cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9917741.v5
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917633.v1
title: Data from: Individual and seasonal variation in contact rate, connectivity and centrality in red fox (Vulpes vulpes) social groups

description: Data used to analyse red fox contact rates and social networks. Results are published in thesis chapter 5 and a paper titled "Individual and seasonal variation in contact rate, connectivity and centrality in red fox (Vulpes vulpes) social groups" that is currently under peer review.<br>For a full description of each file refer to the file data_dictionaries_and_description.docx.
DOI: 10.6084/m9.figshare.9917741.v6
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917741.v7
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917741.v8
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917741.v9
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917741.v10
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917741.v11
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917741.v12
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917537
title: Macko_Gender_initial_trust_apology

description: Data from 19 rounds of trust game<br>
DOI: 10.6084/m9.figshare.9917537.v1
title: Macko_Gender_initial_trust_apology

description: Data from 19 rounds of trust game<br>
DOI: 10.6084/m9.figshare.9917525
title: All 9412 gene sequences of Chlorella vulgaris NJ-7

description: A fasta-formatted file of all 9412 nuclear gene sequences in <i>Chlorella vulgaris</i> NJ-7.<br>
DOI: 10.6084/m9.figshare.9917507
title: Banerjee_EffectsKinesiologyTapingSomatosensation_PLOSONE

description: Raw dataset_Physiological Experiment_PLOS ONE Journal
DOI: 10.6084/m9.figshare.9917525.v1
title: All 9412 gene sequences of Chlorella vulgaris NJ-7

description: A fasta-formatted file of all 9412 nuclear gene sequences in <i>Chlorella vulgaris</i> NJ-7.<br>
DOI: 10.6084/m9.figshare.9917741.v13
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917462
title: GWAS and WGS datasets

description: these files contains the genotypes of 674 individuals (chip_snp.map,chip_snp.ped.gz,rad_snp.map,rad_snp.ped.gz),and WGS data.
DOI: 10.6084/m9.figshare.9917507.v2
title: Banerjee_EffectsKinesiologyTapingSomatosensation_PLOSONE

description: Raw dataset_Physiological Experiment_PLOS ONE Journal
DOI: 10.6084/m9.figshare.9917507.v1
title: Banerjee_EffectsKinesiologyTapingSomatosensation_PLOSONE

description: Raw dataset_Physiological Experiment_PLOS ONE Journal
DOI: 10.6084/m9.figshare.9917459
title: Regional ocean atmosphere coupled simulations of 2005 western North Pacific summer monsoon with RegCM4_LICOM and WRF4_LICOM

description: Two state-of-the-art regional climate models (RegCM4 and WRF4 with a resolution of 15km) were coupled with a high resolution (0.1°) North Pacific Ocean model (LICOM_np). The regional ocean-atmosphere coupled models have been applied to western North Pacific. The performance of the regional ocean-atmosphere coupled models in the simulations of 2005 summer monsoon rainfall over western North Pacific (WNP) was assessed and inter-compared.
DOI: 10.6084/m9.figshare.9917741.v14
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917459.v1
title: Regional ocean atmosphere coupled simulations of 2005 western North Pacific summer monsoon with RegCM4_LICOM and WRF4_LICOM

description: Two state-of-the-art regional climate models (RegCM4 and WRF4 with a resolution of 15km) were coupled with a high resolution (0.1°) North Pacific Ocean model (LICOM_np). The regional ocean-atmosphere coupled models have been applied to western North Pacific. The performance of the regional ocean-atmosphere coupled models in the simulations of 2005 summer monsoon rainfall over western North Pacific (WNP) was assessed and inter-compared.
DOI: 10.6084/m9.figshare.9917453.v1
title: Hybrid genome assembly and annotation of a pandrug-resistant Klebsiella pneumoniae strain with Nanopore sequencing

description: The assembly genome sequence of K. pneumoniae KP58 and phylogenetic tree files.<br>
DOI: 10.6084/m9.figshare.9917741.v15
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917462.v1
title: GWAS datasets

description: these files contains the genotypes of 674 individuals (chip_snp.map,chip_snp.ped.gz,rad_snp.map,rad_snp.ped.gz).
DOI: 10.6084/m9.figshare.9917741.v16
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917228
title: archive.bin

