success that are mitigated by ant bodyguards"

description: These files contain the data, metadata and code for the analysis performed in the context of the publication "Specialist fig-consuming Lepidopterans can inflict costs to reproductive success that are mitigated by ant bodyguards".
DOI: 10.6084/m9.figshare.22762226.v2
title: Data and R code - "Specialist fig-consuming Lepidopterans can inflict costs to reproductive success that are mitigated by ant bodyguards"

description: These files contain the data, metadata and code for the analysis performed in the context of the publication "Specialist fig-consuming Lepidopterans can inflict costs to reproductive success that are mitigated by ant bodyguards".
DOI: 10.6084/m9.figshare.22762226.v1
title: Data and R code - "Specialist fig-consuming Lepidopterans can inflict costs to reproductive success that are mitigated by ant bodyguards"

description: These files contain the data, metadata and code for the analysis performed in the context of the publication "Specialist fig-consuming Lepidopterans can inflict costs to reproductive success that are mitigated by ant bodyguards".
DOI: 10.5281/zenodo.8336736
title: The mechanistic origin of amber pigmentation of Perithemis tenera (Say, 1840) wings (Anisoptera: Libellulidae)

description: Animal coloration serves various signaling and non-signaling functions. In damselflies and dragonflies (Odonata), such colors may not only play photoprotective and/or thermoregulatory roles but also serve as visual signals during courtship and/or agonistic interactions. Here, we analyzed the coloration of <em>Perithemis tenera</em> wings, a potential secondary sexual ornament, applying spectrophotometry and visual modeling to gain a deeper understanding of their color mechanisms and functions. The amber coloration of the <em>P. tenera</em> wings results from the interaction of light with both the melanized chitin matrix and possibly ommochrome pigments. This coloration stands out significantly against the dry Brazilian savannah, the natural habitat of <em>P. tenera</em>, making it easily detectable by conspecifics. Additionally, by fitting the absorbance curve of <em>P. tenera</em> wings to the extinction coefficient of different melanins, we deduced that pheomelanin is likely the pigment embedded in the wing’s chitinous matrix. If the presence of pheomelanin is confirmed, a pigment predominantly found in vertebrates and known to be associated with an increased risk of melanoma and Parkinson’s disease, <em>P. tenera</em> could become a valuable species for studying these diseases and their potential links to pigmentation, as well as its roles in insects’ immune systems and cuticle sclerotization.
DOI: 10.5281/zenodo.8336735
title: The mechanistic origin of amber pigmentation of Perithemis tenera (Say, 1840) wings (Anisoptera: Libellulidae)

description: Animal coloration serves various signaling and non-signaling functions. In damselflies and dragonflies (Odonata), such colors may not only play photoprotective and/or thermoregulatory roles but also serve as visual signals during courtship and/or agonistic interactions. Here, we analyzed the coloration of <em>Perithemis tenera</em> wings, a potential secondary sexual ornament, applying spectrophotometry and visual modeling to gain a deeper understanding of their color mechanisms and functions. The amber coloration of the <em>P. tenera</em> wings results from the interaction of light with both the melanized chitin matrix and possibly ommochrome pigments. This coloration stands out significantly against the dry Brazilian savannah, the natural habitat of <em>P. tenera</em>, making it easily detectable by conspecifics. Additionally, by fitting the absorbance curve of <em>P. tenera</em> wings to the extinction coefficient of different melanins, we deduced that pheomelanin is likely the pigment embedded in the wing’s chitinous matrix. If the presence of pheomelanin is confirmed, a pigment predominantly found in vertebrates and known to be associated with an increased risk of melanoma and Parkinson’s disease, <em>P. tenera</em> could become a valuable species for studying these diseases and their potential links to pigmentation, as well as its roles in insects’ immune systems and cuticle sclerotization.
DOI: 10.5519/qd.tsn3y8vy
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5281/zenodo.8297790
title: Inferring shape transformations in a drawing task

description: Data repository relative to the following publication: Schmidt, F., Tiedemann, H., Fleming, R. W., & Morgenstern, Y. (in press). Inferring shape transformations in a drawing task. <em>Memory & Cognition</em>. https://doi.org/10.3758/s13421-023-01452-0 The repository includes several MATLAB demos that show how to load the drawings, test, and sample shapes (Figure4_showdrawpercond.m), and scripts that plot Figure 5 and 6 from the manuscript and compute the statistics (Figure5or6_overall_results.m). The latter demo (Figure5or6_overall_results.m) has the option of running the analysis pipeline on the drawing (through analysisScript1.m) or using precomputed analysis. Note that running the analysis pipeline will require that you have ShapeComp, which you can download here: https://sites.google.com/view/yanivmorgenstern/home/tools/shapecomp If you use this data or code, please cite our paper. For questions or suggestions, please contact Filipp Schmidt (filipp.schmidt@psychol.uni-giessen.de) and/or Yaniv Morgenstern (Yaniv.Morgenstern@gmail.com).
description: The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation—project number 222641018—SFB/TRR 135 TP C1), by the European Research Council (ERC) Consolidator Award 'SHAPE'—project number ERC-2015-CoG-682859 and by 'The Adaptive Mind', funded by the Excellence Program of the Hessian Ministry of Higher Education, Science, Research and Art.
DOI: 10.5281/zenodo.8297789
title: Inferring shape transformations in a drawing task

