cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18710/4ucfsf
title: Replication Data for: Variation in Phenolic Chemistry in Zostera marina Seagrass along Environmental Gradients

description: This dataset consists of 9 files containing quantitative data (absorbance and concentration) of individual flavonoids and phenolic acids in the seagrass Zostera marina L. collected from various locations along the coast of Norway. The polyphenolic content was determined by the use of analytical HPLC with UV-Vis detection.
description: Abstract: Chemical ecology has been suggested as a less time-consuming and more cost-efficient monitoring tool of seagrass ecosystems than traditional methods. Phenolic chemistry in Zostera marina samples was analyzed against latitude, sea depth, sample position within a seagrass meadow (periphery or center) and wave exposure. Multivariate data analysis showed that rosmarinic acid correlated moderately positively with depth, while the flavonoids had an overall strong negative correlation with increasing depth—possibly reflecting lack of stress-induced conditions with increasing depth, rather than a different response to light conditions. At a molecular level, the flavonoids were separated into two groups; one group is well described by the variables of depth and wave exposure, and the other group that was not well described by these variables—the latter may reflect biosynthetic dependencies or other unrevealed factors. A higher flavonoid/rosmarinic acid ratio was seen in the periphery of a seagrass meadow, while the contrary ratio was seen in the center. This may reflect higher plant stress in the periphery of a meadow, and the flavonoid/rosmarinic acid ratio may provide a possible molecular index of seagrass ecosystem health. Further studies are needed before the full potential of using variation in phenolic chemistry as a seagrass ecosystem monitoring tool is established.
DOI: 10.18710/pigssk/gbebsz
title: 20201116_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/ikwhom
title: 20200923_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/nnb02g
title: 20201021_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/owbyki
title: 20201110_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/phzbav
title: 20201126_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/taz8ck
title: 20200628_BJN_REDTEC_gal.txt
DOI: 10.18710/q0f8pa/dx8gpt
title: PLAG-30.stl
DOI: 10.18710/q0f8pa/hse4e9
title: PLAG-75.stl
DOI: 10.18710/q0f8pa/ku2gg5
title: PLAG-57.stl
DOI: 10.18710/q0f8pa/njra1c
title: PLAG-32.stl
DOI: 10.18710/q0f8pa/u1jkdf
title: PLAG-97.stl
DOI: 10.18710/8o9zv0/cg5jht
title: 07_Figure_5.PNG
DOI: 10.18710/eazdnv/bf8f7g
title: Figure_10_(continued).jpg

description: Figure 10 (second part) from Koehl et al. (2020).
DOI: 10.18710/mw69ce/sss2da
title: TGO_Ramfjordmoen_ionosonde_data_2019-02.SAO.zip
DOI: 10.18710/oegwzw/1aq2c5
title: 01_ReadMeFile.txt
DOI: 10.18710/pigssk/9awkxb
title: 20201106_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/a9qmsw
title: 20200507_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/ajhbgr
title: 20200819_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/bw1ar5
title: 20200303_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/c1jlum
title: 20200312_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/c1rhwo
title: 20200630_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/caivyv
title: 20200501_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/e0jtju
title: 20200315_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/ezi6g0
title: 20200131_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/hfjvki
title: 20200505_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/hqawpb
title: 20200526_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/kjo0xd
title: 20201028_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/n3polo
title: 20200423_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/nfteio
title: 20200919_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/oscstp
title: 20200611_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/own7xs
title: 20201129_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/ptuojt
title: 20200517_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/qwg5im
title: 20200531_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/sjolqv
title: 20200626_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/uh4l0c
title: 20200227_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/vjdbyo
title: 20200827_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/wzaj0l
title: 20200914_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/xe8ruz
title: 20200103_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/xkqlhf
title: 20200516_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/yfcdxq
title: 20200614_BJN_REDTEC_gps.txt
DOI: 10.18710/q0f8pa/24av3c
title: PLAG-157.stl
DOI: 10.18710/q0f8pa/41h5rw
title: PLAG-49.stl
DOI: 10.18710/q0f8pa/5bmjki
title: OPX188.stl
DOI: 10.18710/q0f8pa/blddrr
title: OPX-17.stl
DOI: 10.18710/q0f8pa/btmrgb
title: OPX63.stl
DOI: 10.18710/q0f8pa/d8n0cx
title: OPX-2.stl
DOI: 10.18710/q0f8pa/hi1qhs
title: PLAG-73.stl
DOI: 10.18710/q0f8pa/s3zj0s
title: OPX66.stl
DOI: 10.18710/q0f8pa/s7x7ug
title: PLAG-74.stl
DOI: 10.18710/q0f8pa/tslakf
title: PLAG-103.stl
DOI: 10.18710/q0f8pa/txczs8
title: OPX208.stl
DOI: 10.18710/q0f8pa/uk3ouz
title: OPX-105.stl
DOI: 10.18710/q0f8pa/uutj4l
title: PLAG-141.stl
DOI: 10.18710/q0f8pa/xmiga8
title: OPX-112.stl
DOI: 10.18710/r0ywne
title: TGO Ramfjordmoen Ionosonde Data July 2019