description: Simulation archive from REBOUND N--body simulation (see REBOUND docs for a description of simulation archives--rebound ). <br><br>We start the simulation with an eccentric disk (e=0.5) around a black hole (BH). Disk (BH) mass is 10^4 (4x10^6 Msun). Disk contains 100 stars of identical mass. GR effects are included, as is the potential due to a surrounding stellar cluster (see Madigan et al. 2009 for a model of this potential). <br>
DOI: 10.6084/m9.figshare.9917228.v1
title: archive.bin

description: Simulation archive from REBOUND N--body simulation (see REBOUND docs for a description of simulation archives--rebound ). <br><br>We start the simulation with an eccentric disk (e=0.5) around a black hole (BH). Disk (BH) mass is 10^4 (4x10^6 Msun). Disk contains 100 stars of identical mass. GR effects are included, as is the potential due to a surrounding stellar cluster (see Madigan et al. 2009 for a model of this potential). <br>
DOI: 10.6084/m9.figshare.9917210
title: Evaluation of Regional Climate Models using Regionally-Optimized GRACE Mascons in the Amery and Getz ice shelves basins, Antarctica - DATA

description: Time-series for the regionally-optimized GRACE mascons and the corresponding Mass Budget Method time-series with RACMO2.3p1, RACMO2.3p2, and MAR3.6.41 regional climate models, as described in Mohajerani et al. (2019) are provided as individual ascii files. <br> <b>List of Files:</b><i>GRACE FILES</i>Amery_GRACE-CaronGIA.txtGetz_GRACE-CaronGIA.txt<br> -- Columns --GRACE months (format 03i)Decimal Years (format 12.2f)Mass in Gigatons (format 12.4f)Monthly Uncertainty in Gigatons (format 12.4f)<br> <i>MBM FILES</i>Amery_SMB(MAR3.6.41)-D.txtAmery_SMB(RACMO2.3p2)-D.txt Amery_SMB(RACMO2.3p1)-D.txt Getz_SMB(MAR3.6.41)-D.txt Getz_SMB(RACMO2.3p2)-D.txtGetz_SMB(RACMO2.3p1)-D.txt<br> <i>MBM FILES adjusted by RACMO2.3p1</i>Amery_SMB(MAR3.6.41)-D_adjusted-by-RACMO2.3p1.txt Amery_SMB(RACMO2.3p2)-D_adjusted-by-RACMO2.3p1.txt Totten+Moscow_SMB(MAR3.6.41)-D_adjusted-by-RACMO2.3p1.txtTotten+Moscow_SMB(RACMO2.3p2)-D_adjusted-by-RACMO2.3p1.txt<br> -- Columns --Decimal Years (format 12.3f)Mass in Gigatons (format f)Monthly Uncertainty in Gigatons (format f)<br> <b>Please Cite the paper below when using the provided dataset</b>Citation:Mohajerani, Y., Velicogna, I., & Rignot, E. (2019). Evaluation of Regional Climate Models using Regionally-Optimized GRACE Mascons in the Amery and Getz ice shelves basins, Antarctica. Geophysical Research Letters. doi:10.1029/2019GL084665
DOI: 10.6084/m9.figshare.9917219
title: A BENCHMARK DATASET FOR MANIPURI MEETEI-MAYEK HANDWRITTEN CHARACTER RECOGNITION

description: A benchmark dataset is always required for any classification or recognition system. To the best of our knowledge, no benchmark dataset exists for handwritten character recognition of Manipuri Meetei-Mayek script in public domain so far. Manipuri, also referred to as Meeteilon or sometimes Meiteilon, is a Sino-Tibetan language and also one of the Eight Scheduled languages of Indian Constitution. It is the official language and lingua franca of the southeastern Himalayan state of Manipur, in northeastern India. This language is also used by a significant number of people as their communicating language over the north-east India, and some parts of Bangladesh and Myanmar. It is the most widely spoken language in Northeast India after Bengali and Assamese languages. In this work, we introduce a handwritten Manipuri Meetei-Mayek character dataset which consists of more than 5000 data samples which were collected from a diverse population group that belongs to different age groups (from 4 years to 60 years), genders, educational backgrounds, occupations, communities from three different districts of Manipur, India (Imphal East District, Thoubal District and Kangpokpi District) during March and April 2019. Each individual was asked to write down all the Manipuri characters on one A4-size paper. The recorded responses are scanned with the help of a scanner and then each character is manually segmented from the scanned images. This dataset consists of segmented scanned images of handwritten Manipuri Meetei-Mayek characters (Mapi Mayek, Lonsum Mayek, Cheitap Mayek, Cheising Mayek, Khutam Mayek) of size 128X128 pixels in .JPG format as well as in .MAT format.
DOI: 10.6084/m9.figshare.9917741.v19
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917219.v1
title: A BENCHMARK DATASET FOR MANIPURI MEETEI-MAYEK HANDWRITTEN CHARACTER RECOGNITION

