cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/ddc8-mg42
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 17.8 Million plus (collecting since Feb 1, 2020)Update: I have been receiving a lot of requests asking to add the user's location for each collected tweets. Therefore, the tweets collected after March 20, 2020, 00:00 GMT+5:45 will have the tweet ID column in each database.The first column has date and time in the Unix timestamp. You can easily make it readable using pandas: df['date'] = pd.to_datetime[df['unix'], unit='ms'].Each database (*.db) contain five columns.First column: date and time of the tweetSecond column: tweet ID (Tweets collected after March 20, 2020, 00:00 GMT+5:45)Third column: tweetFourth column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live.To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the last column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.21223/p3/h3bhdr/wu9g2i
title: Data_Dictionary.xls

description: :unav
DOI: 10.21223/p3/h3bhdr/3hokrf
title: Solid.xls

description: :unav
DOI: 10.21223/p3/xjja5z/ksse5f
title: Raw_data.xls

description: :unav
DOI: 10.21223/p3/xjja5z/jlfqlp
title: Material_list.xls

description: :unav
DOI: 10.21223/p3/xjja5z/m7talm
title: Data_dictionary.xls

description: :unav
DOI: 10.21227/s51q-a764
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 17.8 Million plus (collecting since Feb 1, 2020)Update: I have been receiving a lot of requests asking to add the user's location for each collected tweets. I've made necessary changes to the model to include this detail. Tweets collected after March 20, 2020 00:00 GMT+5:45 will have the user's location information and tweet ID.The first column has date and time in the Unix timestamp. You can easily make it readable using pandas: df['date'] = pd.to_datetime[df['unix'], unit='ms'].Each database (*.db) contain five columns.First column: date and time of the tweetSecond column: tweet ID Third column: user's locationFourth column: tweetFifth column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live.To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the last column. New databases will be added to this dataset every week. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.21223/p3/p78gmu/p8asxa
title: Data_dictionary.xls

description: :unav
DOI: 10.21223/p3/p78gmu/e9pxqe
title: PTYield112012_MAJ.tab

description: :unav
DOI: 10.21223/p3/p78gmu/hgounz
title: PTYield102012_CIPHQ.tab

description: :unav
DOI: 10.21223/p3/p78gmu/biuxog
title: PTYield072012_CIPSRM-1.tab

description: :unav
DOI: 10.21223/p3/p78gmu/nwtkcj
title: PTLate blight092012_OXAPMP.tab

description: :unav
DOI: 10.21223/p3/r0tadb/x6cd8c
title: Potato Virus X.xls

description: :unav
DOI: 10.15468/ikee4c
title: An inventory of the spider species of Barcelonnette (France), with taxonomic notes on Piniphantes agnellus n. comb. (Araneae, Linyphiidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Milano, Filippo, Mammola, Stefano, Rollard, Christine, Leccia, Marie-France, Isaia, Marco (2019): An inventory of the spider species of Barcelonnette (France), with taxonomic notes on Piniphantes agnellus n. comb. (Araneae, Linyphiidae). Zoosystema 41 (4): 29-58, DOI: 10.5252/zoosystema2019v41a4
DOI: 10.21223/p3/r0tadb/cetkff
title: Data_Dictionary.xls

description: :unav
DOI: 10.21227/16v3-9p38
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 17.8 Million plus (collecting since Feb 1, 2020)Update: I have been receiving a lot of requests asking to add the user's location for each collected tweets. I've made necessary changes to the model to include this detail. Tweets collected after March 20, 2020 00:00 GMT+5:45 will have the user's location information and tweet ID.The first column has date and time in the Unix timestamp. You can easily make it readable using pandas: df['date'] = pd.to_datetime[df['unix'], unit='ms'].Each database (*.db) contain five columns.First column: date and time of the tweetSecond column: tweet ID (Tweets collected after March 20, 2020 00:00 GMT+5:45)Third column: user's location (Tweets collected after March 20, 2020 00:00 GMT+5:45)Fourth column: tweetFifth column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live.To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the last column. New databases will be added to this dataset every week. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/8z40-gg63
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 17.8 Million plus (collecting since Feb 1, 2020)Update: I have been receiving a lot of requests asking to add the user's location for each collected tweets. Therefore, the tweets collected after March 20, 2020, 00:00 GMT+5:45 will have the tweet ID column in each database.The first column has date and time in the Unix timestamp. You can easily make it readable using pandas: df['date'] = pd.to_datetime[df['unix'], unit='ms'].Each database (*.db) contain five columns.First column: date and time of the tweetSecond column: tweet ID (Tweets collected after March 20, 2020, 00:00 GMT+5:45)Third column: tweetFourth column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live.To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the last column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.21223/p3/e6a7ng/i3f0pi
title: Leaf Miner Fly.xls

