cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7296570
title: HISAR - Hydrologic Indices of South America Rivers

description: This is a preview of the HISAR dataset (Hydrologic Indices of South America Rivers). The HISAR dataset is freely available for non-commercial use. The files provided are (i) drainage line shapefile with river reaches as represented by the MGB model and 73 attributes corresponding to hydrologic indices derived from simulated time series; (ii) gauge points shapefile with 73 attributes corresponding to hydrologic indices derived from observed time series; (iii) maps with hydrologic indices, (iv) maps with information of the error of some indices and (v) the scripts used to calculate the indices. This database provides a spatial view of the variability of the river flow regime characteristics. The line shapefile has 33,749 river reaches with an average length of 15 km and drainage area &gt; 1000 km². The ESRI shapefile also has the attributes of drainage area (<em>Upst_Area_</em> in km²), length (<em>Ltr_Km_ in</em> km), <em>UC</em> (corresponding catchment attribute from hydrological modelling), <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’. Some river reaches do not have all hydrologic indices calculated, due to series of streamflows that could not meet specific criteria. For instance, the baseflow recession constant was automatically calculated using at least five consecutive days of decreasing streamflow, all of which below the Q90 (streamflow value that is exceeded 90% of the time), and this condition was not found in all cases. The gauge points shapefile has 1329 points with 73 attributes corresponding to hydrologic indices derived from observed time series. The drainage area of the gauges ranging from 1,000 to 4,703,503 km<sup>2</sup>. The ESRI shapefile also has the attributes of code, name, latitude (lat), longitude (long), drainage area (<em>Upst_Area_</em> in km²), Country were the gauge point are located, <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’. For more information about HISAR dataset see the journal article DOI: in preparation.
DOI: 10.5281/zenodo.7296577
title: HISAR - Hydrologic Indices of South American Rivers

description: This is a preview of the HISAR dataset (Hydrologic Indices of South American Rivers). The HISAR dataset is freely available for non-commercial use. The files provided are (i) drainage line shapefile with river reaches as represented by the MGB model and 73 attributes corresponding to hydrologic indices derived from simulated time series; (ii) gauge points shapefile with 73 attributes corresponding to hydrologic indices derived from observed time series; (iii) maps with hydrologic indices, (iv) maps with information of the error of some indices and (v) the scripts used to calculate the indices. This database provides a spatial view of the variability of the river flow regime characteristics. The line shapefile has 33,749 river reaches with an average length of 15 km and drainage area &gt; 1000 km². The ESRI shapefile also has the attributes of drainage area (<em>Upst_Area_</em> in km²), length (<em>Ltr_Km_ in</em> km), <em>UC</em> (corresponding catchment attribute from hydrological modelling), <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’. Some river reaches do not have all hydrologic indices calculated, due to series of streamflows that could not meet specific criteria. For instance, the baseflow recession constant was automatically calculated using at least five consecutive days of decreasing streamflow, all of which below the Q90 (streamflow value that is exceeded 90% of the time), and this condition was not found in all cases. The gauge points shapefile has 1329 points with 73 attributes corresponding to hydrologic indices derived from observed time series. The drainage area of the gauges ranging from 1,000 to 4,703,503 km<sup>2</sup>. The ESRI shapefile also has the attributes of code, name, latitude (lat), longitude (long), drainage area (<em>Upst_Area_</em> in km²), Country were the gauge point are located, <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’. For more information about HISAR dataset see the journal article DOI: in preparation.
DOI: 10.6084/m9.figshare.21384792
title: The impact of elevated aestivation temperatures on the behaviour of Bogong Moths (Agrotis infusa) - Data sharing

description: The impact of elevated aestivation temperatures on the behaviour of Bogong Moths (Agrotis infusa) - Data sharing <br> Including raw data for Bogong wet and dry mass, activity data and R script for analysis.
DOI: 10.6084/m9.figshare.21384792.v3
title: The impact of elevated aestivation temperatures on the behaviour of Bogong Moths (Agrotis infusa) - Data sharing