description: A benchmark dataset is always required for any classification or recognition system. To the best of our knowledge, no benchmark dataset exists for handwritten character recognition of Manipuri Meetei-Mayek script in public domain so far. Manipuri, also referred to as Meeteilon or sometimes Meiteilon, is a Sino-Tibetan language and also one of the Eight Scheduled languages of Indian Constitution. It is the official language and lingua franca of the southeastern Himalayan state of Manipur, in northeastern India. This language is also used by a significant number of people as their communicating language over the north-east India, and some parts of Bangladesh and Myanmar. It is the most widely spoken language in Northeast India after Bengali and Assamese languages. In this work, we introduce a handwritten Manipuri Meetei-Mayek character dataset which consists of more than 5000 data samples which were collected from a diverse population group that belongs to different age groups (from 4 years to 60 years), genders, educational backgrounds, occupations, communities from three different districts of Manipur, India (Imphal East District, Thoubal District and Kangpokpi District) during March and April 2019. Each individual was asked to write down all the Manipuri characters on one A4-size paper. The recorded responses are scanned with the help of a scanner and then each character is manually segmented from the scanned images. This dataset consists of segmented scanned images of handwritten Manipuri Meetei-Mayek characters (Mapi Mayek, Lonsum Mayek, Cheitap Mayek, Cheising Mayek, Khutam Mayek) of size 128X128 pixels in .JPG format as well as in .MAT format.
DOI: 10.6084/m9.figshare.9917741
title: ONE test

description: TestThis is a test dataset.<br><br>[experimental] ONE interface<br><br>The data is available via the ONE interface.<br><br>Installation instructions here.<br><br><br>To search and download this dataset:<br><br><br>import onelight as one<br>sessions = one.search(['trials']) # search for all sessions that have a `trials` object<br>session = sessions[0] # take the first session<br>trials = one.load_object(session, 'trials') # load the trials object<br>print(trials.intervals) # trials is a Bunch, values are NumPy arrays or pandas DataFrames<br>print(trials.goCue_times)<br><br><br>
DOI: 10.6084/m9.figshare.9917210.v1
title: Evaluation of Regional Climate Models using Regionally-Optimized GRACE Mascons in the Amery and Getz ice shelves basins, Antarctica - DATA

description: Time-series for the regionally-optimized GRACE mascons and the corresponding Mass Budget Method time-series with RACMO2.3p1, RACMO2.3p2, and MAR3.6.41 regional climate models, as described in Mohajerani et al. (2019) are provided as individual ascii files. <br> <b>List of Files:</b><i>GRACE FILES</i>Amery_GRACE-CaronGIA.txtGetz_GRACE-CaronGIA.txt<br> -- Columns --GRACE months (format 03i)Decimal Years (format 12.2f)Mass in Gigatons (format 12.4f)Monthly Uncertainty in Gigatons (format 12.4f)<br> <i>MBM FILES</i>Amery_SMB(MAR3.6.41)-D.txtAmery_SMB(RACMO2.3p2)-D.txt Amery_SMB(RACMO2.3p1)-D.txt Getz_SMB(MAR3.6.41)-D.txt Getz_SMB(RACMO2.3p2)-D.txtGetz_SMB(RACMO2.3p1)-D.txt<br> <i>MBM FILES adjusted by RACMO2.3p1</i>Amery_SMB(MAR3.6.41)-D_adjusted-by-RACMO2.3p1.txt Amery_SMB(RACMO2.3p2)-D_adjusted-by-RACMO2.3p1.txt Totten+Moscow_SMB(MAR3.6.41)-D_adjusted-by-RACMO2.3p1.txtTotten+Moscow_SMB(RACMO2.3p2)-D_adjusted-by-RACMO2.3p1.txt<br> -- Columns --Decimal Years (format 12.3f)Mass in Gigatons (format f)Monthly Uncertainty in Gigatons (format f)<br> <b>Please Cite the paper below when using the provided dataset</b>Citation:Mohajerani, Y., Velicogna, I., & Rignot, E. (2019). Evaluation of Regional Climate Models using Regionally-Optimized GRACE Mascons in the Amery and Getz ice shelves basins, Antarctica.Geophysical Research Letters. doi:10.1029/2019GL084665
DOI: 10.6084/m9.figshare.9917171
title: 10-year bird observation data from two routes in the state of Chihuahua, Mexico

