cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ufynug
title: Occurrence Download

description: A dataset containing 39664 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brassica napus L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2000",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "not" : {
 "InstitutionCode" : [
 "is one of (iNaturalist)"
 ]
 }
 }
 ]
}

 The dataset includes 39664 records from 744 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021189-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.1298603
title: Evaluation Datasets For Dcase 2018 Bird Audio Detection

description: Evaluation data audio for the DCASE 2018 Bird Audio Detection task (Task 3).


	
	<strong>Crowdsourced dataset, UK ("warblrb10k")</strong> - a held-out set of 2,000 recordings from the same conditions as the Warblr development dataset.
	
	
	<strong>Remote monitoring dataset, Chernobyl ("Chernobyl")</strong> - 6,620 audio clips collected from unattended remote monitoring equipment in the Chernobyl Exclusion Zone (CEZ). This data was collected as part of the TREE (Transfer-Exposure-Effects) research project into the long-term effects of the Chernobyl accident on local ecology. The audio covers a range of birds and includes weather, large mammal and insect noise sampled across various CEZ environments, including abandoned village, grassland and forest areas.
	
	
	<strong>Remote monitoring night-flight calls, Poland ("PolandNFC")</strong> - 4,000 recordings from Hanna Pamuɫa's PhD project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, Poland, using Song Meter SM2 units with microphones mounted on 3–5 m poles. For this challenge, we use a subset derived from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls.
DOI: 10.5281/zenodo.1298604
title: Evaluation Datasets For Dcase 2018 Bird Audio Detection

description: Evaluation data audio for the DCASE 2018 Bird Audio Detection task (Task 3).


	
	<strong>Crowdsourced dataset, UK ("warblrb10k")</strong> - a held-out set of 2,000 recordings from the same conditions as the Warblr development dataset.
	
	
	<strong>Remote monitoring dataset, Chernobyl ("Chernobyl")</strong> - 6,620 audio clips collected from unattended remote monitoring equipment in the Chernobyl Exclusion Zone (CEZ). This data was collected as part of the TREE (Transfer-Exposure-Effects) research project into the long-term effects of the Chernobyl accident on local ecology. The audio covers a range of birds and includes weather, large mammal and insect noise sampled across various CEZ environments, including abandoned village, grassland and forest areas.
	
	
	<strong>Remote monitoring night-flight calls, Poland ("PolandNFC")</strong> - 4,000 recordings from Hanna Pamuɫa's PhD project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, Poland, using Song Meter SM2 units with microphones mounted on 3–5 m poles. For this challenge, we use a subset derived from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls.
DOI: 10.4225/08/5b33415e9ded6
title: Parkes observations for project P456 semester 2018APRS_06

description: The Parkes Pulsar Timing Array (PPTA) project has three primary goals: (a) detection of gravitational waves from astronomical sources, (b) establishment of a pulsar timescale, and (c) improvement of our understanding of Solar-system dynamics. The PPTA is the oldest of three international pulsar timing groups. We have the smallest telescope and the smallest group, but the best measurements and the best results. Our bound on the strength of the incoherent gravitational wave background is the only bound which significantly constrains theoretical models. We have maintained our leading position because: we have the southern sky; we have a small but well-focussed group; we have the best-calibrated receivers; and we have been able to observe with an almost regular cadence of 15 to 20 days at three different wavelengths. With this proposal we aim to maintain our pre-eminent position in the field.

Unlike most observing proposals, this is a continuing proposal for which the observations will continue to improve bounds on ultra-low-frequency gravitational waves until they are finally detected. Continued Parkes observations will remain valuable at least until the first stage of the SKA is able to improve on our sensitivity and observing cadence. Even after the gravitational wave background is detected we will want to continue observations in support of the nascent field of gravitational wave astronomy!
DOI: 10.4225/08/5b33418fbbf27
title: Parkes observations for project P595 semester 2018APRS_04

