cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18710/8gqv9v
title: Replication data for: Animacy and Differential Object Marking in Old Church Slavonic

description: This article explores the synchronic variation between the nominative-accusative (NA) and genitive-accusative (GA) in the oldest layer of canonical Old Church Slavonic (OCS), using parallel Greek and OCS data with principled information status annotation. Firstly, the data are used to clarify the claims made about the pragmatic properties of the alternation in the previous literature. There is a good case for claiming that OCS GA marking functions as a limited type of definiteness marking, i.e. that GA objects will nearly always be previously mentioned or contextually accessible. Secondly, the data are used to examine whether the GA-NA variation correlates with any other discourse properties known to be important in differential object marking systems. The NA is found to be a marker of referential persistence: a new referent will typically be NA-marked if it is an important participant in the further narrative. Third, the focus is shifted to the relationship between subject and object properties. There are indications that the GA is preferred even with new object referents if the subject has low prominence. Thus, the variation is best understood as a situation of differential object marking conditioned by several discourse properties: definiteness, referential persistence and perhaps subject-object asymmetry.
DOI: 10.18710/fzowbn
title: Replication data for: Multi-step parametric inversion of scanning magnetic microscopy data for modeling magnetization of multidomain magnetite

description: The dataset includes magnetic inversion results and high-resolution magnetic scan data of a 30µm thick section of a sample from Modum, Norway. The sample contains large magnetite grains (&gt;100 µm) surrounded by serpentine and carbonate and exhibits a clear multi-domain behavior. Inversion of the data provided an estimation of the fine-scale magnetization of the sample. The dataset contains also inversion results on synthetic magnetic data.
description: ModelVision, 16
DOI: 10.18710/iorwf6/gsrhek
title: Ch7_ex058_130921-1_00-43-00-50.wav
DOI: 10.18710/iorwf6/tqjewm
title: Ch4_Table4-14d_NB_nahooni.mp3
DOI: 10.18710/jfnesu
title: Replication data for: Verbal borrowability and turnover rates

description: This is the dataset used in the study of verbal and nominal borrowings in written literary Russian language, their diachronic developments and their connection to frequency. The files contain a list of Russian lexemes annotated for borrowing status and a number of files with calculated probabilities of disappearance for lexemes of different frequency ranks over different periods of time.
description: Abstract: Conventional wisdom holds that verbs are more difficult to borrow than nouns. Recent studies have supported this claim, inferring it from the fact that synchronically almost every language studied contained a larger proportion of identifiable borrowings among nouns than among verbs. In this paper, I demonstrate that there is a logical fallacy in this inference. Using a large diachronic corpus of Russian texts, I show that verbs have lower turnover rates and, consequently, longer life expectancies than nouns, i.e. they are generally more difficult to replace. I argue that this fact alone could theoretically result in the synchronically observed disparities. The hypothesis of cross-linguistically lower verbal turnover rates, which I propose based on these findings, is difficult to verify directly on a large sample of languages. However, it makes a non-trivial prediciton, which can be tested more easily. It predicts that if a contact situation lasted for a while, but ceased to exist several centuries ago, the proportion of verbs borrowed during that period and survivng to the present day may equal or exceed the proportion of such borrowings among nouns. The data found in the World Loanword Database (Haspelmath & Tadmor 2009) are consistent with this prediction, thus providing evidence in favor of the hypothesis.
DOI: 10.18710/klqvok/socphz
title: JupyterNoteBook_figure4_correlationplot.ipynb

description: Jupyter Notebook with Python3 code for generating Figure 4 in the journal article.
DOI: 10.18710/4yhu4r/5ohirh
title: fields-03400.dat
DOI: 10.18710/4yhu4r/cg5cde
title: fields-08600.dat
DOI: 10.18710/4yhu4r/h9y5rk
title: :unav

description: :unav
DOI: 10.18710/4yhu4r/omwzrc
title: fields-06000.dat
DOI: 10.18710/5ms1jk/kljvsf
title: 05_Data_manufacturing_firm.tab

