cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17309891
title: isme_bins_final.zip

description: Metagenome-assembled genomes (MAGs) reconstructed from the study published by Busi, S.B., de Nies, L., Habier, J. et al. Persistence of birth mode-dependent effects on gut microbiome composition, immune system stimulation and antimicrobial resistance during the first year of life. ISME COMMUN. 1, 8 (2021). https://doi.org/10.1038/s43705-021-00003-5
DOI: 10.6084/m9.figshare.17309891.v1
title: isme_bins_final.zip

description: Metagenome-assembled genomes (MAGs) reconstructed from the study published by Busi, S.B., de Nies, L., Habier, J. et al. Persistence of birth mode-dependent effects on gut microbiome composition, immune system stimulation and antimicrobial resistance during the first year of life. ISME COMMUN. 1, 8 (2021). https://doi.org/10.1038/s43705-021-00003-5
DOI: 10.25721/re8r-wq82
title: Information sur la "quête au plat", sur l'usage du grelot
DOI: 10.6084/m9.figshare.15098094
title: Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

description: This dataset contains 1821 compositions ranging from binary to multi-component alloys along with phase information by referring to several previous reports that compiled experimental data from the published literature.A total of 125 variables for each observation were generated by the Magpie program and down-selected to 12 variables based on linear Pearson correlation coefficient and non-linear normalized mutual information analyses. The down-selected variables can be subdivided into three categories: (1) those that are chemistry-agnostic (e.g., MixingEntropy),(2) those that depend on element pairs (e.g., DeltaHf), (3) those that are depend on chemistry (e.g., maxdiff_Electronegativity).
DOI: 10.6084/m9.figshare.15098094.v1
title: Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

description: This dataset contains 1821 compositions ranging from binary to multi-component alloys along with phase information by referring to several previous reports that compiled experimental data from the published literature.A total of 125 variables for each observation were generated by the Magpie program and down-selected to 12 variables based on linear Pearson correlation coefficient and non-linear normalized mutual information analyses. The down-selected variables can be subdivided into three categories: (1) those that are chemistry-agnostic (e.g., MixingEntropy),(2) those that depend on element pairs (e.g., DeltaHf), (3) those that are depend on chemistry (e.g., maxdiff_Electronegativity).
DOI: 10.25721/jvxz-bz62
title: La quadrette
DOI: 10.25721/b406-am40
title: Formule d'introduction
DOI: 10.5281/zenodo.5791921
title: EU Taxonomy on Sustainable Activities (Tidy)

description: In order to meet the EU’s climate and energy targets for 2030 and reach the objectives of the European green deal, it is vital that we direct investments towards sustainable projects and activities. To achieve this, a common language and a clear definition of what is ‘sustainable’ is needed. This is why the action plan on financing sustainable growth called for the creation of a common classification system for sustainable economic activities, or an EU taxonomy. The EU taxonomy is a classification system, establishing a list of environmentally sustainable economic activities in the areas of Climate mitigation, Climate adaptation, Biodiversity, Circular economy, Water, Pollution prevention. It could play an important role helping the EU scale up sustainable investment and implement the European green deal. The EU taxonomy would provide companies, investors and policymakers with appropriate definitions for which economic activities can be considered environmentally sustainable. It was defined by the Regulation (EU) 2020/852 The European Commmission created an EU Taxonomy Compass provides a visual representation of the contents of the EU Taxonomy, starting with the Delegated Act on the climate objectives, as adopted on 4 June 2021. Whilst you can download the EU Taxonomy in xlsx or json format, they are not tidy datasets, and they are not particularly well-suited for calculations or filtering. Reprex created a tidy version of the EU Taxonomy for developing better sustainability indicators into the Green Deal Data Observatory. This tidy version has subjective weights given to broader NACE sections and divisions. We plan to add better, more objective weights for NACE sections or divisions which are only partially matching the EU Taxonomy. For example, H49.32 is part of the sustainable taxonomy, but the entire H49 division is not. Therefore, we use weight=0.5 for this division. The A2 division is fully part of the taxonomy, and we use a weight=1 to refer to this fact. A better weighting would consider the weight of the H492.32 activity within the H49 division in the European economy. What would make such a weighting tricy is that this weight is different for each European country and the EU as a whole.
DOI: 10.5281/zenodo.5791920
title: EU Taxonomy on Sustainable Activities (Tidy)

