cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6528564
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.6624514
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.6805480
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.6988564
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.7040086
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.7181062
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.7298737
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.7397762
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record a 1% sample of (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files.
DOI: 10.5281/zenodo.7528182
title: Relative importance of the mechanisms triggering the Eurasian ice sheet deglaciation

description: Source data of the experiments presented in the manuscript:<br> Relative importance of the mechanisms triggering the Eurasian ice sheet deglaciation<br> Victor van Aalderen, Sylvie Charbit, Christophe Dumas and Aurelien Quiquet Contact information: victor.van-aalderen@lsce.ipsl.fr
DOI: 10.34740/kaggle/dsv/4842772
title: YouTube Daily
DOI: 10.5281/zenodo.7528670
title: Test upload 1

description: Just a test
DOI: 10.5281/zenodo.7528669
title: Test upload 1

description: Just a test
DOI: 10.6084/m9.figshare.21878688
title: afolabi.sav

description: A cross-sectional survey was used to collect information using a structured questionnaire, and about 50 respondents correctly completed the questionnaire. The data collected were analysed through SPSS, descriptive and inferential statistics, and an exploratory factor analysis was conducted.
DOI: 10.6084/m9.figshare.21878712
title: Data for the Wikipedia Paper

description: This is the dataset used to produce the results in the article on the link between Media visibility and Wikipedia search traffic
DOI: 10.6084/m9.figshare.21878712.v1
title: Data for the Wikipedia Paper

description: This is the dataset used to produce the results in the article on the link between Media visibility and Wikipedia search traffic
DOI: 10.5281/zenodo.7528811
title: Codes and dataset for the green-beard gene simulation

description: Codes and dataset for the green-beard gene simulation.
DOI: 10.5281/zenodo.7528810
title: Codes and dataset for the green-beard gene simulation

description: Codes and dataset for the green-beard gene simulation.
DOI: 10.5519/qd.v1nndgpk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (917 records)
DOI: 10.57702/0m50j2xo
title: Example LDM-API implementation

description: This dataset documents LDM’s API (CKAN), for developers who want to write code that interacts with LDM sites and their data.
DOI: 10.5517/ccdc.csd.cc22j7gb
title: CCDC 1922231: Experimental Crystal Structure Determination

description: Related Article: P. Yogheshwari, P. Karuppasamy, Muthu Senthil Pandian, P. Ramasamy, K. Anitha|2022|J.Mol.Struct.|1270|133884|doi:10.1016/j.molstruc.2022.133884
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2728l6
title: CCDC 2057767: Experimental Crystal Structure Determination

description: Related Article: Karuppasamy Pichan|2021|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.4265170
title: TESS-AUGUST-2020

description: Measurements taken by the European Photometer Network (Project STARS4ALL). August 2020
DOI: 10.5281/zenodo.4265171
title: TESS-AUGUST-2020

description: Measurements taken by the European Photometer Network (Project STARS4ALL). August 2020
DOI: 10.48420/21864987
title: CARMA II Alpha Contamination Avoidance Results

description: Four files consisting of datasets from Alpha contamination avoidance experiments conducted at RAICo1, Cumbria using the CARMA II UGV platform. <br> The UGV is instructed to follow a path consisting of concentric polygons, with each vertex as a navigation waypoint. If avoidance behaviours are enabled, the UGV will not traverse over an area where Alpha contamination has been detected via a front mounted sensor. <br> All files are txt, human readable, tab delimited, derived from larger ROS bag files recorded during the experimental campaign. All files contain a header row with descriptions of each column. Timestamps are in Unix Epoch time (as is standard for the Robot Operating System), positions in metres are given with a captial X, Y, Z for each cartesian axis respectively. The Z value are not necessary for this work, therefore their actual value may not correspond to realworld values, these should be ignored. Quaternion representations of orientation are given by x, y, z, w. Finally, any Alpha radiation intensity values are given as counts/sec. Any position data has been referenced to the first waypoint in the follower path. <br> Data was originally collected in November 2022 (in ROS bag format), which was interrogated to provide the data in these .txt files. <br> The position of the Alpha source was located at approximately: X = 2.0 m | Y = -0.4 m <br> "AlphaOnly_Travelpath.txt" represents the waypoint positions the UGV should try to match. "AlphaOnly_NoAvoidancePath.txt" represents the reference frame of the robot chassis and it's position in space as estimated by SLAM, whilst no avoidance behaviours are enabled. "AlphaOnly_WithAvoidancePath.txt" represents the reference frame of the robot chassis and it's position in space as estimated by SLAM, whilst avoidance behaviours are enabled and enacted when Alpha contamination is detected. "AlphaOnly_WithAvoidanceAlphaCounts.txt" represents the postion of the radiation detector and the measured counts during the experiment. <br> <br>
DOI: 10.48420/21864987.v1
title: CARMA II Alpha Contamination Avoidance Results

