cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/v1rjpf/ak8bod
title: cbb20171106_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/cagyoz
title: cbb20170121_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/xy65hc
title: cbb20170919_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/wwxt9b
title: cbb20170401_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/he5bbf
title: cbb20171004_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/3vbmwp
title: cbb20170703_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3b6zs3
title: cbb20170202_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/mp7arw
title: cbb20170721_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/ahg8df
title: cbb20170605_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/ndpprv
title: cbb20171211_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/hlrwcc
title: cbb20171128_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/acjqlv
title: cbb20171208_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ocwmtr
title: cbb20170131_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rhwpau
title: cbb20170506_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jmiwgx
title: cbb20171010_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/c7a0av
title: cbb20170515_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lmaysm
title: cbb20170602_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kmxmbe
title: cbb20171228_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xb0zfb
title: cbb20170107_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5f4zmn
title: cbb20170510_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gasy04
title: cbb20170309_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dioh4r
title: cbb20170828_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/0ky1um
title: cbb20170513_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/t2iquf
title: cbb20170221_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3bgnms
title: cbb20170811_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kuxtqm
title: cbb20170502_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kffuek
title: cbb20170731_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lglx3d
title: cbb20170618_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/a0rxtf
title: cbb20171225_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/edkas8
title: cbb20170105_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qxxfsg
title: cbb20171124_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5wntqy
title: cbb20170524_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/q1ph4c
title: cbb20170526_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/haon95
title: cbb20171102_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kjdhnh
title: cbb20171101_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ogxnju
title: cbb20170418_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/scd3f4
title: cbb20170722_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qd9qvy
title: cbb20171201_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dx2ibl
title: cbb20171210_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/upzdfd
title: cbb20170530_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wy8q7t
title: cbb20170403_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ihc0bb
title: cbb20170407_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/9sdo0n
title: cbb20170119_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/atovba
title: cbb20170129_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/b9wl1g
title: cbb20170621_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cgloda
title: cbb20170331_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wrlplw
title: cbb20171230_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tisjgx
title: cbb20170719_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/x7yzso
title: cbb20171017_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kpq2x2
title: cbb20171113_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xcntkw
title: cbb20170114_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vliz5f
title: cbb20170825_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/aych6h
title: cbb20170726_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/9yxy3c
title: cbb20170916_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/c0yjde
title: cbb20170322_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hwpw8w
title: cbb20170404_03.txt.gz

description: :unav
DOI: 10.5281/zenodo.3908188
title: Supporting information for Article "Hydrothermal carbonization for sludge disposal in Germany: A comparative assessment for industrial-scale scenarios in 2030"

description: These supporting information files provides details of all case and scenario assumptions, scenario factors, sensitivity assumptions and factors as well as calculation results (PDF file) as well as the detailed backgound calculations (ZIP files with MS Excel Sheets) referred to in the article "Hydrothermal carbonization for sludge disposal in Germany: A comparative assessment for industrial-scale scenarios in 2030" (Journal of Industrial Ecology).
DOI: 10.5281/zenodo.3908187
title: Supporting information for Article "Hydrothermal carbonization for sludge disposal in Germany: A comparative assessment for industrial-scale scenarios in 2030"

description: These supporting information files provides details of all case and scenario assumptions, scenario factors, sensitivity assumptions and factors as well as calculation results (PDF file) as well as the detailed backgound calculations (ZIP files with MS Excel Sheets) referred to in the article "Hydrothermal carbonization for sludge disposal in Germany: A comparative assessment for industrial-scale scenarios in 2030" (Journal of Industrial Ecology).
DOI: 10.7910/dvn/v1rjpf/jkihep
title: cbb20170327_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rh3cih
title: cbb20170311_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cupyjg
title: cbb20170413_03.txt.gz

description: :unav
DOI: 10.5281/zenodo.821567
title: Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.

description: <strong>Data related to the following publication: </strong> Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070 <strong>Brief description of dataset:</strong> The stimulus was presented on a TFT monitor (size: 41,8° x 24,3°) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system. The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65°) and therefore the patch size was the same (“SizeCon”) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (“LumCon”). After a random time between 400 ms and 700 ms after stimulus offset the trials ended. In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants. EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of ±100 µV within an interval of 100 ms were excluded in an artifact rejection step.
description: This work was supported by a stipend to PNH from the Friedrich-Ebert-Stiftung (FES) and by Deutsche Forschungsgemeinschaft (SFB/TRR-135/A2 and IRTG-1901-BrainAct).
DOI: 10.5281/zenodo.821566
title: Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.