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data.  The data from some PULSE@Parkes observations are used to support the FERMI Gamma ray mission, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.4225/08/5b334199f0b41
title: Parkes observations for project P737 semester 2018APRS_08

description: Pulsar observations using the Parkes radio telescope have led to some of the most exciting astronomical results in the last few years.  The aim of this proposal is to carry out tests of the new backend instruments which cannot be carried out during standard maintenance periods. High precision pulsar timing experiments require cross-coupling in the feed to be measured.  This can only be done using observations of PSR J0437-4715 from rise-to-set.  We propose to undertake such observations and make the results available for any users of the telescope.  We will also use P737 to commission new receivers such as the UWB.
DOI: 10.4225/08/5b3341de84d69
title: Parkes observations for project P885 semester 2018APRS_03

description: We propose to observe PSR J1622-4950 and 1E1547.0-5408 weekly, two
of three magnetars known to currently emit radio pulsations, to
track their rotation, flux density, and polarisation. Less frequently
we will monitor XTE J1810-197, which used to emit radio pulsations,
to be on the lookout for its revival - as just happened with PSR
J1622-4950, which turned on in April 2017 after more than 2 years dormant!
These observations yield a continuous record of torque, illuminating
the continued release of magnetic energy. Our broader aims are to
develop a better understanding of the dynamical behaviour of magnetar
magnetospheres, and to establish the conditions under which radio
emission takes place therein, in addition to characterising their
variable radio emission in order to place constraints on the emission
mechanisms, which in at least some respects differ from those of
ordinary pulsars.
DOI: 10.17632/gdj85sf2dc.1
title: RbAp48 Protein is a critical component of GPR158/OCN signaling and Ameliorates Age-Related Memory Loss
DOI: 10.4225/08/5b33437a8a5b4
title: Parkes observations for project P574 semester 2018APRS_02

description: We request time to observe 260 pulsars on a regular basis in order to provide the accurate ephemerides necessary for the detection and characterisation of gamma-ray pulsars with the Fermi satellite. The main science goals are to increase the number of known gamma-ray pulsars (both radio loud and radio quiet), to determine accurate pulse profiles, and to characterise their high energy (phase-resolved) spectra. In the radio, the observations will also allow us to find glitches, characterise timing noise, investigate dispersion and rotation measure variability, and enhance our knowledge of single pulse phenomenology. To date, we
are (co-)authors on 70 papers arising from the collaboration and P574 data. The data have contributed to the PhD theses of students from Bordeaux, Stanford, Swinburne and Oxford.
DOI: 10.5281/zenodo.1297449
title: Example Dataset For 18S Rrna Microbial Community Analysis

description: Original, trimmed (sequencing primers removed) and trimmed+subsampled reads for the Study Group lesson on Microbial Ecology.
DOI: 10.17632/2n823pf39s.1
title: Provident dataset
DOI: 10.17632/kkvz38dvzf.1
title: Replication data for "Spatially-explicit effects of seed and fertilizer intensification for maize in Tanzania"

description: This dataset contains the replication materials for the article in Land Use Policy titled "Spatially-explicit effects of seed and fertilizer intensification for maize in Tanzania" available online at https://doi.org/10.1016/j.landusepol.2018.06.033 Please see "README.txt".
DOI: 10.5281/zenodo.1298555
title: The Italian Music Dataset

description: <strong>Overview</strong>

The dataset is built by exploiting the Spotify and SoundCloud APIs. It is composed of over 14,500 different songs of both famous and less famous Italian musicians. Each song in the dataset is identified by its Spotify id and its title. Tracks' metadata include also lemmatized and POS-tagged lyrics and, in the most of cases, ten musical features directly gathered from Spotify. Musical features include acousticness (float), danceability (float), duration_ms (int), energy (float), instrumentalness (float), liveness (float), loudness (float), speechiness (float), tempo (float) and valence (float). All features range from 0.0 to 1.0 except for loudness that typically ranges between -60 and 0 db, the tempo that represents beats per minute (BPM) and the duration that represents the track in milliseconds. For further information refer to the Spotify's documentation at Spotify Documentation