description: &lt;p&gt;About this dataset: This dataset contains ionosonde data in SAO format and ionograms in PNG format, and covers data from July 01-31, 2019.&lt;/p&gt;&lt;p&gt;About the Tromsø Ionosonde (1993-present): Since 1980, the ionosonde was situated at 69° 35' N, 19° 13' E at Ramfjordmoen near Tromsø, Norway and operated by the Auroral Observatory of the University of Tromsø and later on, in collaboration with QinetiQ. The Lowell ionosonde replaced the Auroral Observatory unit in 1992; see the related publication for details.&lt;/p&gt;
description: TGO Ionosonde Data
DOI: 10.18710/z1apbo/agodzq
title: TGO_Ramfjordmoen_ionosonde_data_2019-11.SAO.zip
DOI: 10.18710/zpfmvk/moqrxa
title: 01_ReadMeFile.txt
DOI: 10.18710/zpfmvk/sbem32
title: TGO_Ramfjordmoen_ionosonde_data_2019-03.PNG.zip
DOI: 10.18710/ckhioe
title: TGO Ramfjordmoen Ionosonde Data October 2019

description: &lt;p&gt;About this dataset: This dataset contains ionosonde data in SAO format and ionograms in PNG format, and covers data from October 01-31, 2019.&lt;/p&gt;&lt;p&gt;About the Tromsø Ionosonde (1993-present): Since 1980, the ionosonde was situated at 69° 35' N, 19° 13' E at Ramfjordmoen near Tromsø, Norway and operated by the Auroral Observatory of the University of Tromsø and later on, in collaboration with QinetiQ. The Lowell ionosonde replaced the Auroral Observatory unit in 1992; see the related publication for details.&lt;/p&gt;
description: TGO Ionosonde Data
DOI: 10.18710/4ucfsf/16vwqm
title: Luteolin_CalibrationCurve_1.txt
DOI: 10.18710/gxdora/bkati7
title: 20201108_BJN_REDOBS_glo.txt
DOI: 10.18710/nwqqbi/3uf6kf
title: fields-11100.dat
DOI: 10.18710/nwqqbi/6x39xx
title: fields-09200.dat
DOI: 10.18710/nwqqbi/j3ju1g
title: fields-07900.dat
DOI: 10.18710/nwqqbi/mw4ijo
title: fields-08100.dat
DOI: 10.18710/nwqqbi/znhnj6
title: fields-07300.dat
DOI: 10.18710/q0f8pa/oofvsf
title: OPX-260.stl
DOI: 10.18710/wa5ycf/ibbakv
title: ALL_GEOMETRIES.txt
DOI: 10.18710/27yhah/dqeswo
title: 01_ReadMeFile.txt
DOI: 10.18710/2zg3ei/ew7pyb
title: Script-Henden et al.2020 JPE-Management action assessment.r