description: Data
DOI: 10.18710/adpc3a
title: Replication data for: Enchaînement, liaison, accentuation chez les apprenants norvégiens
title: Norwegian L1 and French L3: Learners' production of liaison and enchainement

description: [abstract] Given that their L1, Norwegian, and their L2, English, are lexical stress languages, Norwegian speakers will equally tend to stress lexical words during the course of acquisition of L3 French, insuring the prosodic autonomy of each word. In the present paper, we show that this strategy slows down the acquisition of two external sandhi phenomena in French, i.e. liaison and final consonant linking. The data, taken from two corpora recently collected in Tromsø and Oslo, indicate that the learners' acquisition path is conditioned by internal factors like prosodic weight, perceptual salience and frequency, and by external factors like the different tasks to be completed, i.e. reading vs conversation. The data further indicate that liaisons following determiners and clitics are the first categories to be acquired and that spontaneous speech, where the learner does not have direct access to the graphic word, seems to favor erasing the prosodic boundaries required by the prosodic system of her L1.
description: The dataset is based on data from 12 Norwegian learners, 6 from Tromsø (level A2) and 6 from Oslo (level B1/B2), participants in the ongoing project IPFC: Interphonologie du français contemporain (http://cblle.tufs.ac.jp/
ipfc/index.php?id=2). Data are extracted from three registers: text reading, semi-formal conversation and free conversation. The dataset consists of four Excel-files, plus a read-me file. The files "Enchainement_text" and "Enchainement_conversation" contain all potential occurrences of enchainement (rightward consonant linking across a word boundary) observed in the three registers, as well as (non) enchainement of the consonant in the various cases. The files "Liaison_text" and "Liaison_conversation" contain all potential occurrences of liaison observed in the three registers. It further contains information on (non) liaison and (non) enchainement in the various cases, as well as, when a liaison consonant is realised, the type and quality of the consonant produced.
DOI: 10.18710/hlyzib/4vnh2m
title: Survey_distributed_in_the_company.pdf