description: <strong>Data related to the following publication: </strong> Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070 <strong>Brief description of dataset:</strong> The stimulus was presented on a TFT monitor (size: 41,8° x 24,3°) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system. The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65°) and therefore the patch size was the same (“SizeCon”) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (“LumCon”). After a random time between 400 ms and 700 ms after stimulus offset the trials ended. In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants. EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of ±100 µV within an interval of 100 ms were excluded in an artifact rejection step.
description: This work was supported by a stipend to PNH from the Friedrich-Ebert-Stiftung (FES) and by Deutsche Forschungsgemeinschaft (SFB/TRR-135/A2 and IRTG-1901-BrainAct).
DOI: 10.7910/dvn/v1rjpf/plm6m2
title: cbb20171005_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fto2ae
title: cbb20170914_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jeczj0
title: cbb20170917_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zpgwgu
title: cbb20171105_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hfdaos
title: cbb20170505_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/r0ixb1
title: cbb20170321_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/vaoqaj
title: cbb20170709_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/pje6ix
title: cbb20170727_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rwv1jr
title: cbb20170927_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bxzzzv
title: cbb20170128_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fodov5
title: cbb20171107_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/0a9xtj
title: cbb20170123_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pqpidh
title: cbb20170122_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bvdtuo
title: cbb20171019_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/ftm4so
title: cbb20170923_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/uppng5
title: cbb20170907_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/dzn1pz
title: cbb20170617_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/zjohnu
title: cbb20170627_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/tshfyk
title: cbb20171104_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/zmjwdr
title: cbb20170529_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ma9tmg
title: cbb20170214_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/iqvwo6
title: cbb20170326_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/uq3kwe
title: cbb20170611_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/lozkcc
title: cbb20170522_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/enza2n
title: cbb20170101_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/plcxee
title: cbb20170423_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/l5guj1
title: cbb20170209_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/xkn1ai
title: cbb20171024_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/pvbamz
title: cbb20170425_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/g5clbb
title: cbb20170325_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/bn0qn8
title: cbb20170905_03.txt.gz

description: :unav
DOI: 10.21227/23pe-4m84
title: Nurse Care Activities Datasets: In laboratory and in real field

description: Nurse Care Activity RecognitionActivity recognition is specially gaining attention in care facilities for remote monitoring of daily activities for elders living alone and automatic record creation for nurses in hospitals with a goal of providing better care to patients. One challenge for these applications is bridging the gap between models created within the lab and models for real life. In this challenge, participants will create methods for recognizing 12 activities conducted by nurses in both lab and in real-life settings. In real world, there is high chance of getting missing labels during experiments since users are busy at work. The main challenge is leveraging laboratory data to improve models in real-life. This dataset consists of data collected in both settings.
DOI: 10.7910/dvn/v1rjpf/sovzm1
title: cbb20171001_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/027ygx
title: cbb20170606_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/i6yjzp
title: cbb20170702_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/ncuult
title: cbb20170106_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/s1oni8
title: cbb20170204_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/foysec
title: cbb20170819_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/4w5o6m
title: cbb20171018_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/qqx4mf
title: cbb20170405_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/a1acb5
title: cbb20170302_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/h1kcfc
title: cbb20170429_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/0qs5md
title: cbb20171219_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/1wuyuh
title: cbb20170308_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/gbfswi
title: cbb20170116_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/6rknuc
title: cbb20170422_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/5ykbqa
title: cbb20170612_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/wm3fnx
title: cbb20170528_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/bxii44
title: cbb20170616_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/njuxt4
title: cbb20170912_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/agnins
title: cbb20170210_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/bstgea
title: cbb20171218_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/8knqu1
title: cbb20171224_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/0q2x9m
title: cbb20170511_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/gg6hcc
title: cbb20170120_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/wlir7p
title: cbb20170812_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/qyyl2x
title: cbb20171229_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/804rzj
title: cbb20171207_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/dhcxzd
title: cbb20170815_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/zwmsy5
title: cbb20170313_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/4khrzd
title: cbb20170104_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/oqvmwr
title: cbb20171214_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/vellgf
title: cbb20170118_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/evtiuo
title: cbb20170820_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/padgfs
title: cbb20170514_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/0zk5ov
title: cbb20170827_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/poasdg
title: cbb20170212_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/2eor2w
title: cbb20171118_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/fhul5c
title: cbb20170226_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/6ejigs
title: cbb20170328_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/0pc1gr
title: cbb20171203_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/pfxuhf
title: cbb20170609_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/qsv5ux
title: cbb20170117_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/5hwhjp
title: cbb20170218_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/cpjcr9
title: cbb20170720_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/yg3xym
title: cbb20171109_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/lpbiq9
title: cbb20170717_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/etvj2k
title: cbb20171011_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/qgvofu
title: cbb20170710_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/bztpwy
title: cbb20170520_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/ojy2pp
title: cbb20170310_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/fukcy4
title: cbb20170519_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/o3dwhl
title: cbb20170629_03.txt.gz

description: :unav
DOI: 10.15156/bio/sh1407299.08fu
title: SH1407299.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1407300.08fu
title: SH1407300.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1407301.08fu
title: SH1407301.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1407302.08fu
title: SH1407302.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.