description: :unav
DOI: 10.21223/p3/e6a7ng/kmwfxq
title: Data_Dictionary.xls

description: :unav
DOI: 10.17632/24vt4rbpyx.1
title: GGG-PositioningSfMScale: Dataset for Testing Scaling and Uncertainty Propagation Through Camera Positioning Data

description: The proposed dataset can be used to test the precision of scaling SfM reconstructed objects using camera positioning data. In addition position uncertainty data from a DJI GPS RTK in X,Y and Z is added, so the propagation of uncertainty from the positioning sensor to the calculated scale can be calculated. The camera positions are also given with any uncertainty, so other types of position uncertainty can be used to test how the system will react with different uncertainty inputs. Images from two objects are given - an angel statue and a wind turbine blade. Images are taken in a semi-circle pattern around the object and a total of 19 images are taken from each object. The positions of the images are given in a separate file, while all the images contain EXIF data with the used camera parameters. All images are captured using Canon 5Ds DSLR camera. More information on how the scale and scale uncertainty can be calculated are present in the referenced paper.
DOI: 10.17632/kfdy77762r.1
title: Substance use disorders: incidence, prevalence, deaths and DALY's, in 1990-2017

description: Datasets from Global Burden of Diseases Study 2019. Mortality, morbidity, prevalence, from use of tobacco, alcohol, opiates, cannabis, and other drugs, the aggregated indicator (Substance use disorders) in 1990-2017. ICD10 codes: I42.6, K70.03, Z72.0, F10-19. Territories: Global, Eastern Europe, Central Europe, Western Europe, Belarus, Russia. Methods. We used indicators of mortality, morbidity, prevalence, and years of life lost, taking into account the DALY disability from tobacco, alcohol, opiates, cannabis, and other drugs. Indicators of harmful health effects from PS presented from 1990 to 2017. Variables standardized by age and sex. Keywords: global burden of diseases, alcohol, substance use, psychoactive substances.
DOI: 10.17632/tbcbzzmt22.1
title: Pain in MS

description: A dataset comprise raw basic demographic, clinical and detailed quantitative sensory testing (QST) data from three subgroups of individuals: healthy controls, multiple sclerosis (MS) patients with central neuropathic extremity pain and and MS pain-free patients.
DOI: 10.21223/p3/u7phjx/8dd7fo
title: Data_Dictionary.xls

description: :unav
DOI: 10.21223/p3/u7phjx/ndjjnt
title: Root Knot Nematode.xls

description: :unav
DOI: 10.17632/79dp7449jw.1
title: Progression of Cutaneous T-Cell Lymphoma after dupilumab: Case review of 7 patients

description: Supplement 1: Report of Cases Seven patients were prescribed dupilumab for chronic recalcitrant presumed AD (cases 1-4), or as an off-label treatment for patients with MF and severe pruritus (cases 5-7). In all 7 patients, the standard dosing regimen for dupilumab was followed (600mg loading dose, followed by 300mg every 2 weeks subcutaneously). Case vignettes and photographs found below.
DOI: 10.17632/t4d8mv3fxt.1
title: GGG-BenchmarkSfM- Secondary: Secondary Dataset for Benchmarking Close-range SfM Software Performance Captured with Different Cameras