description: File 1
DOI: 10.18710/iorwf6/3pvjaw
title: Ch4_Table4-14t_NB_nmatleequ.mp3
DOI: 10.18710/iorwf6/41imt7
title: Ch7_ex022_160326_10-22.wav
DOI: 10.18710/iorwf6/4ynzs4
title: Ch3_page116b_an-anaq.mp3
DOI: 10.18710/iorwf6/66h4tr
title: Ch6_ex074_130907-3_12-15.mp3
DOI: 10.18710/iorwf6/7bjdmf
title: Ch3_ex004c_neefgw-ee.wav
DOI: 10.18710/iorwf6/8mta1p
title: Ch8_ex015_120715-0-00-30-00-35.mp3
DOI: 10.18710/iorwf6/8nvyto
title: Ch6_ex076_120923-1_00-47.wav
DOI: 10.18710/iorwf6/hadnrj
title: Ch1_ex008_120715-4_00-05.wav
DOI: 10.18710/iorwf6/hjrczx
title: Ch4_Table4-14g_NB_nmaate.wav
DOI: 10.18710/iorwf6/jpf3jn
title: Ch6_ex051_130907-3_08-40.mp3
DOI: 10.18710/iorwf6/kchheu
title: Ch3_ex015a_riuksaen.wav
DOI: 10.18710/iorwf6/kzrpju
title: Ch3_ex118_130825-6_17-02.mp3
DOI: 10.18710/iorwf6/l7ey3a
title: Ch7_ex175_RO-170917-1_03-22.wav
DOI: 10.18710/iorwf6/lxbwin
title: Ch7_ex159_RO-170829-1_12-18.mp3
DOI: 10.18710/iorwf6/oeun0m
title: Ch6_ex164_130911-2_00-59.wav
DOI: 10.18710/iorwf6/ohbcky
title: Ch4_ex057_FGT-171013-1_00-31.mp3
DOI: 10.18710/iorwf6/pl5sho
title: Ch4_Table4-14n_NB_nmeeno.mp3
DOI: 10.18710/iorwf6/qpei3d
title: Ch6_ex185_130907-3_04-52.wav
DOI: 10.18710/iorwf6/qsb8yw
title: Ch6_Table6-10k_neno_nee.mp3
DOI: 10.18710/iorwf6/qtlb4k
title: Ch7_ex050_130913-1_02-43.mp3
DOI: 10.18710/iorwf6/rqoyab
title: Ch7_ex129_130825-7_01-28-01-31.mp3
DOI: 10.18710/iorwf6/sn6cnq
title: Ch6_ex061_130825-6_17-02.mp3
DOI: 10.18710/iorwf6/tcf3vp
title: Ch7_ex105_130825-3_00-51.wav
DOI: 10.18710/iorwf6/tg85uo
title: Ch4_Table4-11b_neon_meseq.wav
DOI: 10.18710/iorwf6/trq5hh
title: Ch3_ex111_130825-6_07-34.wav
DOI: 10.18710/iorwf6/u49i20
title: Ch3_Table3-19b_bankofaq.mp3
DOI: 10.18710/iorwf6/w7os0t
title: Ch7_ex026_120715-4_07-56.mp3
DOI: 10.18710/iorwf6/wize92
title: Ch7_ex127_130914-1_00-20-00-21.wav
DOI: 10.18710/iorwf6/xcq2tq
title: Ch4_Table4-14j_NB_paumaaka.mp3
DOI: 10.18710/iorwf6/xmbwv5
title: Ch6_ex099_130825-6_19-04.mp3
DOI: 10.18710/iorwf6/yj5pcv
title: Ch3_Table3-12a_anjair.mp3
DOI: 10.18710/iorwf6/yo71pm
title: Ch7_ex069_130907-3_04-41.wav
DOI: 10.18710/iorwf6/ypy4rg
title: Ch3_ex129_130914-1_00-47.mp3
DOI: 10.18710/iorwf6/zj1ihq
title: Ch6_ex212_130913-1_02-30.mp3
DOI: 10.18710/iorwf6/zobuh5
title: Ch6_ex205_130907-3_07-36.mp3
DOI: 10.18710/iorwf6/zpz84w
title: Ch3_page093_euns-ee.wav
DOI: 10.18710/jfnesu/bxygpv
title: disapp_prob_V_30.txt
DOI: 10.18710/klqvok/a9azpt
title: DataSet_dipole_mapping.yaml

description: Python-readable file containing the data used for making the dipole mapping plot (figure 3 in Supporting Information PDF).
DOI: 10.18710/klqvok/bjwozo
title: DataSet_mw_lr_lda.yaml

description: Python-readable file containing multiwavelet polarizabilities computed with LDA and linear response.
DOI: 10.18710/klqvok/ofs8lo
title: 000_README.txt
DOI: 10.18710/loiwiu/5vz8h9
title: 20141130_HOP_REDOBS_gps.txt
DOI: 10.18710/loiwiu/eandtr
title: 20141130_NYA_REDTEC_gal.txt
DOI: 10.18710/loiwiu/eozemm
title: 20141130_NYA_REDTEC_glo.txt
DOI: 10.18710/loiwiu/lmqxin
title: 20141130_NYA_REDOBS_gal.txt
DOI: 10.18710/loiwiu/rr3omz
title: 20141130_BJN_REDTEC_gal.txt
DOI: 10.18710/tdjv8x/adwubv
title: 00_ReadMe.pdf
DOI: 10.18710/zam96s
title: Replication Data for: Constructions, their families and their neighborhoods: the case of durak durakom ‘a fool times two’

