cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6085/aa/purxxx_015mtbd003r00_20050910.50.4
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2005-09-10. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20050910.50.5
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2005-09-10. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20051102.40.1
title: (:unkn)
DOI: 10.6085/aa/purxxx_015mtbd003r00_20051216.50.2
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2005-12-16. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was -14.2 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060224.40.2
title: (:unkn)
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060224.50.5
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2006-02-24. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060330.40.1
title: (:unkn)
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060330.40.2
title: (:unkn)
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060514.50.1
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2006-05-14. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060514.50.3
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2006-05-14. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060514.50.5
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2006-05-14. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060612.40.2
title: (:unkn)
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060612.50.5
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2006-06-12. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20060727.50.6
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2006-07-27. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was 2.0 minutes.
DOI: 10.6085/aa/purxxx_015mtbd003r00_20061207.50.2
title: PISCO: Physical Oceanography: moored temperature data: Purisima Point, California, USA (PUR)

description: This metadata record describes moored seawater temperature data collected at Purisima Point, California, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBIC32+4+27) beginning 2006-12-07. The instrument depth was 003 meters, in an overall water depth of 015 meters (both relative to Mean Sea Level, MSL). The sampling interval was -14.3 minutes.
DOI: 10.6085/aa/valxxx_015adcp015r00_20040402.50.8
title: PISCO: Physical Oceanography: bottom-mounted ADCP data: Santa Cruz Island, California, USA (VAL)

description: This metadata record describes bottom-mounted ADCP data collected at Santa Cruz Island, California, USA, by PISCO. Measurements were collected using an RDI 600 kHz Workhorse Sentinel ADCP beginning 2004-04-02. The instrument depth was 015 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The instrument was programmed with a sampling interval of 2.0 minutes and a vertical resolution of 1 meter.
DOI: 10.6085/aa/valxxx_015adcp015r00_20040713.40.2
title: (:unkn)
DOI: 10.6085/aa/valxxx_015adcp015r00_20041220.50.10
title: PISCO: Physical Oceanography: bottom-mounted ADCP data: Santa Cruz Island, California, USA (VAL)

description: This metadata record describes bottom-mounted ADCP data collected at Santa Cruz Island, California, USA, by PISCO. Measurements were collected using an RDI 600 kHz Workhorse Sentinel ADCP beginning 2004-12-20. The instrument depth was 015 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The instrument was programmed with a sampling interval of 2.0 minutes and a vertical resolution of 1 meter.
DOI: 10.6085/aa/valxxx_015adcp015r00_20041220.50.6
title: PISCO: Physical Oceanography: bottom-mounted ADCP data: Santa Cruz Island, California, USA (VAL)

description: This metadata record describes bottom-mounted ADCP data collected at Santa Cruz Island, California, USA, by PISCO. Measurements were collected using an RDI 600 kHz Workhorse Sentinel ADCP beginning 2004-12-20. The instrument depth was 015 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The instrument was programmed with a sampling interval of 2.0 minutes and a vertical resolution of 1 meter.
DOI: 10.6085/aa/valxxx_015adcp015r00_20041220.50.7
title: PISCO: Physical Oceanography: bottom-mounted ADCP data: Santa Cruz Island, California, USA (VAL)

description: This metadata record describes bottom-mounted ADCP data collected at Santa Cruz Island, California, USA, by PISCO. Measurements were collected using an RDI 600 kHz Workhorse Sentinel ADCP beginning 2004-12-20. The instrument depth was 015 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The instrument was programmed with a sampling interval of 2.0 minutes and a vertical resolution of 1 meter.
DOI: 10.6085/aa/valxxx_015adcp015r00_20050224.50.1
title: PISCO: Physical Oceanography: bottom-mounted ADCP data: Santa Cruz Island, California, USA (VAL)

description: This metadata record describes bottom-mounted ADCP data collected at Santa Cruz Island, California, USA, by PISCO. Measurements were collected using an RDI 600 kHz Workhorse Sentinel ADCP beginning 2005-02-24. The instrument depth was 015 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The instrument was programmed with a sampling interval of 2.0 minutes and a vertical resolution of 1 meter.
DOI: 10.6085/aa/valxxx_015adcp015r00_20050224.50.8
title: PISCO: Physical Oceanography: bottom-mounted ADCP data: Santa Cruz Island, California, USA (VAL)

