cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13266314
title: Multivariate egg 2019

description: Multivariate maternal effect on offspring telomere erosion during growth<br>
DOI: 10.21258/1560440
title: Inventaire forestier national suisse - Tableau no 902076
DOI: 10.21258/1556740
title: Inventaire forestier national suisse - Tableau no 902117
DOI: 10.21258/1552375
title: Inventaire forestier national suisse - Tableau no 897436
DOI: 10.6084/m9.figshare.13372979
title: test8.zip

description: Fold 8 test set
DOI: 10.6084/m9.figshare.13372979.v1
title: test8.zip

description: Fold 8 test set
DOI: 10.21258/1620613
title: Inventario forestale nazionale svizzero - Tabella n. 899249
DOI: 10.21258/1551657
title: Inventaire forestier national suisse - Tableau no 898817
DOI: 10.21258/1617924
title: Inventario forestale nazionale svizzero - Tabella n. 892907
DOI: 10.21258/1577742
title: Inventario forestale nazionale svizzero - Tabella n. 897867
DOI: 10.24378/exe.2943
title: Sensitivity of Remote Focusing Microscopes to Magnification Mismatch (dataset)

description: This dataset was used to obtain the figures in the Mohanan & Corbett. (2020) article "Sensitivity of Remote Focusing Microscopes to Magnification Mismatch" published in the Journal of Microscopy.
description: MATLAB scripts require MATLAB to run. OME-TIFF files can be opened by multiple viewers, e.g. ImageJ.
description: The article associated with this dataset is available in ORE at: http://hdl.handle.net/10871/124049
DOI: 10.21258/1551779
title: Inventaire forestier national suisse - Tableau no 891689
DOI: 10.21258/1573386
title: Inventaire forestier national suisse - Tableau no 891962
DOI: 10.21258/1545844
title: Inventaire forestier national suisse - Tableau no 897867
DOI: 10.5281/zenodo.4320346
title: Dataset Sistem Rekomendasi Olahraga

description: Data berisi dataset terkait sistem rekomendasi olahraga, file excel sistem rekomendasi, dan file pendukung lainnya
DOI: 10.5281/zenodo.4320345
title: Dataset Sistem Rekomendasi Olahraga

description: Data berisi dataset terkait sistem rekomendasi olahraga, file excel sistem rekomendasi, dan file pendukung lainnya
DOI: 10.21258/1545854
title: Inventaire forestier national suisse - Tableau no 891726
DOI: 10.6084/m9.figshare.13372793
title: SFG spectra of ice nucleating protein

description: SFG and 2D IR Spectra of the ice nucleation protein construct InaZ9R.
DOI: 10.6084/m9.figshare.13372793.v2
title: SFG spectra of ice nucleating protein

description: SFG and 2D IR Spectra of the ice nucleation protein construct InaZ9R.
DOI: 10.6084/m9.figshare.13372793.v1
title: SFG spectra of ice nucleating protein

description: SFG and 2D IR Spectra of the ice nucleation protein construct InaZ9R.
DOI: 10.21258/1602352
title: Inventario forestale nazionale svizzero - Tabella n. 899019
DOI: 10.6084/m9.figshare.13373060.v1
title: Intraspecific leopard

description: These are detection history for intra-specific leopard population obtained using camera traps
DOI: 10.6084/m9.figshare.13373060
title: Intraspecific leopard

description: These are detection history for intra-specific leopard population obtained using camera traps
DOI: 10.21258/1559653
title: Inventaire forestier national suisse - Tableau no 891793
DOI: 10.21258/1600918
title: Inventario forestale nazionale svizzero - Tabella n. 902046
DOI: 10.21258/1586655
title: Inventario forestale nazionale svizzero - Tabella n. 898860
DOI: 10.21258/1593198
title: Inventario forestale nazionale svizzero - Tabella n. 897149
DOI: 10.21258/1588723
title: Inventario forestale nazionale svizzero - Tabella n. 897870
DOI: 10.21258/1572112
title: Inventaire forestier national suisse - Tableau no 898105
DOI: 10.21258/1539352
title: Inventaire forestier national suisse - Tableau no 898957
DOI: 10.21258/1562103
title: Inventaire forestier national suisse - Tableau no 899417
DOI: 10.21258/1608646
title: Inventario forestale nazionale svizzero - Tabella n. 898768
DOI: 10.21258/1563977
title: Inventaire forestier national suisse - Tableau no 891695
DOI: 10.21258/1542425
title: Inventaire forestier national suisse - Tableau no 891963
DOI: 10.21258/1571763
title: Inventaire forestier national suisse - Tableau no 898773
DOI: 10.6084/m9.figshare.13372955
title: test5.zip