description: The proposed dataset is an addition to the one published here - http://dx.doi.org/10.17632/bzxk2n78s9.1 . It is also aimed at benchmarking the performance of SfM software under varying conditions - image positions, but also used cameras. The dataset is comprised on images of 11 objects with varying shapes, sizes and surface textures. It is divided into three parts: - Objects imaged in a semi-circle only from one side - 9 images per object - Objects imaged in a full circle from only one height - 18 images per object - Objects images in a full circle from multiple heights - 54 images per object Three cameras are used to capture the images - Canon 600D, Canon 6D and Canon 5Ds. All images contain EXIF data with used camera parameters. The images do not come with ground truth. If you require image data, plus ground truth meshes for comparison, please refer to this dataset - http://dx.doi.org/10.17632/bzxk2n78s9.1
DOI: 10.17632/kywtjgm7nf.2
title: Dataset for "The Discontinuous Effect of Economic Performance on Political Turnover"
DOI: 10.17632/6p8tdn5vmm.2
title: western blots for Mol. Cell

description: This dataset is described in the manuscript "Gene architecture and sequence composition underpin selective dependency of long RNAs on components of the nuclear export pathway". See Methods section in the paper, particularly "Western blot".
DOI: 10.3886/e118343v1
title: Organizing Time Banks: Lessons from Matching Markets

description: Data on Time Banks in the US.
DOI: 10.15468/pmqzwm
title: Revisiting Calohypsibiidae and Microhypsibiidae: Fractonotus Pilato, 1998 and its phylogenetic position within Isohypsibiidae (Eutardigrada: Parachela)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Gąsiorek, Piotr, Morek, Witold, Stec, Daniel, Blagden, Brian, Michalczyk, Łukasz (2019): Revisiting Calohypsibiidae and Microhypsibiidae: Fractonotus Pilato, 1998 and its phylogenetic position within Isohypsibiidae (Eutardigrada: Parachela). Zoosystema 41 (6): 71-89, DOI: 10.5252/zoosystema2019v41a6
DOI: 10.5281/zenodo.3718558
title: IO Islamic 2428. Ḥadîḳat-al'âlam, The First Maḳâlah of the History of the Ḳuṭbshâhs and the Niżâms or Âṣafîs of Ḥaidarâbâd

description: IO Islamic 2428. Ḥadîḳat-al’âlam, The First Maḳâlah of the History of the Ḳuṭbshâhs and the Niżâms or Âṣafîs of Ḥaidarâbâd
DOI: 10.5281/zenodo.3718559
title: IO Islamic 2428. Ḥadîḳat-al'âlam, The First Maḳâlah of the History of the Ḳuṭbshâhs and the Niżâms or Âṣafîs of Ḥaidarâbâd

description: IO Islamic 2428. Ḥadîḳat-al’âlam, The First Maḳâlah of the History of the Ḳuṭbshâhs and the Niżâms or Âṣafîs of Ḥaidarâbâd
DOI: 10.21223/p3/rqboqt/mj41vr
title: Viral Kinetics .xls

description: :unav
DOI: 10.21223/p3/rqboqt/d9pia5
title: Data_dictionary.xls

description: :unav
DOI: 10.21223/p3/rqboqt/e7tkph
title: Isolates.xls

description: :unav
DOI: 10.15468/o0crd9
title: First records of Ropalomeridae (Diptera, Acalyptratae) from French Guiana

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ale-Rocha, Rosaly, Pollet, Marc (2019): First records of Ropalomeridae (Diptera, Acalyptratae) from French Guiana. Zoosystema 41 (1): 1-5, DOI: 10.5252/zoosystema2019v41a1
DOI: 10.17632/8rjrmncrhw.2
title: THE PSYCHIATRIC IMPACT OF THE NOVEL CORONAVIRUS OUTBREAK

description: Patients with confirmed or suspected 2019-nCoV may experience might experience boredom, loneliness, and anger. The psychological impact of stressful events related to an infectious disease outbreak may be mediated by peoples' perceptions of those events. The emotional and behavioural reactions of patients and staff are understood to to stress in the face of an overwhelming event.
DOI: 10.21223/p3/vllfpk/hza7ux
title: Yield estimation of Unica potato.xls