description: The impact of elevated aestivation temperatures on the behaviour of Bogong Moths (Agrotis infusa) - Data sharing <br> Including raw data for Bogong wet and dry mass, activity data and R script for analysis.
DOI: 10.6084/m9.figshare.21384792.v2
title: The impact of elevated aestivation temperatures on the behaviour of Bogong Moths (Agrotis infusa) - Data sharing

description: The impact of elevated aestivation temperatures on the behaviour of Bogong Moths (Agrotis infusa) - Data sharing <br> Including raw data for Bogong wet and dry mass, activity data and R script for analysis.
DOI: 10.6084/m9.figshare.21507921
title: organik1.sav

description: organic food purchase intention
DOI: 10.5281/zenodo.7289744
title: Language ROIs mixed between hemispheres

description: We developed a new approach that assesses laterality of certain circuitry by calculating seedmaps using ROIs that involve <em>n </em>left-hemisphere (LH) and <em>m</em> right-hemisphere (RH) grayordinates, where <em>n+m</em> equals the total size of the original ROI. In essence, <em>n</em> grayordinates are randomly removed from the LH and added to the RH. This is done for each value of <em>n</em> 10 times to create different source ROIs to sample from. This project was done to study the language network, and so we publish three sets of ROIs: for "Broca's area" (inferior frontal gyrus), middle temporal gyrus, and a "Wernicke's area" near the temperoparietal junction. These ROIs were generated from the ventral attention network atlas published at neuroatlas.org.
description: {"references": ["Hermosillo, R. J. M., Moore, L. A., Fezcko, E., Dworetsky, A., Pines, A., Conan, G., Mooney, M. A., Randolph, A., Adeyemo, B., Earl, E., Perrone, A., Carrasco, C. M., Uriarte-Lopez, J., Snider, K., Doyle, O., Cordova, M., Nagel, B. J., Ewing, S. W. F., Satterthwaite, T., \u2026 Fair, D. A. (2022). A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography (p. 2022.01.12.475422). bioRxiv. https://doi.org/10.1101/2022.01.12.475422"]}
DOI: 10.5281/zenodo.7289743
title: Language ROIs mixed between hemispheres

description: We developed a new approach that assesses laterality of certain circuitry by calculating seedmaps using ROIs that involve <em>n </em>left-hemisphere (LH) and <em>m</em> right-hemisphere (RH) grayordinates, where <em>n+m</em> equals the total size of the original ROI. In essence, <em>n</em> grayordinates are randomly removed from the LH and added to the RH. This is done for each value of <em>n</em> 10 times to create different source ROIs to sample from. This project was done to study the language network, and so we publish three sets of ROIs: for "Broca's area" (inferior frontal gyrus), middle temporal gyrus, and a "Wernicke's area" near the temperoparietal junction. These ROIs were generated from the ventral attention network atlas published at neuroatlas.org.
description: {"references": ["Hermosillo, R. J. M., Moore, L. A., Fezcko, E., Dworetsky, A., Pines, A., Conan, G., Mooney, M. A., Randolph, A., Adeyemo, B., Earl, E., Perrone, A., Carrasco, C. M., Uriarte-Lopez, J., Snider, K., Doyle, O., Cordova, M., Nagel, B. J., Ewing, S. W. F., Satterthwaite, T., \u2026 Fair, D. A. (2022). A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography (p. 2022.01.12.475422). bioRxiv. https://doi.org/10.1101/2022.01.12.475422"]}
DOI: 10.6073/pasta/070c0bec46e1336684c534f9a4034334
title: Point-count bird censusing: long-term monitoring of bird abundance and diversity along the Salt River in the greater Phoenix metropolitan area, ongoing since 2013