description: This dataset concerns the Russian construction where the same noun occurs in the nominative and instrumental cases, such as durak durakom ‘ultimate fool’. The dataset contains examples excerpted from the Russian National Corpus (ruscorpora.ru).
description: Article abstract: On the basis of data from the Russian National Corpus, we analyze the meanings and structure of the syntactic construction observed in phrases like дурак дураком, which we term the “NOM-INS construction”. We argue that the construction constitutes a network of three closely related subcategories, which we refer to as “Extreme”, “Paragon” and “Topic change”. It is furthermore shown that a diachronic change has taken place, whereby Topic Change has taken over for Extreme as the dominant subcategory. Our analysis provides ample evidence for the main tenets of Construction Grammar, namely that a language is a network of related constructions, that meaning is often not compositional, that there is a continuum from idiomatic to schematic uses of constructions, and that constructions evolve over time.
DOI: 10.18710/4yhu4r/jkwicn
title: fields-01600.dat
DOI: 10.18710/54zjgq/bcqygt
title: DatabaseNumeralsRNC2018forCARTsmallWithExamples.tab

description: This is the database with examples in Cyrillic.
DOI: 10.18710/5ms1jk/xafvl8
title: 01_Data_university_example.tab

description: Data
DOI: 10.18710/brozeg/mknkzi
title: 00_README.txt
DOI: 10.18710/cvw8yu/r60gua
title: 00_ReadMe.pdf
DOI: 10.18710/dqjs4a/3lquih
title: Study_One_Do_mothers_also_manipulate_grandparental_care.tab
DOI: 10.18710/dqjs4a/frjmef
title: Study_Two_Do_mothers_also_manipulate_grandparental_care.txt
DOI: 10.18710/eifz2j/8i0snf
title: Free_gas_flow_rates.txt
DOI: 10.18710/fzowbn/xct5ev
title: Table_1.txt

description: Table_1_samples properties
DOI: 10.18710/ghhunu/spqiis
title: st63preisach.067
DOI: 10.18710/hlyzib/joq1a0
title: Data_from_survey.tab

description: File 3
DOI: 10.18710/iorwf6/i8ed7q
title: Ch3_page122g_isat.mp3
DOI: 10.18710/iorwf6/kvnstu
title: Ch3_ex119_160326_10-36.wav
DOI: 10.18710/jfnesu/poumw1
title: disapp_prob_N_20.txt
DOI: 10.18710/loiwiu/bzasun
title: 20141130_KHO_REDTEC_gps.txt
DOI: 10.18710/qeofpy/0s631i
title: GAM_model_input.R

description: R script to calculate PUD (photo-unit-days) in each grid cell.
DOI: 10.18710/ssa38j/wgwrre
title: data.npz

description: File containing images and labels stored as numpy arrays.
DOI: 10.18710/t9nq9l
title: Replication data for: The beginning of a beautiful friendship: rule-based and statistical analysis of Middle Russian

description: We describe and compare two tools for processing Middle Russian texts.
Both tools provide lemmatization, part-of-speech and morphological annotation.
One (“RNC”) was developed for annotating texts in the Russian
National Corpus and is rule-based. The other one (“TOROT”) is being used
for annotating the eponymous corpus and is statistical. We apply the two
analyzers to the same Middle Russian text and then compare their outputs
with high-quality manual annotation. Since the analyzers use different annotation
schemes and spelling principles, we have to harmonize their outputs
before we can compare them. The comparison shows that TOROT
performs considerably better than RNC (lemmatization 69.8% vs. 47.3%,
part of speech 89.5% vs. 54.2%, morphology 81.5% vs. 16.7%). If, however,
we limit the evaluation set only to those tokens for which the analyzers provide
a guess and in addition consider the RNC response correct if one of the
multiple guesses it provides is correct, the numbers become comparable
(88.5% vs. 91.9%, 93.9% vs. 95.2%, 81.5% vs. 86.8%). We develop a simple
procedure which boosts TOROT lemmatization accuracy by 8.7% by using
RNC lemma guesses when TOROT fails to provide one and matching them
against the existing TOROT lemma database. We conclude that a statistical
analyzer (trained on a large material) can deal with non-standardised
historical texts better than a rule-based one. Still, it is possible to make the
analyzers collaborate, boosting the performance of the superior one.
DOI: 10.18710/vgqylq/3yj81e
title: atm_T.txt
DOI: 10.18710/vgqylq/cg1jli
title: wind_speed.txt
DOI: 10.18710/vgqylq/k60ohd
title: README_flares.txt
DOI: 10.18710/vgqylq/ob4hw6
title: Lon.txt
DOI: 10.18710/vgqylq/wsnt2g
title: dens_bottle.txt
DOI: 10.18710/vgqylq/zgxnwg
title: dens_bottle.txt
DOI: 10.18710/vh95qc/panrbo
title: Immigr_rates_regr_analysis1.pdf
DOI: 10.18710/zmjlzt/cdwi3i
title: Dataset_Immigrant_Integration_ GovernanceDIIG.csv
DOI: 10.18710/iorwf6/fbf6jh
title: Ch7_ex041_120715-3_00-14.mp3
DOI: 10.18710/iorwf6/oqudrw
title: Ch6_ex062_130825-6_13-28.mp3
DOI: 10.18710/iorwf6/phbrl0
title: Ch6_ex142_120923-2_05-03.wav
DOI: 10.18710/iorwf6/xa3ljd
title: Ch3_ex056i_mais_oni.mp3
DOI: 10.18710/ssa38j/lifqos
title: Auroral Image Classification with Deep Neural Networks.ipynb