description: Fold 5 test set
DOI: 10.6084/m9.figshare.13372955.v2
title: test5.zip

description: Fold 5 test set
DOI: 10.6084/m9.figshare.13372955.v1
title: test5.zip

description: Fold 5 test set
DOI: 10.6084/m9.figshare.13373120
title: Supplementary Material for: Collecting Valid and Reliable Data: Fieldwork Monitoring Strategies in a Health Examination Survey

description: <b><i>Introduction:</i></b> Health surveys constitute a relevant information source to access the population’s health status. Given that survey errors can significantly influence estimates and invalidate study findings, it is crucial that the fieldwork progress is closely monitored to ensure data quality. The objective of this study was to describe the fieldwork monitoring conducted during the first Portuguese National Health Examination Survey (INSEF) regarding protocol deviations and key performance indicators (KPI). <b><i>Methods:</i></b> Data derived from interviewer observation and from the statistical quality control of selected KPI were used to monitor the four components of the INSEF survey (recruitment, physical examination, blood collection and health questionnaire). Survey KPI included response rate, average time distribution for procedures, distribution of the last digit in a specific measure, proportion of haemolysed blood samples and missing values. <b><i>Results:</i></b> Interviewer observation identified deviations from the established protocols, which were promptly corrected. During fieldwork monitoring through KPI, upon implementation of corrective measures, the participation rate increased 2.5-fold, and a 4.4-fold decrease in non-adherence to standardized survey procedures was observed in the average time distribution for blood pressure measurement. The proportion of measurements with the terminal digit of 0 or 5 decreased to 19.6 and 16.5%, respectively, after the pilot study. The proportion of haemolysed samples was at baseline level, below 2.5%. Missing data issues were minimized by promptly communicating them to the interviewer, who could recontact the participant and fill in the missing information. <b><i>Discussion/Conclusion:</i></b> Although the majority of the deviations from the established protocol occurred during the first weeks of the fieldwork, our results emphasize the importance of continuous monitoring of survey KPI to ensure data quality throughout the survey.
DOI: 10.5281/zenodo.4313278
title: Doing Science in Times of Covid-19: A Survey

description: <strong>Abstract</strong> This NWO Corona Fast-Track Data study is a two-wave (non-panel) survey of academic researchers working at Dutch research institutions. Through this survey research we aimed to study scientists’ information gathering and spreading behaviour with regard to the latest scientific information on COVID-19. In the midst of the COVID-19 pandemic, the expertise of researchers from medicine and biology, but also social sciences, humanities and economics is urgently needed to counter misinformation. In order to gain timely and otherwise lost insights on how to improve science communication during this crisis, we collected the data in two survey waves. The first wave survey data were collected between 9 June and 31 August; the second between 26 October and 30 November 2020. <strong>Contents:</strong> - Covid-19 Survey Methodology: here you will find a technical report, demographic tables and some general information about the survey project. - Covid-19 Survey Questionnaire: here you will find the full questionnaire used for the project. - Covid-19 Survey Codebook: here you will find the variables names and their explanation. - June5_August31_2020: here you will find the Wave 1 survey data (June-August 2020) - Oct26_Nov30_2020: here you will find the Wave 2 survey data (October-November 2020) <strong>Funding:</strong> This dataset was funded under the NWO Corona Fast-Track Data scheme. Project id: 440.20.028; Project title: “Collecting systematic survey data on scientists’ information-seeking and information-spreading behaviour in a time of crisis”. <strong>How to cite</strong> Roex, K. & Colavizza, G. (2020). Doing Science in Times of Covid-19: A Survey. [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4313278 .
DOI: 10.5281/zenodo.4313277
title: Doing Science in Times of Covid-19: A Survey