description: Data from a 10-year period of bird observation from two routes in the state of Chihuahua. Six files are provided: a) spss: it contains species' count for the 10-year period, per year, per species; b) inv: contains the name of each species in English, Spanish and their scientific names, as well as the observation routes; c) tags: it has the Spanish, English and scientific names of all species in the form of tags; d) species, it contains sums in order to determine species' movements throught the routes of observation; e) la regina route: contatins latitudes and longitudes for each stop within La Regina route; f) santa monica route: contatins latitudes and longitudes for each stop within Santa Monica route.
DOI: 10.6084/m9.figshare.9917162
title: Crowdsourced datasets to study the generation and impact of text highlighting in classification tasks

description: Here we present the datasets derived from our experiments on using crowdsourcing for document classification tasks. These experiments resemble a two-step process that first highlights excerpts from the text and then leverage these to workers for classification. Thus our experiments groups into highlighting generation and classification. For generating highlights, we leverage crowdsourcing and automatic approaches such us extractive summarization and question answering models. For our classification experiments, we consider documents from two different domains: systematic literature reviews and amazon product reviews. Specifically, we study how highlighting text passages could aid workers in judging the relevance of a document given an input question. We spec these datasets to benefit not only to study these particular problem domains but a broader set of classification problems where individual judgments from workers are scarce.<br><br>In a nutshell, the datasets represent two kinds of tasks:<br>- classification tasks with highlighting support.- highlighting tasks, where the workers highlight evidence.<br><b>Classification tasks</b><br>In this task, workers classified documents based on a given predicate. <br><b>classification tasks using crowdsourced highlights</b><br>Files:<i>- classification_amazon-crowd-highlights.csv</i><i>- classification_oa-crowd-highlights.csv</i><i>- classification_tech-crowd-highlights.csv</i><i>- classification_tech-3x12-crowd-highlights.csv</i><i>- classification_tech-6x6-crowd-highlights.csv</i><br><b>classification tasks using ML-generated highlights</b><br>Files:<i>- classification_amazon-ML-highlights.csv</i><i>- classification_oa-ML-highlights.csv</i><i>- classification_tech-ML-highlights.csv</i><br><br><b>Highlighting tasks</b><br><b>crowdsourced highlights</b><br><br>In this task, workers highlighted excerpts from documents that are relevant to a given predicate, to support future classification tasks.<br>File: <i>crowdsourced_highlights.csv.</i><br><i><br></i>The file contains one line per highlight (generated by one worker); the column that holds the highlighted fragment(s) is <i>highlighted_text</i>. The <i>highlighted_text</i> is a "list of lists" (Python syntax), so iterating over this list will give you the text fragment generated by one worker. Also, the <i>experiment</i> column indicates domain + task design. So, to get the highlights used in the classification experiments, use the rows that end with <i>"-highlight"</i>.<br><br><b>ML-generated highlights</b><br>We also consider automatic approaches to generate text highlights — specifically, extractive summarization and question-answering models.<br><br>File: <i>ml_highlights.csv.</i>
DOI: 10.6084/m9.figshare.9917111
title: Abundance data

description: Raw data for Progress in Oceanography submission. Grigor et al. "Non-carnivorous feeding in Arctic chaetognaths".Chaetognath abundance data.