description: R-script to perform analyses and generate plots from the published paper
DOI: 10.18710/a1xxqx/dcceoa
title: TGO_Ramfjordmoen_ionosonde_data_2019-12.PNG.zip
DOI: 10.18710/a3satc/55p8b6
title: actually_data_1994.tab
DOI: 10.18710/ed8hsd/zld0kq
title: results-plots-5-1000.pdf

description: plots for performance metrics obtained by R/PoC.results.SIMULATED.R.
DOI: 10.18710/fkjajr/scmifj
title: S SO MODERN RUSSIAN.xlsx

description: This database contains 998 Modern Russian verbs in S- and SO- collected from the Russian National Corpus. Each verb is assigned a number of tags and is illustrated with examples from the corpus.
DOI: 10.18710/gxdora/4myyhx
title: 20201226_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/8kebkm
title: 20200330_BJN_REDOBS_gps.txt
DOI: 10.18710/kwyih2
title: TGO Ramfjordmoen Ionosonde Data January 2020

description: &lt;p&gt;About this dataset: This dataset contains ionosonde data in SAO format and ionograms in PNG format, and covers data from January 01-31, 2020.&lt;/p&gt;&lt;p&gt;About the Tromsø Ionosonde (1993-present): Since 1980, the ionosonde was situated at 69° 35' N, 19° 13' E at Ramfjordmoen near Tromsø, Norway and operated by the Auroral Observatory of the University of Tromsø and later on, in collaboration with QinetiQ. The Lowell ionosonde replaced the Auroral Observatory unit in 1992; see the related publication for details.&lt;/p&gt;
description: TGO Ionosonde Data
DOI: 10.18710/nwqqbi/1xcjzj
title: fields-01100.dat
DOI: 10.18710/nwqqbi/3d6nlq
title: fields-03900.dat
DOI: 10.18710/nwqqbi/g1mpix
title: fields-02800.dat
DOI: 10.18710/nwqqbi/rgfnlz
title: fields-07100.dat
DOI: 10.18710/pigssk/7htg8r
title: 20200508_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/qiqtdy
title: 20200603_BJN_REDTEC_gps.txt
DOI: 10.18710/q0f8pa/ds9wrl
title: OPX-224.stl
DOI: 10.18710/q0f8pa/s8d3j3
title: OPX-79.stl
DOI: 10.18710/q0f8pa/xaylze
title: OPX-53.stl
DOI: 10.18710/q4dirk/9sedcn
title: a_ 51.00_ 0.34-Fxye_all.eps
DOI: 10.18710/tcwuqn/nc9bdf
title: OBS4.zip

description: Vertical channel files of OBS-4
DOI: 10.18710/vcwbyv/78ioce
title: TGO_Ramfjordmoen_ionosonde_data_2020-06.SAO.zip
DOI: 10.18710/wa5ycf
title: Replication Data for: Multiwavelets applied to metal-ligand interactions: Energies free from basis set errors

description: &lt;h3&gt;Introduction&lt;/h3&gt;
This Dataverse record contains data for reproducing the results in our corresponding journal article. For more information about the computational protocols used to generate the data, please see the journal article or the ChemRxiv entry (see below).

&lt;h3&gt;How to use&lt;/h3&gt;
This data set two data files: molecular coordinates (ALL_GEOMETRIES.txt) and metal-ligand interaction energy data (Raw_Data.csv). These formats lend themselves for easy preparation and analysis with Python.&lt;br&gt;
&lt;br&gt;

For example, in order to load the data set into a Pandas DataFrame, do the following:

&lt;pre&gt;
 &lt;code&gt;
 import pandas as pd
 data = pd.read_csv('Raw_Data.csv')
 &lt;/code&gt;
&lt;/pre&gt;

You can prepare a list of all geometries in the following way:

&lt;pre&gt;
 &lt;code&gt;
 with open('ALL_GEOMETRIES.txt') as f:
 raw_string = f.read()
 
 molecules = [mol.split('\n') for mol in raw_string.split('\n\n')]
 &lt;/code&gt;
&lt;/pre&gt;

The ReadMe file contains descriptions of all data fields found in Raw_Data.csv. All energies are given in Hartrees, and all geometries are given in Angströms.