description: :unav
DOI: 10.21223/p3/vllfpk/mlxfvs
title: Data_Dictionary.xls

description: :unav
DOI: 10.5281/zenodo.3718561
title: Gradients1-KOK1606-PPPCPN-UW_2020-03-10_v1.0

description: Cruise: KOK1606, Gradients 1<br> Project Name: Simons Foundation, SCOPE, Gradients NPSG The phosphorus content of particulate material (PP) was collected by semi-automated and sequential filtration of the ship’s underway flow through seawater onto acid-washed and combusted GFF filters. Select samples had a piggybacked GFF filter holder in sequence for collection of dissolved organic material and/or evaluation of trapping efficiency of GFF; these samples are termed ‘&lt;0.7 micron P’. Following filtration, filters were placed in histoprep capsules and stored at -80°C for later analysis. Post-cruise, all filters were thawed overnight, combusted at 450°C for 4.5 hours, then extracted with 0.15 M HCl for 1 hour at 60°C. PP was then analyzed spectrophotometrically as SRP in a 1.0 cm cell at 880 nm following Strickland and Parsons (1972). Recovery efficiency was assessed from the analysis of a known dry weight of certified reference material (National Institute of Standards, NIST 1515, apple leaves, certified 0.159% P by weight). The carbon and nitrogen content of particulate material (PC, PN) was collected by semi-automated and sequential filtration of the ship’s underway flow through seawater onto a combusted GFF filter. Select samples had a piggybacked GFF filter holder in sequence for collection of dissolved organic material and/or evaluation of trapping efficiency of GFF; these samples are termed ‘&lt;0.7 micron C’ or '&lt;0.7 micron N'. Following filtration, filters were folded in half and placed in combusted aluminum foil and stored at -80°C for later analysis. Post-cruise, all filters were thawed, acidified, and dried overnight at 60°C. Samples were then balled into Ag and Sn boats and plated for analysis. Organic carbon and total nitrogen content was analyzed using high-temperature combustion (1020 C) on a ThermoQuest NC 2500 elemental analyzer. Three standards with known %C and %N were weighed, folded in tin boats, and analyzed with the samples to develop the calibration equation for each run. The calibration equation was then used to calculate the mass (mg) of carbon and nitrogen in each sample using EAS Clarity Chromatography Station software. The standards are Cystine (Elemental Microanalysis, B2035, 11.66 %TN, 29.99 %TC), Atropine (Elemental Microanalysis, B2002, 4.84 %TN, 70.56%TC), and GoniLab Secondary Standard (low %C)-A1 (an internal standard of sediment from the Gulf of Mexico, MV0803, with low carbon - 0.12 %N, 1.3 %C). Depth of 4m refers to the depth of the seawater intake on the R/V Ka’imikai-O-Kanaloa (KOK). The ship’s location, sea surface temperature and salinity from ship’s underway instrumentation are matched to the mid-point of each PCPN filtration time, and PP data were matched to the closest PCPN filtration.<br>
DOI: 10.21223/p3/j0drye/dkzpqc
title: Data_contig_sequences.rar

description: RsA Plant Kingdom
DOI: 10.21223/p3/j0drye/ppl1z6
title: CIP.sweetpotato.2009.PER.CIPHQ.siRNA-SPVD2.rar

description: 2009-08-26_GAF-10_inserts: sweetpotato cultivar Huachano infected with SPFMV (library prepared ligating 5' adapter first); 2009-08-26_GAF-12_inserts: sweetpotato cultivar Huachano infected with SPFMV and SPCSV (library prepared ligating 5' adapter first); 2009-08-26_GAF-11_inserts: sweetpotato cultivar Huachano infected with SPCSV (library prepared ligating 5' adapter first).
DOI: 10.21223/p3/sl0ald/aeggzv
title: Niassa baseline pag_1.tab

description: :unav
DOI: 10.21223/p3/sl0ald/cti2nm
title: Niassa baseline pag_8.tab