description: <strong>Abstract</strong> This NWO Corona Fast-Track Data study is a two-wave (non-panel) survey of academic researchers working at Dutch research institutions. Through this survey research we aimed to study scientists’ information gathering and spreading behaviour with regard to the latest scientific information on COVID-19. In the midst of the COVID-19 pandemic, the expertise of researchers from medicine and biology, but also social sciences, humanities and economics is urgently needed to counter misinformation. In order to gain timely and otherwise lost insights on how to improve science communication during this crisis, we collected the data in two survey waves. The first wave survey data were collected between 9 June and 31 August; the second between 26 October and 30 November 2020. <strong>Contents:</strong> - Covid-19 Survey Methodology: here you will find a technical report, demographic tables and some general information about the survey project. - Covid-19 Survey Questionnaire: here you will find the full questionnaire used for the project. - Covid-19 Survey Codebook: here you will find the variables names and their explanation. - June5_August31_2020: here you will find the Wave 1 survey data (June-August 2020) - Oct26_Nov30_2020: here you will find the Wave 2 survey data (October-November 2020) <strong>Funding:</strong> This dataset was funded under the NWO Corona Fast-Track Data scheme. Project id: 440.20.028; Project title: “Collecting systematic survey data on scientists’ information-seeking and information-spreading behaviour in a time of crisis”. <strong>How to cite</strong> Roex, K. & Colavizza, G. (2020). Doing Science in Times of Covid-19: A Survey. [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4313278 .
DOI: 10.5281/zenodo.4314574
title: Flexible collaboration on the Nile increases benefits and enhances economic resilience

description: The data that support the findings of this study are available from the corresponding author upon reasonable. The data are not publicly available due to state restrictions and contain information that could compromise research participant privacy/consent.
DOI: 10.5281/zenodo.3928049
title: Flexible collaboration on the Nile increases benefits and enhances economic resilience

description: The data that support the findings of this study are available from the corresponding author upon reasonable. The data are not publicly available due to state restrictions and contain information that could compromise research participant privacy/consent.
DOI: 10.21258/1625498
title: Inventario forestale nazionale svizzero - Tabella n. 902058
DOI: 10.21258/1563519
title: Inventaire forestier national suisse - Tableau no 897210
DOI: 10.21227/2pnb-e632
title: ECG data for deep transfer learning

description: Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks, AlexNet and GoogLeNet, as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using 8 volunteers. The signals are pre-processed using elliptical filter for signal noises like baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECG with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental data set was augmented by adding two different publicly available data sets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel", but also presents a lightweight solution to otherwise computationally heavy problems.
DOI: 10.21227/x6w8-h979
title: ECG data for deep transfer learning

description: Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks, AlexNet and GoogLeNet, as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using 8 volunteers. The signals are pre-processed using elliptical filter for signal noises like baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECG with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental data set was augmented by adding two different publicly available data sets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel", but also presents a lightweight solution to otherwise computationally heavy problems.
DOI: 10.21258/1554746
title: Inventaire forestier national suisse - Tableau no 897049
DOI: 10.21258/1545748
title: Inventaire forestier national suisse - Tableau no 896927
DOI: 10.5517/ccdc.csd.cc1j6bmp
title: CCDC 1406272: Experimental Crystal Structure Determination

description: Related Article: Alexandre Trehoux, Régis Guillot, Martin Clemancey, Geneviève Blondin, Jean-Marc Latour, Jean-Pierre Mahy, Frédéric Avenier|2020|Dalton Trans.|49|16657|doi:10.1039/D0DT03308A
DOI: 10.5517/ccdc.csd.cc24qz95
title: CCDC 1988287: Experimental Crystal Structure Determination