description: Four files consisting of datasets from Alpha contamination avoidance experiments conducted at RAICo1, Cumbria using the CARMA II UGV platform. <br> The UGV is instructed to follow a path consisting of concentric polygons, with each vertex as a navigation waypoint. If avoidance behaviours are enabled, the UGV will not traverse over an area where Alpha contamination has been detected via a front mounted sensor. <br> All files are txt, human readable, tab delimited, derived from larger ROS bag files recorded during the experimental campaign. All files contain a header row with descriptions of each column. Timestamps are in Unix Epoch time (as is standard for the Robot Operating System), positions in metres are given with a captial X, Y, Z for each cartesian axis respectively. The Z value are not necessary for this work, therefore their actual value may not correspond to realworld values, these should be ignored. Quaternion representations of orientation are given by x, y, z, w. Finally, any Alpha radiation intensity values are given as counts/sec. Any position data has been referenced to the first waypoint in the follower path. <br> Data was originally collected in November 2022 (in ROS bag format), which was interrogated to provide the data in these .txt files. <br> The position of the Alpha source was located at approximately: X = 2.0 m | Y = -0.4 m <br> "AlphaOnly_Travelpath.txt" represents the waypoint positions the UGV should try to match. "AlphaOnly_NoAvoidancePath.txt" represents the reference frame of the robot chassis and it's position in space as estimated by SLAM, whilst no avoidance behaviours are enabled. "AlphaOnly_WithAvoidancePath.txt" represents the reference frame of the robot chassis and it's position in space as estimated by SLAM, whilst avoidance behaviours are enabled and enacted when Alpha contamination is detected. "AlphaOnly_WithAvoidanceAlphaCounts.txt" represents the postion of the radiation detector and the measured counts during the experiment. <br> <br>
DOI: 10.5281/zenodo.7528856
title: Litterfall production and litter decomposition experiments: in-situ datasets of nutrient fluxes in two Bornean lowland rain forests associated with Acacia invasion

description: This dataset contains the original data from which the figures and tables for the article "Differential impacts of <em>Acacia</em> invasion on nutrient fluxes in two distinct Bornean lowland tropical rain forests" were prepared. It documents parameters relevant to nutrient fluxes via litterfall production and leaf litter decomposition rates from 2016 to 2017 in two selected lowland rainforests in Brunei Darussalam that are associated with <em>Acacia</em> invasion. Both litterfall sample collection and litter decomposition bag experiments followed standard protocols. Leaf litterfall fractions from the litterfall production experiment were analysed for nutrient contents of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), and calcium (Ca). Nutrient addition and nutrient use efficiency values were calculated based on nutrient concentration and monthly leaf litterfall production in the different habitat types studied. The mean percentage of litter mass remaining, K day<sup>-1</sup>, K year<sup>-1</sup>, half-life t<sub>0.5</sub>, pH values, and nutrient concentrations (N, P, K, Mg, Ca) were calculated for leaf litter samples collected after 336 days in the different habitats.
DOI: 10.5281/zenodo.7528855
title: Litterfall production and litter decomposition experiments: in-situ datasets of nutrient fluxes in two Bornean lowland rain forests associated with Acacia invasion

description: This dataset contains the original data from which the figures and tables for the article "Differential impacts of <em>Acacia</em> invasion on nutrient fluxes in two distinct Bornean lowland tropical rain forests" were prepared. It documents parameters relevant to nutrient fluxes via litterfall production and leaf litter decomposition rates from 2016 to 2017 in two selected lowland rainforests in Brunei Darussalam that are associated with <em>Acacia</em> invasion. Both litterfall sample collection and litter decomposition bag experiments followed standard protocols. Leaf litterfall fractions from the litterfall production experiment were analysed for nutrient contents of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), and calcium (Ca). Nutrient addition and nutrient use efficiency values were calculated based on nutrient concentration and monthly leaf litterfall production in the different habitat types studied. The mean percentage of litter mass remaining, K day<sup>-1</sup>, K year<sup>-1</sup>, half-life t<sub>0.5</sub>, pH values, and nutrient concentrations (N, P, K, Mg, Ca) were calculated for leaf litter samples collected after 336 days in the different habitats.
DOI: 10.5281/zenodo.6534569
title: Fast4DRegistration