description: Waterways are often the focus of restoration efforts in urban areas. In arid regions, passive discharge of urban water sources may stimulate the recovery or growth of wetland and riparian features in dewatered or ephemeral aquatic systems. In the greater Phoenix metropolitan area (GPMA), sections of the Salt and Gila Rivers have been the targets of active restoration through seeding, planting, and irrigation. At the same time, revegetation has occurred in some sections of the rivers in response to runoff from urban water sources (e.g., storm drains). This dataset catalogs the results of bird surveys conducted at several locations along the Salt River in and around the GPMA beginning in March 2013. Monitoring locations focus on reaches of the river with different characteristics, including: (1) urbanized with perennial water and actively restored (n=2 reaches), (2) urbanized with perennial water and passively restored (n=2 reaches), (3) urbanized with ephemeral water but not restored (n=2 reaches), and (4) non-urban reference areas with perennial water (n=1 reach). This program expands on bird monitoring that the CAP LTER conducts at other locations in and around the GPMA, and complements herpetological surveys that are performed at these locations along the Salt River where the bird surveys are performed. This is a long-term monitoring effort of the CAP LTER with on-going data collection.
DOI: 10.15468/mwdhkq
title: Spermatophyta and invasive plants in Wanda Mountains, Heilongjiang Province, China

description: This is the first checklist of Spermatophyta and invasive plants in Wanda Mountains, including native species and invasive species a total of 97 families 355 genera 716 species and infraspecific taxa are listed. There are 95 families, 329 genera, and 669 species and infraspecific taxa of native plants; 19 families, 37 genera, and 47 species of invasive plants. The resource data includes not only the taxa information but also the record notes.
DOI: 10.15468/4x7279
title: gazetteerofnechina

description: This set of data provides a collection of place names in Northeast China. It includes the names of provinces, cities, counties, towns and villages in Northeast China. When we clean up specimens, we often encounter great difficulty determining the location. Many specimens, especially the older ones, have incomplete information when labeling the location, leading to many errors in the digitized information. Therefore, a detailed set of place names containing historical knowledge is necessary when cleaning up specimen records. This data set is an attempt to organize a complete set of geographical names in Northeast China. The current place name collection only includes the current names. In the follow-up update, our goal is to completely sort out the place names and their changes from the beginning of specimen collection 150 years ago. Our data will be collected from the Ministry of Civil Affairs of China, civil affairs departments of local governments at all levels, various atlases, local chronicles and place name research databases.
DOI: 10.5281/zenodo.7262220
title: Crowd simulation (CrowdSim2) for tracking and object detection

description: CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars). Information summarizing number of folders, seconds and frames of data for different weather conditions Condition Folders Seconds Frames Sun 2899 86 970 2 174 250 Rain 1633 48 990 1 224 750 Fog 1653 49 590 1 239 750 Snow 1646 49 380 1 234 500 Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: CrowdSim <strong>Acknowledgments</strong> This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk. <strong>Citing the Crowdsim 2</strong> The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form.<br> Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people <pre><code class="language-markdown">TBA: Article citations using this dataset will appear here</code></pre> and this Zenodo Dataset <pre><code class="language-markdown">@dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }</code></pre>
DOI: 10.5281/zenodo.7262219
title: Crowd simulation (CrowdSim2) for tracking and object detection

description: CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars). Information summarizing number of folders, seconds and frames of data for different weather conditions Condition Folders Seconds Frames Sun 2899 86 970 2 174 250 Rain 1633 48 990 1 224 750 Fog 1653 49 590 1 239 750 Snow 1646 49 380 1 234 500 Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: CrowdSim <strong>Acknowledgments</strong> This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk. <strong>Citing the Crowdsim 2</strong> The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form.<br> Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people <pre><code class="language-markdown">TBA: Article citations using this dataset will appear here</code></pre> and this Zenodo Dataset <pre><code class="language-markdown">@dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }</code></pre>
DOI: 10.5281/zenodo.7296881
title: [IO Islamic 897] قصّۀ شاه مردان على