description: Google colab notebook to training ResNet 18 classifier on aurora dataset.
DOI: 10.18710/54zjgq/nln3ia
title: DatabaseNumeralsRNC2018forCARTsmall.txt

description: This is the database without examples in Cyrillic.
DOI: 10.18710/5ms1jk/fv01zi
title: 03_Data_university_implemented.txt

description: Data
DOI: 10.18710/brozeg/wwoct9
title: 01_Collembola
DOI: 10.18710/ghhunu/n3tedd
title: st63.hys
DOI: 10.18710/ghhunu/qxmlsd
title: st63preisach.007
DOI: 10.18710/iorwf6/0wsx6f
title: Ch3_ex056d_qnaak_funuf.mp3
DOI: 10.18710/iorwf6/1aurkw
title: Ch3_ex005a_kreej-ee.wav
DOI: 10.18710/iorwf6/2rops8
title: Ch7_ex176_RO-170917-1_08-16.mp3
DOI: 10.18710/iorwf6/4ldzhz
title: Ch7_ex007_130920-1_00-22.mp3
DOI: 10.18710/iorwf6/chbcij
title: Ch6_ex175_120715-4_02-59.mp3
DOI: 10.18710/iorwf6/ctjekx
title: Ch4_Table4-11h_neon_haaq.wav
DOI: 10.18710/iorwf6/dv2z8k
title: Ch3_Table3-12f_anrooq.mp3
DOI: 10.18710/iorwf6/efvkqb
title: Ch3_ex006b_nuif.mp3
DOI: 10.18710/iorwf6/fctghe
title: Ch6_ex075_120715-2_00-25.wav
DOI: 10.18710/iorwf6/fn4gv5
title: Ch6_ex038_130825-7_00-38.mp3
DOI: 10.18710/iorwf6/fnxajh
title: Ch7_ex104_140726_00-00-00-14.mp3
DOI: 10.18710/iorwf6/g2ktz7
title: Ch7_ex019_130902-1_03-41.wav
DOI: 10.18710/iorwf6/hkzyer
title: Ch7_ex169_RO-170830-4_07-15.mp3
DOI: 10.18710/iorwf6/j8sqpy
title: Ch6_ex093_130914-3_01-51.wav
DOI: 10.18710/iorwf6/khy6jc
title: Ch6_ex173_130821-1_09-56.wav
DOI: 10.18710/iorwf6/l0brc1
title: Ch4_Table4-14h_NB_naloole.wav
DOI: 10.18710/iorwf6/lfsqpt
title: Ch6_ex190_130821-1_05-00.wav
DOI: 10.18710/iorwf6/mekfua
title: Ch3_ex008b_mainuan.wav
DOI: 10.18710/iorwf6/mlxf8t
title: Ch6_ex188_130902-1_02-09.wav
DOI: 10.18710/iorwf6/okkdkz
title: Ch3_ex026_130902-1_02-59.wav
DOI: 10.18710/iorwf6/ptlval
title: Ch3_Table3-20e_paratrao.mp3
DOI: 10.18710/iorwf6/ue5ymm
title: Ch3_ex038c_anhaek.wav
DOI: 10.18710/iorwf6/vecqxb
title: Ch6_ex076_120923-1_00-47.mp3
DOI: 10.18710/iorwf6/vq9mew
title: Ch3_ex111_130825-6_07-34.mp3
DOI: 10.18710/iorwf6/waf7fg
title: Ch7_ex171_RO-170829-1_13-10.mp3
DOI: 10.18710/iorwf6/weyrrt
title: Ch3_ex001c_amaf.mp3
DOI: 10.18710/iorwf6/wtawcm
title: Ch3_ex084d_naijeer.wav
DOI: 10.18710/iorwf6/xeuh3z
title: Ch7_ex092_130913-1_00-16.mp3
DOI: 10.18710/iorwf6/ydxvgz
title: Ch6_ex026_130825-6_03-33.mp3
DOI: 10.18710/iorwf6/zlthir
title: Ch7_ex017_130825-6_10-05.mp3
DOI: 10.18710/owkabr/8qic9u
title: mc_functions.py
DOI: 10.18710/qeofpy/qheg3k
title: GAM_models.r