description: Related Article: E. M. Kadilenko, N. P. Gritsan, E. V. Tretyakov, S. V. Fokin, G. V. Romanenko, A. S. Bogomyakov, D. E. Gorbunov, D. Schollmeyer, M. Baumgarten, V. I. Ovcharenko|2020|Dalton Trans.|49|16916|doi:10.1039/D0DT03184D
DOI: 10.5517/ccdc.csd.cc22j7f9
title: CCDC 1922230: Experimental Crystal Structure Determination

description: Related Article: E. M. Kadilenko, N. P. Gritsan, E. V. Tretyakov, S. V. Fokin, G. V. Romanenko, A. S. Bogomyakov, D. E. Gorbunov, D. Schollmeyer, M. Baumgarten, V. I. Ovcharenko|2020|Dalton Trans.|49|16916|doi:10.1039/D0DT03184D
DOI: 10.5517/ccdc.csd.cc22j7d8
title: CCDC 1922229: Experimental Crystal Structure Determination

description: Related Article: E. M. Kadilenko, N. P. Gritsan, E. V. Tretyakov, S. V. Fokin, G. V. Romanenko, A. S. Bogomyakov, D. E. Gorbunov, D. Schollmeyer, M. Baumgarten, V. I. Ovcharenko|2020|Dalton Trans.|49|16916|doi:10.1039/D0DT03184D
DOI: 10.5517/ccdc.csd.cc216f3t
title: CCDC 1883005: Experimental Crystal Structure Determination

description: Related Article: Mehmet Sevim, Serdar Batıkan Kavukcu, Armağan Kınal, Onur Şahin, Hayati Türkmen|2020|Dalton Trans.|49|16903|doi:10.1039/D0DT02937H
DOI: 10.5517/ccdc.csd.cc26hh3b
title: CCDC 2040671: Experimental Crystal Structure Determination

description: Related Article: Subrata Bhattacharjee, Avishek Guin, Rahul N. Gaykar, Akkattu T. Biju|2020|Org.Lett.|22|9097|doi:10.1021/acs.orglett.0c03494
DOI: 10.5517/ccdc.csd.cc25r6v0
title: CCDC 2018312: Experimental Crystal Structure Determination

description: Related Article: Krishna Sundar Das, Baishakhi Pal, Sayan Saha, Sohel Akhtar, Avik De, Partha Pratim Ray, Raju Mondal|2020|Dalton Trans.|49|17005|doi:10.1039/D0DT02589E
DOI: 10.5517/ccdc.csd.cc26hxbz
title: CCDC 2041081: Experimental Crystal Structure Determination

description: Related Article: B.-B. Liang, H.-G. Xiong, W.-Y. Hong, H.-G. Yao|2020|IUCrData|5|x201433|doi:10.1107/S2414314620014339
DOI: 10.5517/ccdc.csd.cc26jgdm
title: CCDC 2041610: Experimental Crystal Structure Determination

description: Related Article: U. Böhme, F. Bitto|2020|IUCrData|5|x201444|doi:10.1107/S2414314620014443
DOI: 10.5517/ccdc.csd.cc26jn4k
title: CCDC 2041788: Experimental Crystal Structure Determination

description: Related Article: Luciano Barluzzi, Rosario Scopelliti, Marinella Mazzanti|2020|J.Am.Chem.Soc.|142|19047|doi:10.1021/jacs.0c09814
DOI: 10.5517/ccdc.csd.cc26jn5l
title: CCDC 2041789: Experimental Crystal Structure Determination

description: Related Article: Luciano Barluzzi, Rosario Scopelliti, Marinella Mazzanti|2020|J.Am.Chem.Soc.|142|19047|doi:10.1021/jacs.0c09814
DOI: 10.5517/ccdc.csd.cc26jn6m
title: CCDC 2041790: Experimental Crystal Structure Determination

description: Related Article: Luciano Barluzzi, Rosario Scopelliti, Marinella Mazzanti|2020|J.Am.Chem.Soc.|142|19047|doi:10.1021/jacs.0c09814
DOI: 10.5517/ccdc.csd.cc26jn7n
title: CCDC 2041791: Experimental Crystal Structure Determination