description: <strong>Fast4DReg: Fast registration of 4D microscopy datasets</strong><br> Pylvänäinen J.W et al, 2023 Fast4DReg is a Fiji macro for drift correction in 3D video and is able to correct drift in all x-, y- and/or z-directions. Fast4DReg creates intensity projections along both axes and estimates their drift based on cross-correlation, and then translates the 3D video frame by frame. Additionally, Fast4DReg can be used for aligning multichannel 3D images which is particularly useful for instruments that suffer from a misalignment of channels. GitHub repository: https://github.com/guijacquemet/Fast4DReg Datasets are available in Zenodo: <strong>1 Software archive</strong><br> 1.1 Fast4DReg scripts (5)<br> 1.2 Helper scripts (2)<br> 1.3 Documentation from GitHub <strong>2 Dataset 1: Synthetic drift -datasets</strong><br> 2.1 Synthetic drift files (2)<br> 2.2 Expected results (3) <strong>3 Dataset 2: Noisy images -dataset</strong><br> 3.1 Noisy dataset files (12)<br> 3.2 Average corrected apply output (12)<br> 3.4 Maximum corrected apply output (12) <strong>4 Dataset 3: HUVEC monolayer -dataset</strong><br> 4.1 HUVEC -dataset channel 1 original<br> 4.2 Biological dataset HUVEC channel 1 Fast4DReg<br> 4.3 Biological dataset HUVEC channel 1 C3DD <strong>5 Dataset 4: Mouse lung slice -dataset</strong><br> 5.1 Mouse lung slice original<br> 5.2 Mouse lung slice Fast4DReg <strong>6 Dataset 5: Calibration slide -dataset</strong><br> 6.1 Calibration slide -dataset original<br> 6.2 Calibration slide -dataset Fast4DReg <strong>7 Dataset 6: Filopodia-dataset -dataset</strong><br> 7.1 Filopodia-dataset original<br> 7.2 Filopodia-dataset Fast4DReg <strong>8 Dataset 7: DCIS.com Filopodia -dataset</strong><br> 8.1 DCIS.com Filopodia -dataset original<br> 8.2 DCIS.com Filopodia -dataset Fast4DReg <strong>7 Videos</strong><br> 7.1 Video 1<br> 7.2 Video 2<br> 7.3 Video 3<br> 7.4 Video 4<br> 7.5 Video 5
DOI: 10.5281/zenodo.6534570
title: Fast4DRegistration

description: Fast4DReg: A New Pipeline for 3D Video Drift Correction<br> Pylvänäinen J.W et al, 2022 Fast4DReg is a Fiji macro for drift correction in 3D video and is able to correct drift in all x-, y- and/or z-directions. Fast4DReg creates intensity projections along both axes and estimates their drift using the NanoJ-Core (Laine et al., 2019) cross-correlation-based drift correction, and then translates the 3D video frame by frame (Figure 1). Additionally, Fast4DReg can be used for aligning multichannel 3D images which is particularly useful for instruments that suffer from a misalignment of channels. Datasets are available in Zenodo: <strong>1 Software archive</strong><br> 1.1 Fast4DReg scripts (4)<br> 1.2 Helper scripts (2)<br> 1.3 Documentation from GitHub <strong>2 Dataset 1: Synthetic drift -datasets</strong><br> 2.1 Synthetic drift files (3)<br> 2.2 Expected results (6) <strong>3 Dataset 2: the HUVEC monolayer -dataset</strong><br> 3.1 Biological dataset HUVEC channel 1 original<br> 3.2 Biological dataset HUVEC channel 1 F4DReg<br> 3.3 Biological dataset HUVEC channel 1 C3DD <strong>4 Channel alignment</strong><br> 4.1 Dataset 3: Registration slide -dataset (2)<br> 4.2 Dataset 4: Filopodia-dataset (2) <strong>5 Videos</strong><br> 5.1 Video 1<br> 5.2 Video 2<br> 5.3 Video 3