description: In order to meet the EU’s climate and energy targets for 2030 and reach the objectives of the European green deal, it is vital that we direct investments towards sustainable projects and activities. To achieve this, a common language and a clear definition of what is ‘sustainable’ is needed. This is why the action plan on financing sustainable growth called for the creation of a common classification system for sustainable economic activities, or an EU taxonomy. The EU taxonomy is a classification system, establishing a list of environmentally sustainable economic activities in the areas of Climate mitigation, Climate adaptation, Biodiversity, Circular economy, Water, Pollution prevention. It could play an important role helping the EU scale up sustainable investment and implement the European green deal. The EU taxonomy would provide companies, investors and policymakers with appropriate definitions for which economic activities can be considered environmentally sustainable. It was defined by the Regulation (EU) 2020/852 The European Commmission created an EU Taxonomy Compass provides a visual representation of the contents of the EU Taxonomy, starting with the Delegated Act on the climate objectives, as adopted on 4 June 2021. Whilst you can download the EU Taxonomy in xlsx or json format, they are not tidy datasets, and they are not particularly well-suited for calculations or filtering. Reprex created a tidy version of the EU Taxonomy for developing better sustainability indicators into the Green Deal Data Observatory. This tidy version has subjective weights given to broader NACE sections and divisions. We plan to add better, more objective weights for NACE sections or divisions which are only partially matching the EU Taxonomy. For example, H49.32 is part of the sustainable taxonomy, but the entire H49 division is not. Therefore, we use weight=0.5 for this division. The A2 division is fully part of the taxonomy, and we use a weight=1 to refer to this fact. A better weighting would consider the weight of the H492.32 activity within the H49 division in the European economy. What would make such a weighting tricy is that this weight is different for each European country and the EU as a whole.
DOI: 10.25721/9d3h-6t67
title: Bourrée
DOI: 10.6084/m9.figshare.17310626
title: Late Neogene anisotropy of magnetic susceptibility records in the western Qaidam Basin and their tectonic implications

description: AMS results from the Qigequan section
DOI: 10.6084/m9.figshare.17310626.v1
title: Late Neogene anisotropy of magnetic susceptibility records in the western Qaidam Basin and their tectonic implications

description: AMS results from the Qigequan section
DOI: 10.5258/soton/d1887
title: Dataset for Thermal fluctuations of the optical properties of nanomechanical photonic metamaterials

description: This research dataset should be interpreted and understood in the context of the corresponding manuscript, which has been published in Advanced Optical Materials. All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. The data correspond to the data shown in the figures of the manuscript.
DOI: 10.48370/ofd/b4ol3k/bggrs7
title: HMW_Succisa_pratensis_Moench_H_5723.jpg

description: :unav
DOI: 10.48370/ofd/b4ol3k
title: Czarcikęs łąkowy (Succisa pratensis Moench), 5723

description: Herbarium
DOI: 10.48370/ofd/lxvhol/ydvd9p
title: HMW_Succisa_pratensis_Moench_H_7153.jpg

description: :unav
DOI: 10.48370/ofd/lxvhol
title: Czarcikęs łąkowy (Succisa pratensis Moench), 7153

description: Herbarium
DOI: 10.48370/ofd/hbd4rh/u9vbv3
title: HMW_Succisa_pratensis_Moench_H_6503.jpg

description: :unav
DOI: 10.48370/ofd/hbd4rh
title: Czarcikęs łąkowy (Succisa pratensis Moench), 6503

description: Herbarium
DOI: 10.48370/ofd/unhzsi/347pm0
title: HMW_Succisa_pratensis_Moench_H_6502.jpg

description: :unav
DOI: 10.48370/ofd/unhzsi
title: Czarcikęs łąkowy (Succisa pratensis Moench), 6502

description: Herbarium
DOI: 10.48370/ofd/nyi6jm/qqyz4t
title: HMW_Succisa_pratensis_Moench_H_0691.jpg

description: :unav
DOI: 10.48370/ofd/nyi6jm
title: Czarcikęs łąkowy (Succisa pratensis Moench), 691