description: Data repository relative to the following publication: Schmidt, F., Tiedemann, H., Fleming, R. W., & Morgenstern, Y. (in press). Inferring shape transformations in a drawing task. <em>Memory & Cognition</em>. https://doi.org/10.3758/s13421-023-01452-0 The repository includes several MATLAB demos that show how to load the drawings, test, and sample shapes (Figure4_showdrawpercond.m), and scripts that plot Figure 5 and 6 from the manuscript and compute the statistics (Figure5or6_overall_results.m). The latter demo (Figure5or6_overall_results.m) has the option of running the analysis pipeline on the drawing (through analysisScript1.m) or using precomputed analysis. Note that running the analysis pipeline will require that you have ShapeComp, which you can download here: https://sites.google.com/view/yanivmorgenstern/home/tools/shapecomp If you use this data or code, please cite our paper. For questions or suggestions, please contact Filipp Schmidt (filipp.schmidt@psychol.uni-giessen.de) and/or Yaniv Morgenstern (Yaniv.Morgenstern@gmail.com).
description: The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation—project number 222641018—SFB/TRR 135 TP C1), by the European Research Council (ERC) Consolidator Award 'SHAPE'—project number ERC-2015-CoG-682859 and by 'The Adaptive Mind', funded by the Excellence Program of the Hessian Ministry of Higher Education, Science, Research and Art.
DOI: 10.6084/m9.figshare.24125757
title: Effective mechanical potential of cell–cell interaction in tissues harboring cavity and in cell sheet.

description: Source data and movies for "Effective mechanical potential of cell–cell interaction in tissues harboring cavity and in cell sheet toward morphogenesis" by Koyama et al.
DOI: 10.6084/m9.figshare.20480355
title: Innovation and elaboration on the avian tree of life

description: Processed data for reproducing figures and analyses in: "Innovation and elaboration on the avian tree of life".The corresponding code is available one GitHub: https://github.com/TGuillerme/elaboration_exploration_bird_beaks
DOI: 10.6084/m9.figshare.20480355.v1
title: Innovation and elaboration on the avian tree of life

description: Processed data for reproducing figures and analyses in: "Innovation and elaboration on the avian tree of life".
DOI: 10.6084/m9.figshare.24125766
title: Experiment Input and Output Data

description: The Data contains, NIFTY 50 & Dow Jones Industrial Average historical data from 01/01/2020 to 31/12/2022.
DOI: 10.6084/m9.figshare.24125766.v1
title: Experiment Input and Output Data

description: The Data contains, NIFTY 50 & Dow Jones Industrial Average historical data from 01/01/2020 to 31/12/2022.
DOI: 10.6084/m9.figshare.24125766.v2
title: Experiment Input and Output Data

description: The Data contains, NIFTY 50 & Dow Jones Industrial Average historical data from 01/01/2020 to 31/12/2022.
DOI: 10.5281/zenodo.8337723
title: Coding for 100 files of alt.atheism of the 20 Newsgroups dataset

description: The zip file contains the first (lexicographic ordering) 100 files of the alt.atheism class of the 20 Newsgroup dataset coded to eleven categories.
DOI: 10.5281/zenodo.8337722
title: Coding for 100 files of alt.atheism of the 20 Newsgroups dataset

description: The zip file contains the first (lexicographic ordering) 100 files of the alt.atheism class of the 20 Newsgroup dataset coded to eleven categories.
DOI: 10.6084/m9.figshare.24100680.v1
title: Calculating Disruption Indices at scale with Dimensions - Supplementary Materials

description: This repository contains the Jupyter notebooks and SQL queries for Dimensions on Google BigQuery<br>related to the publication "Dimensions: Calculating Disruption Indices at Scale", M. Pasin & J. Sixt, 2023.<b>ABSTRACT of the related publication:</b>Evaluating the disruptive nature of academic ideas is a new area of research evaluation that moves beyond standard citation-based metrics by taking into account the broader citation context of publications or patents. The "CD index" and a number of related indicators have been proposed in order to characterise mathematically the disruptiveness of scientific publications or patents. This research area has generated a lot of attention in recent years, yet there is no general consensus on the significance and reliability of disruption indices. More experimentation and evaluation would be desirable, however is hampered by the fact that these indicators are expensive and time-consuming to calculate, especially if done at scale on large citation networks. We present a novel method to calculate disruption indices that leverages the Dimensions cloud-based research infrastructure and reduces the computational time taken to produce such indices by an order of magnitude, as well as making available such functionalities within an online environment that requires no set-up efforts. We explain the novel algorithm and describe how its results align with preexisting implementations of disruption indicators. This method will enable researchers to develop, validate and improve mathematical disruption models more quickly and with more precision, thus contributing to the development of this new research area.
DOI: 10.6084/m9.figshare.24105243
title: 5，6-二甲基苯并咪唑

description: 论文中的原始数据
DOI: 10.6084/m9.figshare.24105243.v1
title: Untitled Item

description: Raw data in the paper
DOI: 10.6084/m9.figshare.24105243.v2
title: 5，6-二甲基苯并咪唑

description: 论文中的原始数据