description: R script to make GAM models of accessibility vs presence/absence of tourists and make tables & figures for manuscript.
DOI: 10.18710/vgqylq/1iedp9
title: temp_cont.txt
DOI: 10.18710/vgqylq/30s1ba
title: depth.txt
DOI: 10.18710/vgqylq/dkuhzx
title: methane.txt
DOI: 10.18710/vgqylq/ippk1w
title: sal_cont.txt
DOI: 10.18710/vgqylq/oyfv0l
title: Lat.txt
DOI: 10.18710/ghhunu/jdmvdl
title: st63preisach.063
DOI: 10.18710/ghhunu/qjm84l
title: st63preisach.024
DOI: 10.18710/ghhunu/ug3con
title: st63preisach.098
DOI: 10.18710/iorwf6/76coo1
title: Ch6_ex166_130902-1_00-51.mp3
DOI: 10.18710/iorwf6/kqsubl
title: Ch7_ex020_120715-4_02-26.wav
DOI: 10.18710/iorwf6/qaqamg
title: Ch4_Table4-11a_neno_meseq.wav
DOI: 10.18710/iorwf6/ytozze
title: Ch3_page121a_niis.wav
DOI: 10.18710/ssa38j
title: Replication Data for: Auroral Image Classification with Deep Neural Networks

description: Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statistics and for supporting scientists to study auroral images in an objective, organized and repeatable manner. Although previous studies have presented tools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with a high precision (&gt;90%). This work considers seven auroral subclasses; breakup, colored, arcs-bands, discrete, patchy, edge and clear-faint. Five different deep neural network architectures have been tested along with the well known classification methods; k nearest neighbor (KNN) and support vector machine (SVM). A set of clean nighttime color auroral images, without ambiguous auroral forms, moonlight, twilight, clouds etc., were used for training and testing. The deep neural networks generally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highest performance with an average classification precision of 92%. Although the results indicate that high precision aurora classification is an attainable objective using deep neural networks, it is stressed that a common consensus of the auroral morphology and the criteria for each class needs.&lt;br&gt;&lt;br&gt;The authors would like to thank Urban Brändström and the Swedish Institute of Space Physics for providing the original auroral image data. The image data archive is freely accessible at http://www2.irf.se/allsky/data.html, however, the users are obliged to contact the Kiruna Atmospheric and Geophysical Observatory before usage
DOI: 10.18710/wakckt
title: Replication data for: Constructions and language change: From genitive to accusative objects in Russian