&lt;h3&gt;Journal article&lt;/h3&gt;
&lt;a href="https://doi.org/10.1063/5.0046023"&gt;Brakestad et al. "Multiwavelets applied to metal–ligand interactions: Energies free from basis set errors". J. Chem. Phys. (2021)&lt;/a&gt;&lt;br&gt;&lt;br&gt;

&lt;h3&gt;Abstract from journal article&lt;/h3&gt;
Transition metal-catalyzed reactions invariably include steps where ligands associate or dissociate. In order to obtain reliable energies for such reactions, sufficiently large basis sets need to be employed. In this paper, we have used high-precision multiwavelet calculations to compute the metal–ligand association energies for 27 transition metal complexes with common ligands, such as H2, CO, olefins, and solvent molecules. By comparing our multiwavelet results to a variety of frequently used Gaussian-type basis sets, we show that counterpoise corrections, which are widely employed to correct for basis set superposition errors, often lead to underbinding. Additionally, counterpoise corrections are difficult to employ when the association step also involves a chemical transformation. Multiwavelets, which can be conveniently applied to all types of reactions, provide a promising alternative for computing electronic interaction energies free from any basis set errors.

&lt;h3&gt;ChemRxiv record&lt;/h3&gt;
&lt;p&gt;&lt;a href="https://doi.org/10.26434/chemrxiv.13669951.v1"&gt;https://doi.org/10.26434/chemrxiv.13669951.v1&lt;/a&gt;&lt;/p&gt;
&lt;/br&gt;
description: MRChem, 1
description: ORCA, 4.2.1
description: ORCA, 4.1.2
DOI: 10.18710/xctgxt/5rvqnn
title: 01_ReadMeFile.txt
DOI: 10.18710/xldxq3/ce9eyd
title: 0_README.txt
DOI: 10.18710/xldxq3/orxyvr
title: 3_raw_activity.txt
DOI: 10.18710/z1apbo
title: TGO Ramfjordmoen Ionosonde Data November 2019

description: &lt;p&gt;About this dataset: This dataset contains ionosonde data in SAO format and ionograms in PNG format, and covers data from November 01-30, 2019.&lt;/p&gt;&lt;p&gt;About the Tromsø Ionosonde (1993-present): Since 1980, the ionosonde was situated at 69° 35' N, 19° 13' E at Ramfjordmoen near Tromsø, Norway and operated by the Auroral Observatory of the University of Tromsø and later on, in collaboration with QinetiQ. The Lowell ionosonde replaced the Auroral Observatory unit in 1992; see the related publication for details.&lt;/p&gt;
description: TGO Ionosonde Data
DOI: 10.18710/6ectte/1umhcb
title: Tilfeldighet.2008-2019.Xtab(7880).txt

description: THIS TXT FILE LISTS THE CROSSTABS THAT WERE THE BASES FOR FIGURES 1 THRU 3.
DOI: 10.18710/c8mq7v/8eco7g
title: 00_ReadMe.txt

description: Read Me file containing information about the other files in this dataset.
DOI: 10.18710/ed8hsd/0zeuyi
title: SIMULATED-5-1000-0.500.data.Rdata