For further information regarding the dataset and the related project visit SoBigData Catalogue
description: {"references": ["\"The Italian Music Superdiversity. Geography, Emotion and Language: one resource to find them, one resource to rule them all.\", L. Pollacci, R. Guidotti, G. Rossetti, F.Giannotti, D. Pedreschi, In Multimedia Tools and Applications."]}
DOI: 10.5281/zenodo.1298556
title: The Italian Music Dataset

description: <strong>Overview</strong>

The dataset is built by exploiting the Spotify and SoundCloud APIs. It is composed of over 14,500 different songs of both famous and less famous Italian musicians. Each song in the dataset is identified by its Spotify id and its title. Tracks' metadata include also lemmatized and POS-tagged lyrics and, in the most of cases, ten musical features directly gathered from Spotify. Musical features include acousticness (float), danceability (float), duration_ms (int), energy (float), instrumentalness (float), liveness (float), loudness (float), speechiness (float), tempo (float) and valence (float). All features range from 0.0 to 1.0 except for loudness that typically ranges between -60 and 0 db, the tempo that represents beats per minute (BPM) and the duration that represents the track in milliseconds. For further information refer to the Spotify's documentation at Spotify Documentation

For further information regarding the dataset and the related project visit SoBigData Catalogue
description: {"references": ["\"The Italian Music Superdiversity. Geography, Emotion and Language: one resource to find them, one resource to rule them all.\", L. Pollacci, R. Guidotti, G. Rossetti, F.Giannotti, D. Pedreschi, In Multimedia Tools and Applications."]}
DOI: 10.25827/h8k4-k744
title: E. Coli and Field Water Quality

description: This information was collected and made available to the Stormwater Quality Team to help make decisions about water quality in the Rio Grande, and look at contributions from different parts of the city (e.g., North Diversion Channel, Southside Wastewater Reclamation Plant).

2007 NMED/SWQB Standard Operating Procedures were used for field protocols for collecting E. coli samples, with the amendment of taking one picture per site instead of four.

https://www.env.nm.gov/swqb/documents/swqbdocs/MAS/SOP/2007/SWQB-MASStandardOperatingProcedures2007.pdf
DOI: 10.15151/esrf-es-60226184
title: Holland-Belgium BAG
DOI: 10.17632/h5vb37rspw.1
title: Data for: Tumor necrosis factor-alpha protein concentrations in bronchoalveolar lavage fluid from healthy horses and horses with severe equine asthma
DOI: 10.5061/dryad.1n51337/1
title: Data from: Evaluating tools for the spatial management of fisheries

description: These data contain, microsatellite allele scores, otolith microchemistry readings and morphometric truss measurements of yellowtail snapper.
DOI: 10.7910/dvn/v1rjpf/fpftrl
title: cbb20150509_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lldmc0
title: cbb20150831_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ead82v
title: cbb20150730_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/blkwmp
title: cbb20151119_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ykzc8j
title: cbb20150714_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gytfjl
title: cbb20150531_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vw1lv4
title: cbb20150120_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ooygzm
title: cbb20150410_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fjuvio
title: cbb20151117_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/1e3h8m
title: cbb20150223_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/clm7bu
title: cbb20150102_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/anwka0
title: cbb20150320_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lslhze
title: cbb20150226_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hqz20r
title: cbb20151220_03.txt.gz

description: :unav
DOI: 10.5281/zenodo.3904738
title: The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data

description: This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER
description: This study was supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) (EP/L016052/1), the Royal Academy of Engineering (RF201617\16\23)(RF/132). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). T.O. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (220204/Z/20/Z).
DOI: 10.7910/dvn/v1rjpf/sqfmjh
title: cbb20150824_03.txt.gz

description: :unav
DOI: 10.5281/zenodo.3904737
title: The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI: Supporting Data

description: This data was created to perform the first experiments with the SILVER method of optimizing radial MRI acquisition. The files are mainly .mat files containing the numerical data used to assess the SILVER method using MATLAB (Version R2018b). Instructions on how to use the data and how to reproduce the experiments are available on https://github.com/SophieSchau/SILVER
description: This study was supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) (EP/L016052/1), the Royal Academy of Engineering (RF201617\16\23)(RF/132). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). T.O. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (220204/Z/20/Z).
DOI: 10.7910/dvn/v1rjpf/jsynbb
title: cbb20151103_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/aykcr1
title: cbb20150107_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hv6vqq
title: cbb20150519_03.txt.gz