description: :unav
DOI: 10.21223/p3/sl0ald/ed3051
title: Niassa baseline pag_5.tab

description: :unav
DOI: 10.21223/p3/sl0ald/hhmbiu
title: Niassa baseline pag_2.tab

description: :unav
DOI: 10.21223/p3/sl0ald/lazqtp
title: Niassa baseline pag_6.tab

description: :unav
DOI: 10.21223/p3/sl0ald/r6rlw5
title: Niassa baseline pag_4_new.tab

description: :unav
DOI: 10.21223/p3/sl0ald/tjwa2d
title: Niassa Baseline pag_3.tab

description: :unav
DOI: 10.21223/p3/sl0ald/ame9ew
title: Niassa baseline pag_9.tab

description: :unav
DOI: 10.21223/p3/sl0ald/c7knbt
title: Niassa baseline pag_14.tab

description: :unav
DOI: 10.21223/p3/sl0ald/cmjlsz
title: Niassa baseline pag_15.tab

description: :unav
DOI: 10.21223/p3/sl0ald/gumhwf
title: Niassa baseline pag_17.tab

description: :unav
DOI: 10.21223/p3/sl0ald/hcvvvj
title: Niassa baseline pag_12.tab

description: :unav
DOI: 10.21223/p3/sl0ald/jwl5un
title: Niassa baseline pag_13.tab

description: :unav
DOI: 10.21223/p3/sl0ald/ocynxh
title: Niassa baseline pag_18.tab

description: :unav
DOI: 10.21223/p3/sl0ald/st5ywd
title: Niassa baseline pag_16.tab

description: :unav
DOI: 10.21223/p3/sl0ald/vuqhcp
title: Niassa baseline pag_10.tab

description: :unav
DOI: 10.21223/p3/sl0ald/xonahw
title: Niassa baseline pag_11.tab

description: :unav
DOI: 10.25573/serc.11968740.v1
title: Dataset: Carbon stocks and accretion rates for wetland sediment at Miner Slough, Sacramento-San Joaquin Delta, California

description: This data release includes soil properties for one sediment core taken in a tidal scrub-shrub wetland in Miner Slough, Sacramento-San Joaquin Delta, California. The dominant vegetation near the core collection area included Schoenoplectus acutus, Typha sp, and Cornus sericea. A Russian corer was used to collect one soil core and was analyzed for bulk density, fraction organic matter, and accretion rates using 137Cs analysis. Bulk density was measured by drying samples drawn with a 1 cc syringe plug at 80 degrees celsius for 24 hours. Loss on ignition was measured by combusting samples at 500 degrees celsius for 4 hours. Radiocesium activity was determined with the gamma counting method.
DOI: 10.21223/p3/sl0ald/1xzawe
title: Niassa baseline pag_62.tab

description: :unav
DOI: 10.21223/p3/sl0ald/32y8sk
title: Niassa baseline pag_51.tab

description: :unav
DOI: 10.21223/p3/sl0ald/7onjtm
title: Niassa baseline pag_41.tab

description: :unav
DOI: 10.21223/p3/sl0ald/cua62f
title: Niassa baseline pag_21.tab

description: :unav
DOI: 10.21223/p3/sl0ald/dv4flf
title: Niassa baseline pag_20.tab

description: :unav
DOI: 10.21223/p3/sl0ald/ocidsw
title: Niassa baseline pag_19.tab

description: :unav
DOI: 10.21223/p3/sl0ald/pcxseq
title: Niassa baseline pag_32.tab

description: :unav
DOI: 10.21223/p3/sl0ald/reqiq7
title: Niassa baseline pag_22.tab

description: :unav
DOI: 10.21223/p3/sl0ald/3wbb8m
title: Niassa baseline pag_72.tab

description: :unav
DOI: 10.21223/p3/sl0ald/8avxxc
title: Baseline Niassa Report Narratives_Naico_First draft_V5_20140624.pdf

description: :unav
DOI: 10.21223/p3/sl0ald/dzanen
title: BaseLineSurvey Data Dictionary.txt