description: This metadata record describes bottom-mounted ADCP data collected at Santa Cruz Island, California, USA, by PISCO. Measurements were collected using an RDI 600 kHz Workhorse Sentinel ADCP beginning 2005-02-24. The instrument depth was 015 meters, in an overall water depth of 15 meters (both relative to Mean Sea Level, MSL). The instrument was programmed with a sampling interval of 2.0 minutes and a vertical resolution of 1 meter.
DOI: 10.6085/aa/valxxx_015adcp015r00_20050602.40.3
title: (:unkn)
DOI: 10.12751/g-node.t77b3p
title: Data for 'Modular architecture facilitates noise-driven control of synchrony in neuronal networks'

description: In this data repository, we provide raw data and intermediate analysis results accompanying our publication. Files are compressed zip-archives, downloadable separately in order to reproduce expiermental results and simulations independently. The preliminary publication title is "Modular architecture facilitates noise-driven control of synchrony in neuronal networks". A preprint can be found at https://arxiv.org/abs/2205.10563. The accompanying code repository can be found at https://github.com/Priesemann-Group/stimulating_modular_cultures.
description: IsSupplementTo: Yamamoto et al.: 'Modular architecture facilitates noise-driven control of synchrony in neuronal networks'. Science Advances, submitted.
description: IsSupplementTo: Yamamoto et al. (2022) Preprint of 'Modular architecture facilitates noise-driven control of synchrony in neuronal networks' based on the data. arXiv. https://arxiv.org/abs/2205.10563. (https://doi.org/10.48550/arXiv.2205.10563)
description: IsReferencedBy: StimulatingModularCultures: Analysis code. Github. https://github.com/Priesemann-Group/stimulating_modular_cultures. (https://github.com/Priesemann-Group/stimulating_modular_cultures)
DOI: 10.4121/ebaf0dc5-d8d2-4eb7-8f28-21f7f9b2335c
title: Data companion to: Hydrodynamics of Expanded Bed Adsorption studied through CFD-DEM

description: Data companion to:<br><strong>Hydrodynamics of Expanded Bed Adsorption studied through CFD-DEM</strong><em>Tim M.J. Nijssen, Johan T. Padding, Marcel Ottens</em>Chemical Engineering Science 280, 2023<br>contact: t.m.j.nijssen@tudelft.nl (Tim M.J. Nijssen)<br>This set contains data on the liquid flow and particle movement in CFD-DEM simulations of Expanded Bed Adsorption columns. In total 20 different cases are included, covering various operational conditions, material parameters and column designs. The set contains raw simulation output on fluid (CFD) and particles (DEM), as well as rendered videos. A description of file types and naming is provided in <em>README.txt</em>. Description of the 20 different cases in included in <em>case_description.csv</em>.<br>Delft University of Technology (NL), 13-07-2023
DOI: 10.4121/ebaf0dc5-d8d2-4eb7-8f28-21f7f9b2335c.v1
title: Data companion to: Hydrodynamics of Expanded Bed Adsorption studied through CFD-DEM

description: Data companion to:<br><strong>Hydrodynamics of Expanded Bed Adsorption studied through CFD-DEM</strong><em>Tim M.J. Nijssen, Johan T. Padding, Marcel Ottens</em>Chemical Engineering Science 280, 2023<br>contact: t.m.j.nijssen@tudelft.nl (Tim M.J. Nijssen)<br>This set contains data on the liquid flow and particle movement in CFD-DEM simulations of Expanded Bed Adsorption columns. In total 20 different cases are included, covering various operational conditions, material parameters and column designs. The set contains raw simulation output on fluid (CFD) and particles (DEM), as well as rendered videos. A description of file types and naming is provided in <em>README.txt</em>. Description of the 20 different cases in included in <em>case_description.csv</em>.<br>Delft University of Technology (NL), 13-07-2023
DOI: 10.6084/m9.figshare.20101202
title: HGGNN data and code