description: An R data file which is similar to "data∕SIMULATED/SIMULATED-5-1000-0.025.data.Rdata", but was generated using "delta=0.500"
DOI: 10.18710/gxdora/2hxkvn
title: 20200516_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/4tdqhv
title: 20200419_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/9twite
title: 20200428_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/e2xx19
title: 20200529_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/e9ldpl
title: 20200817_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/fcmgpp
title: 20200505_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/hejbjd
title: 20200730_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/hfp05i
title: 20201116_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/l0waat
title: 20200629_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/naq6nz
title: 20201118_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/ou1ir6
title: 20200208_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/rk72ok
title: 20200301_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/rtzvez
title: 20200624_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/ujeows
title: 20200906_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/utoxbk
title: 20200413_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/vamih8
title: 20201127_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/vyqxih
title: 20201024_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/yk63rh
title: 20201030_BJN_REDOBS_gal.txt
DOI: 10.18710/mtpr9e/b9zaoa
title: 00_README_BF_v4.txt
DOI: 10.18710/mtpr9e/cqneh2
title: 18S_TOB5_R2_All.fastq.gz
DOI: 10.18710/nwqqbi/79zvfh
title: fields-05300.dat
DOI: 10.18710/nwqqbi/auixry
title: fields-01500.dat
DOI: 10.18710/nwqqbi/kzay8r
title: fields-09400.dat
DOI: 10.18710/nwqqbi/rkehd0
title: fields-05400.dat
DOI: 10.18710/nwqqbi/sntoa6
title: fields-04200.dat
DOI: 10.18710/nwqqbi/yry4t6
title: fields-00400.dat
DOI: 10.18710/nwqqbi/yvd24c
title: fields-03700.dat
DOI: 10.18710/nwqqbi/zbs2oo
title: fields-03300.dat
DOI: 10.18710/pigssk/9irp6e
title: 20200320_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/a4famf
title: 20200722_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/ec8fgh
title: 20200720_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/h6dwfj
title: 20201228_BJN_REDTEC_gal.txt
DOI: 10.18710/pigssk/karufx
title: 20200915_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/uhhdtj
title: 20200605_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/vv8khk
title: 20201114_BJN_REDTEC_glo.txt
DOI: 10.18710/pigssk/wmumth
title: 20200228_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/x4typw
title: 20200610_BJN_REDTEC_gps.txt
DOI: 10.18710/pigssk/zhuzki
title: 20200810_BJN_REDTEC_gal.txt
DOI: 10.18710/q0f8pa/6gfbkx
title: OPX-191.stl
DOI: 10.18710/q0f8pa/ba2ws4
title: OPX-107.stl
DOI: 10.18710/q0f8pa/fkkbwr
title: OPX-34.stl
DOI: 10.18710/q0f8pa/gwa37v
title: OPX-70.stl
DOI: 10.18710/q0f8pa/gwnxtj
title: OPX-56.stl
DOI: 10.18710/q0f8pa/i18od1
title: OPX-63.stl
DOI: 10.18710/q0f8pa/nujxrz
title: OPX-57.stl
DOI: 10.18710/q0f8pa/oqtyxb
title: OPX-92.stl
DOI: 10.18710/q0f8pa/otzfjp
title: OPX-35.stl
DOI: 10.18710/q0f8pa/pa4um8
title: OPX-25.stl
DOI: 10.18710/q4dirk/4ihwkw
title: a_ 51.20_ 0.34-Fxye_all.eps
DOI: 10.18710/q4dirk/j8pmux
title: a_ 51.40_ 0.11-Fyze_all.eps
DOI: 10.18710/qc6vnp/fukrkm
title: 01_ReadMeFile.txt
DOI: 10.18710/spauiy/jjsloi
title: 01_Bryo_abundance.txt
DOI: 10.18710/zlw59w/ax1y0t
title: case_study_data.RData

description: Data from the case study, which is data on small mammals from the long-term monitoring program COAT (www.coat.no) on the Arctic Tundra.
DOI: 10.18710/zlw59w/gwl4nm
title: 3_plotting_sim_all.R

description: Script to plot the results from the simulation study
DOI: 10.18710/ig0mmh
title: Replication Data for: "Contrasting responses of plant and lichen carbon based secondary compounds across an elevational gradient"