description: :unav
DOI: 10.21223/p3/sl0ald/fkv8xi
title: Niass baseline pag_4.tab

description: :unav
DOI: 10.21223/p3/sl0ald/hl1jny
title: Niassa baseline pag_63.tab

description: :unav
DOI: 10.21223/p3/sl0ald/ibx1h5
title: Niassa baseline pag_71.tab

description: :unav
DOI: 10.21223/p3/sl0ald/jsc6rv
title: Niassa baseline pag_101.tab

description: :unav
DOI: 10.21223/p3/sl0ald/mweyim
title: Nutritious OFSP Base Survey Questionnaire (English).pdf

description: :unav
DOI: 10.21223/p3/sl0ald/r9s935
title: Niassa baseline pag 7.tab

description: :unav
DOI: 10.5281/zenodo.3717240
title: Total Arctic sea ice extent during the Instrumental Era

description: This dataset contains six different reconstructions of total Arctic sea ice extent during the Instrumental Era. These reconstructions were produced using data assimilation of climate model output and temperature observations. The prior ensembles were drawn from climate model output from both the Community Climate System Model version 4 (CCSM4, Last Millennium simulation Landrum et al., 2013) and Max Planck Institute for Meteorology (MPI-ESM-P, Last Millennium simulation Taylor et al., 2012). Temperature observations were drawn from three different products: 25 HadCRUT, Berkeley Earth (BE, Rohde et al., 2013), and NASA Goddard Institute for Space Studies 26 (GISTEMP, Hansen et al., 2010). These reconstructions are published in Brennan et al. (2020). Brennan, M. K., Hakim, G. J., & Blanchard-Wrigglesworth, E. (2020). Arctic sea-ice variability during the instrumental era. Geophysical Research Letters, 47, e2019GL086843. https:// doi.org/10.1029/2019GL086843
description: MKB was supported by the NSF GRFP program. GH was supported by NSF award AGS-1602223 and NOAA award NA18OAR4310422. EBW was supported by NOAA MAPP grant NA18OAR4310274.
DOI: 10.21223/p3/1bbkln/7edrlj
title: 4 Data_Dictionary_NASPOT11.xlsx

description: :unav
DOI: 10.21223/p3/1bbkln/jaxvwh
title: 2 PPB-Data-NASPOT11-On-farm.tab

description: :unav
DOI: 10.21223/p3/1bbkln/o6elbt
title: 3 PPB-Data-NASPOT11-On-station.xlsx

description: :unav
DOI: 10.21223/p3/1bbkln/t0kg7u
title: 1 Sweetpotato_Release_NASPOT 11_April16-2010.pdf

description: :unav
DOI: 10.5281/zenodo.3717239
title: Total Arctic sea ice extent during the Instrumental Era

description: This dataset contains six different reconstructions of total Arctic sea ice extent during the Instrumental Era. These reconstructions were produced using data assimilation of climate model output and temperature observations. The prior ensembles were drawn from climate model output from both the Community Climate System Model version 4 (CCSM4, Last Millennium simulation Landrum et al., 2013) and Max Planck Institute for Meteorology (MPI-ESM-P, Last Millennium simulation Taylor et al., 2012). Temperature observations were drawn from three different products: 25 HadCRUT, Berkeley Earth (BE, Rohde et al., 2013), and NASA Goddard Institute for Space Studies 26 (GISTEMP, Hansen et al., 2010). These reconstructions are published in Brennan et al. (2020). Brennan, M. K., Hakim, G. J., & Blanchard-Wrigglesworth, E. (2020). Arctic sea-ice variability during the instrumental era. Geophysical Research Letters, 47, e2019GL086843. https:// doi.org/10.1029/2019GL086843
description: MKB was supported by the NSF GRFP program. GH was supported by NSF award AGS-1602223 and NOAA award NA18OAR4310422. EBW was supported by NOAA MAPP grant NA18OAR4310274.
DOI: 10.21223/p3/sjjggu/cuedrd
title: Primers_list.pdf

description: :unav