description: <strong>Abstract</strong> Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to recover values from unsampled areas, while minimal attention has been paid to the values of irregular areas, which are naturally formed by road networks or administrative areas. To address this problem, this study proposes a hierarchical geospatial graph neural network model based on the spatial hierarchical constraints of areas. The model first characterizes spatial relationships between irregular areas from different spatial scales. Then, it aggregates information from neighboring areas with graph neural networks, and finally, it imputes missing values in fine-grained areas under hierarchical relationship constraints. To investigate the performance of the proposed model, we constructed a new dataset consisting of the urban statistical values of irregular areas in New York City. Experiments on the dataset showed that the proposed model outperformed the baselines by 30.34%, 14.43%, and 14.47% in RMSE, MAE, and MRE, respectively. This research enriches datasets on spatial missing value research and provides a methodological reference for the completion and prediction of a wide variety of geographic big data. It can be used in numerous spatial decision applications, including traffic management, public safety, and public resource allocation. <br> <strong>Keywords:</strong> urban area, spatial prediction, hierarchical constraint, spatial interpolation <br> This is the code and data of "A hierarchical constraint-based graph neural network for imputing"
DOI: 10.6084/m9.figshare.20101202.v4
title: HGGNN data and code

description: <strong>Abstract</strong> Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to recover values from unsampled areas, while minimal attention has been paid to the values of irregular areas, which are naturally formed by road networks or administrative areas. To address this problem, this study proposes a hierarchical geospatial graph neural network model based on the spatial hierarchical constraints of areas. The model first characterizes spatial relationships between irregular areas from different spatial scales. Then, it aggregates information from neighboring areas with graph neural networks, and finally, it imputes missing values in fine-grained areas under hierarchical relationship constraints. To investigate the performance of the proposed model, we constructed a new dataset consisting of the urban statistical values of irregular areas in New York City. Experiments on the dataset showed that the proposed model outperformed the baselines by 30.34%, 14.43%, and 14.47% in RMSE, MAE, and MRE, respectively. This research enriches datasets on spatial missing value research and provides a methodological reference for the completion and prediction of a wide variety of geographic big data. It can be used in numerous spatial decision applications, including traffic management, public safety, and public resource allocation. <br> <strong>Keywords:</strong> urban area, spatial prediction, hierarchical constraint, spatial interpolation <br> This is the code and data of "A hierarchical constraint-based graph neural network for imputing"
DOI: 10.5281/zenodo.8146210
title: The Digital Archive as an Inclusive Tool for Knowledge Construction through Design Practices_List of Case studies

description: Title of the paper: "The Digital Archive as an Inclusive Tool for Knowledge Construction through Design Practices" List of Case studies
DOI: 10.5281/zenodo.8146209
title: The Digital Archive as an Inclusive Tool for Knowledge Construction through Design Practices_List of Case studies

description: Title of the paper: "The Digital Archive as an Inclusive Tool for Knowledge Construction through Design Practices" List of Case studies
DOI: 10.5281/zenodo.8146165
title: Antimagic Labelings of All Trees of Order 3 to 25

description: This archive contains antimagic labelings for all trees of order 3 to 25 found by evolutionary search as described in the paper: Branson, L. and Sutton, A. M., <em>Finding Antimagic Labelings of Trees by Evolutionary Search</em>, 2023. https://doi.org/10.1145/3594805.3607133 The archive contains 1679 compressed sub-archives, each containing 100000 data files, except the last, which contains 79144 data files. The total number of data files is 167879144 (cf. the total number of unlabeled trees of order 3-25 in entry A000055 of the OEIS https://oeis.org/A000055). Each data file corresponds to a unique tree and is named <strong>tree-&lt;n&gt;-&lt;id&gt;.viz</strong> where <strong>&lt;n&gt;</strong> is the order (number of vertices) of the tree and <strong>&lt;id&gt;</strong> is a unique identifier for the tree of that order. Each data file is a text file written in the DOT graph description language that specifies the structure of the tree along with an edge labeling and vertex labeling that is antimagic.
DOI: 10.5281/zenodo.8146164
title: Antimagic Labelings of All Trees of Order 3 to 25