description: This dataset contains concentrations of phenolic compounds in individual species of lichens and vascular plants sampled across an elevational gradient from 1120 to 1600 m above sea level near Finse in southern Norway.
DOI: 10.18710/l60vdi/u6ruio
title: IL_knowledge_tests.txt

description: This file contains the final 21-item information literacy tests, including questions and answer alternatives, in both English and Norwegian. The 50-item pilot test, in Norwegian, is also included.
DOI: 10.18710/gxdora/zjizqk
title: 20200912_BJN_REDOBS_gps.txt
DOI: 10.18710/mtpr9e/oyr4nt
title: Metadata_Amplicon_Sequences_Dataverse.txt
DOI: 10.18710/nwqqbi/bhqtzr
title: fields-03800.dat
DOI: 10.18710/q0f8pa/3ygjbw
title: OPX-221.stl
DOI: 10.18710/q0f8pa/a9qdmv
title: OPX-135.stl
DOI: 10.18710/q0f8pa/cjvryc
title: OPX-281.stl
DOI: 10.18710/q0f8pa/eytag7
title: OPX-246.stl
DOI: 10.18710/q0f8pa/ljrba9
title: OPX-130.stl
DOI: 10.18710/q0f8pa/qhbtwt
title: OPX-136.stl
DOI: 10.18710/q0f8pa/sxckhd
title: OPX-93.stl
DOI: 10.18710/q0f8pa/u20mxr
title: OPX-172.stl
DOI: 10.18710/q0f8pa/wms8f4
title: OPX-84.stl
DOI: 10.18710/q0f8pa/zsqsgy
title: OPX-128.stl
DOI: 10.18710/q4dirk/5zpmmg
title: a_ 51.40_ 0.29-Fyze_all.eps
DOI: 10.18710/2yjitd/uynxcg
title: data_participants.tab

description: Participants data
DOI: 10.18710/chb8qs/taa5iu
title: :unav

description: :unav
DOI: 10.18710/ed8hsd/e6iywl
title: hg19ToMm9.over.chain

description: human hg19 to mouse mm9 liftover chain. Required for R/PoC.results.GSE105018.R.
DOI: 10.18710/gxdora/f0kjn8
title: 20201106_BJN_REDOBS_gps.txt
DOI: 10.18710/q0f8pa/6zh5xb
title: OPX-189.stl
DOI: 10.18710/q0f8pa/bqp0kb
title: OPX-139.stl
DOI: 10.18710/q0f8pa/gdnnel
title: OPX-204.stl
DOI: 10.18710/q0f8pa/iuqnkr
title: OPX-213.stl
DOI: 10.18710/q0f8pa/ksjov7
title: OPX-166.stl
DOI: 10.18710/q0f8pa/qezqh8
title: OPX-270.stl
DOI: 10.18710/q0f8pa/teknjw
title: OPX-210.stl
DOI: 10.18710/q0f8pa/v94sri
title: PLAG-87.stl
DOI: 10.18710/tu1gsy/wtevbm
title: 01_data_20200816.tab
DOI: 10.18710/zlw59w/9boqff
title: 1_Sim50data_low_det.R

description: Script simulating data from a low detection probability scenario under the spatially hierarchical occupancy model.
DOI: 10.18710/zlw59w/rbowaw
title: 2_sim_low_occ.R

description: Script analyzing data simulated from the low occupancy probability scenario with the spatially hierarchical occupancy model.
DOI: 10.18710/8o9zv0/uxzg2h
title: 12_Figure_10.PNG
DOI: 10.18710/ed8hsd/pn0jqi
title: files.2019-10-10.json