description: :unav
DOI: 10.25403/upresearchdata.12562337
title: Predicting parturition in Beagles using foetal membrane measurements

description: Daily measurements of selected foetal membranes and compartments from 17 to 32 days of gestation in Beagles
DOI: 10.7910/dvn/v1rjpf/igu8if
title: cbb20150427_03.txt.gz

description: :unav
DOI: 10.25403/upresearchdata.12562337.v1
title: Predicting parturition in Beagles using foetal membrane measurements

description: Daily measurements of selected foetal membranes and compartments from 17 to 32 days of gestation in Beagles
DOI: 10.7910/dvn/v1rjpf/pnzdru
title: cbb20150607_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/7gi1nz
title: cbb20150830_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/q92vaq
title: cbb20150415_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rw3kor
title: cbb20151015_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/4feoly
title: cbb20150424_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5lip9x
title: cbb20150610_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ppfyr1
title: cbb20150319_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cyazko
title: cbb20150214_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kc5suc
title: cbb20150303_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/9ciq50
title: cbb20151022_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wcndzd
title: cbb20150209_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/b3rgzr
title: cbb20151110_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qopxoh
title: cbb20151228_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dqkyxv
title: cbb20150428_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kc9ni9
title: cbb20151019_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/6wx0pk
title: cbb20150429_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/izreix
title: cbb20150521_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/y9hojv
title: cbb20150807_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qzwcst
title: cbb20150403_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8ildjs
title: cbb20150106_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tt2cli
title: cbb20151203_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zytqh7
title: cbb20150524_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fajaf6
title: cbb20150301_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sywede
title: cbb20150103_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/t6xovq
title: cbb20150604_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kydxtq
title: cbb20150316_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/os5xns
title: cbb20150704_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mqjtwm
title: cbb20150321_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/d1zmfh
title: cbb20150108_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mudrgc
title: cbb20151201_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tixevt
title: cbb20150527_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qu845j
title: cbb20151111_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3kbvyi
title: cbb20150819_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ha5ryy
title: cbb20151003_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rutdkz
title: cbb20151126_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/b2gqy3
title: cbb20150408_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/if1rju
title: cbb20150309_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/h7hk6r
title: cbb20150802_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tsj3my
title: cbb20150728_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/7rbry8
title: cbb20150221_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lzkhux
title: cbb20151021_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/g16mi2
title: cbb20150529_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/an1lwp
title: cbb20150901_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ho4opw
title: cbb20151017_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jqcavy
title: cbb20151123_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zyze8r
title: cbb20150718_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zrjawd
title: cbb20150104_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/r5e4e8
title: cbb20150502_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8si1to
title: cbb20151004_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tkt9y2
title: cbb20150111_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/f9kfzu
title: cbb20150109_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5bcy6x
title: cbb20151016_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/yzji1n
title: cbb20151029_03.txt.gz

description: :unav
DOI: 10.21227/sc8c-2r02
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.7910/dvn/v1rjpf/kcbiau
title: cbb20150827_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/q0o2rv
title: cbb20151008_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/2p9dak
title: cbb20150430_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/c7ejn7
title: cbb20150326_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tdhcsz
title: cbb20151107_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ty22ny
title: cbb20150626_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/7eej4l
title: cbb20150725_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/w1klyf
title: cbb20151204_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wwues2
title: cbb20150512_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pgpxif
title: cbb20150404_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/94sfbk
title: cbb20150307_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vieb6i
title: cbb20151104_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/s78zag
title: cbb20150705_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ugrk2l
title: cbb20150311_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8bpuwo
title: cbb20150407_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/js6bom
title: cbb20151024_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ay43bt
title: cbb20150601_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hbf4ou
title: cbb20150702_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gzpsib
title: cbb20150628_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gac278
title: cbb20150110_03.txt.gz

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