description: Related Article: Luciano Barluzzi, Rosario Scopelliti, Marinella Mazzanti|2020|J.Am.Chem.Soc.|142|19047|doi:10.1021/jacs.0c09814
DOI: 10.5517/ccdc.csd.cc1j6bnq
title: CCDC 1406273: Experimental Crystal Structure Determination

description: Related Article: Alexandre Trehoux, Régis Guillot, Martin Clemancey, Geneviève Blondin, Jean-Marc Latour, Jean-Pierre Mahy, Frédéric Avenier|2020|Dalton Trans.|49|16657|doi:10.1039/D0DT03308A
DOI: 10.5517/ccdc.csd.cc26k576
title: CCDC 2042287: Experimental Crystal Structure Determination

description: Related Article: Jinwoo Kim, Dongwook Kim, Sukbok Chang|2020|J.Am.Chem.Soc.|142|19052|doi:10.1021/jacs.0c09982
DOI: 10.5517/ccdc.csd.cc26k587
title: CCDC 2042288: Experimental Crystal Structure Determination

description: Related Article: Jinwoo Kim, Dongwook Kim, Sukbok Chang|2020|J.Am.Chem.Soc.|142|19052|doi:10.1021/jacs.0c09982
DOI: 10.5517/ccdc.csd.cc26k598
title: CCDC 2042289: Experimental Crystal Structure Determination

description: Related Article: Jinwoo Kim, Dongwook Kim, Sukbok Chang|2020|J.Am.Chem.Soc.|142|19052|doi:10.1021/jacs.0c09982
DOI: 10.5517/ccdc.csd.cc26n1b8
title: CCDC 2045049: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold|2020|CSD Communication|||
DOI: 10.21258/1581497
title: Inventario forestale nazionale svizzero - Tabella n. 897819
DOI: 10.21258/1577732
title: Inventario forestale nazionale svizzero - Tabella n. 891915
DOI: 10.15472/ezfszu
title: Listado de Flora presente en el Predio la Esperanza, del municipio de Buenavista

description: Este recurso contiene el listado de especies de flora observados en el predio la esperanza del Municipio de Buenavista. Abarca la composición vegetal y florística de coberturas vegetales como guadual, bosque ripario,bosque primario. Este estudio se realizó con el fin de caracterizar el predio para establecer el plan de Manejo Ambiental del predio. Este predio tiene como objetivo la conservación del recurso hídrico del municipio y fue adquirido por el ente territorial bajo la ley 99 de 1993, artículo 111. Esta lista se realizó en el año 2020 y logró identificar la presencia de 61 plantas, distribuidas en 58 especies y 3 géneros.
DOI: 10.21258/1561334
title: Inventaire forestier national suisse - Tableau no 898031
DOI: 10.21258/1611704
title: Inventario forestale nazionale svizzero - Tabella n. 892945
DOI: 10.21258/1549791
title: Inventaire forestier national suisse - Tableau no 892072
DOI: 10.21227/mybp-sr56
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 279,963 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.15472/6eefvf
title: Listado de Flora presente en los Predios Libertad (1 a 5-2), del municipio de Circasia

description: Este recurso contiene el listado de especies de flora observados en los predios Libertad 1,2,3,4,5-1,5-2 adquiridos por el municipio de Circasia. Abarca la composición vegetal y florística de coberturas vegetales como bosque ripario,bosque natural, regeneración Natural Este estudio se realizó con el fin de caracterizar la composición de flora y fauna y realizar el plan de Manejo Ambiental del predio. Este predio tiene como objetivo la conservación del recurso hídrico del municipio y fue adquirido por el ente territorial bajo la ley 99 de 1993, artículo 111. Esta lista se realizó en el año 2020 y logró identificar la presencia de 65 especies Y 3 géneros. Abarcando un total de 4 clases, siendo la más abundante Magnoliopsida con 57 registros.
DOI: 10.21258/1551818
title: Inventaire forestier national suisse - Tableau no 898224
DOI: 10.21258/1536715
title: Inventaire forestier national suisse - Tableau no 892019
DOI: 10.21258/1542138
title: Inventaire forestier national suisse - Tableau no 892898
DOI: 10.21258/1610057
title: Inventario forestale nazionale svizzero - Tabella n. 902279