description: This archive contains antimagic labelings for all trees of order 3 to 25 found by evolutionary search as described in the paper: Branson, L. and Sutton, A. M., <em>Finding Antimagic Labelings of Trees by Evolutionary Search</em>, 2023. https://doi.org/10.1145/3594805.3607133 The archive contains 1679 compressed sub-archives, each containing 100000 data files, except the last, which contains 79144 data files. The total number of data files is 167879144 (cf. the total number of unlabeled trees of order 3-25 in entry A000055 of the OEIS https://oeis.org/A000055). Each data file corresponds to a unique tree and is named <strong>tree-&lt;n&gt;-&lt;id&gt;.viz</strong> where <strong>&lt;n&gt;</strong> is the order (number of vertices) of the tree and <strong>&lt;id&gt;</strong> is a unique identifier for the tree of that order. Each data file is a text file written in the DOT graph description language that specifies the structure of the tree along with an edge labeling and vertex labeling that is antimagic.
DOI: 10.6084/m9.figshare.21621222
title: Detection and classification of bottlenose dolphin’s (Tursiops truncatus, Montagu, 1821) vocalizations in the central-northern Adriatic Sea

description: This dataset is part of a collection and contains the results of dolphins’ vocalizations detection and classification. In details: <strong>whistles.txt</strong> includes useful information on 303 whistles visually detected from the whole recording, where: the first column reports the whistle starting time offset the second column reports the whistle end-time offset the third column contains the classification (W for single whistle, MW for multiple whistles) and the relative quality grade of the whistle. The whistle quality increases with increasing grade (from 1 to 3). <br> <strong>clicks.txt</strong> contains a list of the classified echolocalization emission events detected by an algorithm specifically developed for this aim, where: the first column reports the starting time offset the second column reports the end-time offset the third column contains the mean ICI bin in classes of 5ms <br> <br> These txt files can be directly used with Audacity software to mark the vocalization contained in the raw wave file stored in DATA 1 as described in this tutorial. <br> <strong>results.xlsx</strong> reports the results of an algorithm-based analysis of 99 acoustic segments, achieved splitting the raw audio signal in 98 one-minute-length parts and one 20-second-lenght final part. This analysis was performed in order to detect and classify high-frequency dolphin’s emissions. The csv file includes the following information for each acoustic segment: the segment number; the total number of considered peaks; the number of detected click trains; the minimum ICI value of the detected trains; the maximum ICI value of the detected trains; the average ICI value of the detected trains; the number of observed whistles, if present in the segment; the presence in the relative segment of multiple whistles (Yes/No). <br>
DOI: 10.6084/m9.figshare.21621222.v4
title: Detection and classification of bottlenose dolphin’s (Tursiops truncatus, Montagu, 1821) vocalizations in the central-northern Adriatic Sea

description: This dataset is part of a collection and contains the results of dolphins’ vocalizations detection and classification. In details: <strong>whistles.txt</strong> includes useful information on 303 whistles visually detected from the whole recording, where: the first column reports the whistle starting time offset the second column reports the whistle end-time offset the third column contains the classification (W for single whistle, MW for multiple whistles) and the relative quality grade of the whistle. The whistle quality increases with increasing grade (from 1 to 3). <br> <strong>clicks.txt</strong> contains a list of the classified echolocalization emission events detected by an algorithm specifically developed for this aim, where: the first column reports the starting time offset the second column reports the end-time offset the third column contains the classification in classes of 5ms (f.e. class 5 means 20-25 ms class range, class 10 means 45-50 ms range) <br> <br> These txt files can be directly used with Audacity software to mark the vocalization contained in the raw wave file stored in DATA 1 as described in this tutorial. <br> <strong>results.xlsx</strong> reports the results of an algorithm-based analysis of 99 acoustic segments, achieved splitting the raw audio signal in 98 one-minute-length parts and one 20-second-lenght final part. This analysis was performed in order to detect and classify high-frequency dolphin’s emissions. The csv file includes the following information for each acoustic segment: the segment number; the total number of considered peaks; the number of detected click trains; the minimum ICI value of the detected trains; the maximum ICI value of the detected trains; the average ICI value of the detected trains; the number of observed whistles, if present in the segment; the presence in the relative segment of multiple whistles (Yes/No). <br>