description: JSON file containing information on all Illumina Human Methylation 450 assay results in TCGA.
DOI: 10.18710/gxdora/5exj02
title: 20200717_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/dal4ao
title: 20201121_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/eojocg
title: 20200629_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/goklxx
title: 20201004_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/hbbq1e
title: 20200724_BJN_REDOBS_gal.txt
DOI: 10.18710/gxdora/ipwa7u
title: 20200523_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/jlddda
title: 20200225_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/meyrll
title: 20200819_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/myswih
title: 20201209_BJN_REDOBS_gps.txt
DOI: 10.18710/gxdora/nvzak0
title: 20201207_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/sfkuv9
title: 20201111_BJN_REDOBS_glo.txt
DOI: 10.18710/gxdora/yumvae
title: 20201219_BJN_REDOBS_gal.txt
DOI: 10.18710/h5g3k5/cpgkko
title: 00_ReadMe.txt
DOI: 10.18710/jdwlva
title: Replication Data for: Terrestrial Inputs Shape Coastal Microbial Communities in a High Arctic Fjord (Isfjorden, Svalbard)

description: The dataset was taken in June and August 2019 in Isfjorden, Svalbard, an Arctic fjord system with various rivers and glaciers supplying meltwater in summer. 
The aim of the study is to investigate the effect of river inputs during the spring freshet and later in summer on bacterial and archaeal communities and diversity. 
We hypothesized that changes in dissolved organic matter and nutrient inputs, and increasing stratification shapes the fjord microbial communities. 
We sequenced the 16S rRNA gene of filtere water or sediment samples using the Illumina MiSeq PE technology and investigated environmental controls using multivariate statistics.
description: cutadapt, 2.8
description: trimmomatic, 0.39
description: pear, 0.9.6
description: fastqc, 0.11.9
description: swarm, 3.0.0
description: sina, 1.6.0
description: R, 3.6.3
DOI: 10.18710/lijffp/gfshcg
title: TGO_Ramfjordmoen_ionosonde_data_2020-12.SAO.zip
DOI: 10.18710/nl8uqc
title: The superlative alternation in present-day English: Questionnaire data

description: This dataset contains elicitation data collected through a questionnaire on superlative strategy choice in English (X-est vs. most X). Native speakers (n = 675) were asked to indicate their preferred superlative variants of the adjectives used in n = 120 systematically manipulated prompt sentences. The data enable us to quantify the effects of seven contextual constraints and four speaker variables on the superlative alternation.
description: [publication abstract, “The superlative alternation in British and American English: Questionnaire-based insights."]

Drawing on data gleaned from a large-scale questionnaire study, this paper investigates the role of contextual constraints in the alternation of synthetic (-est) and analytic (most) superlative forms. The multifactorial analysis suggests that variables that have been interpreted along the lines of processing complexity in previous studies increase the odds of the analytic superlative, albeit to varying degrees. These findings substantiate the hypothesis that the cognitive support strategy of more-support, as identified by Mondorf (2009b) for the comparative, also extends to the superlative. Despite this empirical support, my analysis exposes the limitations of a complexity-based account of variation in gradation strategy choice. It is further observed that speakers of the two main reference varieties British English and American English do not systematically differ in their sensitivity to the cognitive mechanisms that underlie this alternation. This observation adds to the body of evidence that suggests a high degree of cross-varietal homogeneity in morphosyntactic alternations. From a methodological perspective, it is argued that elicitation studies of this kind constitute a valuable complement to the extant corpus studies conducted on this alternation in that they allow for the operationalisation of linguistic and extralinguistic variables in a controlled setting.
description: LimeSurvey, 3.5.1 (http://www.limesurvey.org)
description: R: A language and environment for statistical computing, 3.6.3 (https://www.r-project.org)
DOI: 10.18710/nwqqbi/09qkkh
title: fields-04800.dat
DOI: 10.18710/nwqqbi/3whdy3
title: fields-01000.dat
DOI: 10.18710/nwqqbi/lt5zwc
title: fields-04000.dat
DOI: 10.18710/nwqqbi/m05ykd
title: fields-00800.dat
DOI: 10.18710/nwqqbi/pegqku
title: fields-03500.dat
DOI: 10.18710/nwqqbi/pf9ugs
title: fields-04400.dat