description: <strong>Ḳiṣṣ-i-Shâh-i-mardân ‘Alî.</strong> <strong>This manuscript is now IO Islamic 786 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Hermann Ethé</strong><strong><em>, </em></strong><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> <strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong> <strong>number 897 here with notations and hyperlinks].</strong> 786 Ḳiṣṣ-i-Shâh-i-mardân ‘Alî (قصّۀ شاه مردان على). Another very large, but likewise incomplete, Persian romance of similar character and similar contents as the preceding one, and therefore styled, in a note, on fol. 1<sup>a</sup>, قصّۀ حمزه ; but whether it is merely another enlarged version of the Ḥamzanâma or rather an independent romance, dealing with heroic deeds of the same period, is difficult to say; at any rate, the chief hero here is ‘Alî bin Abî Ṭâlib, Muḥammad’s son-in-law and fourth Khalîf, not Ḥamzah, the prophet’s uncle; every paragraph begins with the words امّا در محلّ و زمانى كه , and closes with the phrase يا على مدد. On the other hand, several characters appearing in the preceding copy of the Ḥamzanâma, are found here too, for instance, زمرّدشاه. In spite of its excessive length, this copy is by no means complete; it begins abruptly thus: امّا در محلّ و زمانى كه نقاب وار گوهرپوش طهماسپ را گرفت زمرّدشارا گرفت نقاب وار از عقب عمر الخ, and a great number of leaves are missing at the end; there is also a blank between the middle of fol. 6<sup>a</sup> and that of fol. 7<sup>a</sup>, and portions of ff. 8, 650, and 651 are torn away. Neither date nor author’s name appears anywhere. The same note on fol. 1<sup>a</sup> which gives to this romance the title of قصّۀ حمزه also states that it was copied A.H. 1083 (A.D. 1672, 1673). No. 897, ff. 664, ll. 17; distinct Nasta’lîḳ; size, 10 in. by 5<sup>1/4</sup> in.
DOI: 10.5281/zenodo.7296880
title: [IO Islamic 897] قصّۀ شاه مردان على

description: <strong>Ḳiṣṣ-i-Shâh-i-mardân ‘Alî.</strong> <strong>This manuscript is now IO Islamic 786 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Hermann Ethé</strong><strong><em>, </em></strong><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> <strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong> <strong>number 897 here with notations and hyperlinks].</strong> 786 Ḳiṣṣ-i-Shâh-i-mardân ‘Alî (قصّۀ شاه مردان على). Another very large, but likewise incomplete, Persian romance of similar character and similar contents as the preceding one, and therefore styled, in a note, on fol. 1<sup>a</sup>, قصّۀ حمزه ; but whether it is merely another enlarged version of the Ḥamzanâma or rather an independent romance, dealing with heroic deeds of the same period, is difficult to say; at any rate, the chief hero here is ‘Alî bin Abî Ṭâlib, Muḥammad’s son-in-law and fourth Khalîf, not Ḥamzah, the prophet’s uncle; every paragraph begins with the words امّا در محلّ و زمانى كه , and closes with the phrase يا على مدد. On the other hand, several characters appearing in the preceding copy of the Ḥamzanâma, are found here too, for instance, زمرّدشاه. In spite of its excessive length, this copy is by no means complete; it begins abruptly thus: امّا در محلّ و زمانى كه نقاب وار گوهرپوش طهماسپ را گرفت زمرّدشارا گرفت نقاب وار از عقب عمر الخ, and a great number of leaves are missing at the end; there is also a blank between the middle of fol. 6<sup>a</sup> and that of fol. 7<sup>a</sup>, and portions of ff. 8, 650, and 651 are torn away. Neither date nor author’s name appears anywhere. The same note on fol. 1<sup>a</sup> which gives to this romance the title of قصّۀ حمزه also states that it was copied A.H. 1083 (A.D. 1672, 1673). No. 897, ff. 664, ll. 17; distinct Nasta’lîḳ; size, 10 in. by 5<sup>1/4</sup> in.