description: This article reports on a corpus study of ongoing language change in Russian, whereby genitive-governing verbs like bojat’sja “fear” combine with objects in the accusative in addition to the traditionally normative genitive. While the use of the accusative is still not very frequent in Contemporary Standard Russian, we demonstrate that it is increasing and that a number of factors such as individuation (animacy), grammatical voice, frequency and verb semantics (intensionality and directionality) promote the use of the accusative. Our analysis is couched in Construction Grammar, and we show that the shift from genitive to accusative objects in Russian provides empirical support for Construction Grammar as a theory applicable to language change.
DOI: 10.18710/bugmhy
title: Replication data for: "Cyclic" Time in the History of Russian: Culture and Language Internal Factors

description: This study contains Old Russian data concerning an article about cyclic time in the history of Russian. The data are from four Old Russian Chronicles: Povest’ vremennyx let (available at TOROT: http://nestor.uit.no/), Suzdal’skaja letopis’ (available at http://www.lrc-lib.ru/rus_letopisi/Suzdal/search.htm), Galicko-Volynskaja letopis’ (available at http://www.lrc-lib.ru/rus_letopisi/gvl/index.php), and Kievskaja letopis’ (available at http://www.lrc-lib.ru/rus_letopisi/Kiev/search.htm). Searches were performed in 2014.
DOI: 10.18710/ghhunu
title: Replication Data for: Separating geometry- from stress-induced remanent magnetization in magnetite with ilmenite lamellae from the Stardalur basalts, Iceland

description: Bulk rock magnetic measurements (hysteresis loops, backfield curves and FORC diagrams) were done on basalts from the Stardalur volcano. Realistic geometries of magnetite grains from those basalts were obtained by FIB-SEM nanotomography. Meshes of these geometries were used to create micromagnetic models. The magnetic properties of these grains were modeled using the three dimensional micromagnetic code MERRILL and the influence of grain shape and size were isolated by modeling hysteresis loops. Comparing the modeled hysteresis loops with measured bulk hysteresis loops allows for quantification of internal stress in these grains. 
This dataset includes the meshes used to do the micromagnetic modeling, the resulting hysteresis loop data, and the raw data files for the rock magnetic measurements.
DOI: 10.18710/ghhunu/7kxsgn
title: st63preisach.079
DOI: 10.18710/ghhunu/qnfhhp
title: st63preisach.081
DOI: 10.18710/i6bd3r
title: Replication Data for: Effect of legume intercropping on N2O emissions and CH4 uptake during maize production in the Great Rift Valley, Ethiopia

description: The data report weekly measurements of soil nitrous oxide (N2O) emission and methane (CH4) uptake in the growing seasons 2015 and 2016 in a maize field experiment performed on the Hawassa University campus, Central Rift Valley, Ethiopia. The experiment was designed to test the effect of intercropping the forage legumes lablab (L. purpureus) and Crotalaria (C. juncea) into nitrogen (N) and phosphorous (P) fertilized maize 3 and 6 weeks after sowing of maize. The forage legumes were harvested at flowering and half of the biomass was returned to the same plots as mulch in the first season. In the second season, N fertilization was halved in mulched plots. Intercropping effects are evaluated against fully and non-fertilized maize treatments. The results are published in Raji SG and Dörsch P, Biogesciences, https://doi.org/10.5194/bg-2019-303
DOI: 10.18710/iorwf6/38vql1
title: Ch6_ex138_160315_41-50_elicit_noWAV.mp3
DOI: 10.18710/iorwf6/4unigc
title: Ch3_page094d_uran.mp3
DOI: 10.18710/iorwf6/5pq4f4
title: Ch3_ex018c_rekaq.wav
DOI: 10.18710/iorwf6/95waki
title: Ch6_ex170_130821-1_01-40.mp3
DOI: 10.18710/iorwf6/9ho0ad
title: Ch6_ex092_180923_01-16_elicit.wav
DOI: 10.18710/iorwf6/arrnuf
title: Ch6_ex068_130907-5_00-21-00-29.mp3