description: Herbarium
DOI: 10.48370/ofd/deme3z/in1kgx
title: HMW_Succisa_pratensis_Moench_H_7890.jpg

description: :unav
DOI: 10.48370/ofd/deme3z
title: Czarcikęs łąkowy (Succisa pratensis Moench), 7890

description: Herbarium
DOI: 10.48370/ofd/lcoocr/fzhfjz
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_3983.jpg

description: :unav
DOI: 10.48370/ofd/lcoocr
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 3983

description: Herbarium
DOI: 10.48370/ofd/o0jdsn/hlfuyb
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_4901.jpg

description: :unav
DOI: 10.48370/ofd/o0jdsn
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 4901

description: Herbarium
DOI: 10.48370/ofd/qyen4m/mjidel
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_3316.jpg

description: :unav
DOI: 10.48370/ofd/qyen4m
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 3316

description: Herbarium
DOI: 10.48370/ofd/ka4kkg/suwwen
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_2390.jpg

description: :unav
DOI: 10.48370/ofd/ka4kkg
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 2390

description: Herbarium
DOI: 10.48370/ofd/qquw6r/nynpcw
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_9178.jpg

description: :unav
DOI: 10.48370/ofd/qquw6r
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 9178

description: Herbarium
DOI: 10.48370/ofd/3dv8uy/zvuzxh
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_7585.jpg

description: :unav
DOI: 10.48370/ofd/3dv8uy
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 7585

description: Herbarium
DOI: 10.48370/ofd/1csjby/ibddqt
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_7160.jpg

description: :unav
DOI: 10.48370/ofd/1csjby
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 7160

description: Herbarium
DOI: 10.48370/ofd/2lg1qf/og1rcf
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_7148.jpg

description: :unav
DOI: 10.48370/ofd/2lg1qf
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 7148

description: Herbarium
DOI: 10.48370/ofd/je8vo8/n3pnhy
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_7110.jpg

description: :unav
DOI: 10.48370/ofd/je8vo8
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 7110

description: Herbarium
DOI: 10.48370/ofd/empvfq/h7grmp
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_5239.jpg

description: :unav
DOI: 10.48370/ofd/empvfq
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 5239

description: Herbarium
DOI: 10.48370/ofd/1023kr/rdul9k
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_4542.jpg

description: :unav
DOI: 10.6084/m9.figshare.15170058
title: An improved integrated generalization method of contours and rivers using importance sequence of all points from these two elements

description: experimental data and code<br>
DOI: 10.48370/ofd/1023kr
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 4542

description: Herbarium
DOI: 10.48370/ofd/olyz8q/znpzo7
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_4461.jpg

description: :unav
DOI: 10.48370/ofd/olyz8q
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 4461

description: Herbarium
DOI: 10.48370/ofd/1ntzby/qqjgo4
title: HMW_Stuckenia_pectinata_(L.)_Börner_H_3503.jpg

description: :unav
DOI: 10.48370/ofd/1ntzby
title: Rdestnica grzebieniasta (Stuckenia pectinata (L.) Börner), 3503

description: Herbarium
DOI: 10.5281/zenodo.5795716
title: Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

description: Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.
description: This research was supported by the National Science Foundation (Grant No. 1756568), the Department of Energy (Grant No. DE-SC0019344), and the National Oceanographic and Atmospheric Administration (Grant No. NA21OAR4310256).
DOI: 10.5281/zenodo.4774437
title: Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

description: Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.
description: This research was supported by the National Science Foundation (Grant No. 1756568), the Department of Energy (Grant No. DE-SC0019344), and the National Oceanographic and Atmospheric Administration (Grant No. NA21OAR4310256).
DOI: 10.48370/ofd/bytsq9/59sn12
title: HMW_Streptopus_amplexifolius_(L.)_DC._H_01360.jpg

description: :unav
DOI: 10.48370/ofd/bytsq9
title: Liczydło górskie (Streptopus amplexifolius (L.) DC.), 1360

description: Herbarium
DOI: 10.48370/ofd/qi2xxg/yxs45o
title: HMW_Stratiotes_aloides_L._H_00689.jpg

description: :unav
DOI: 10.48370/ofd/qi2xxg
title: Osoka aloesowata (Stratiotes aloides L.), 689

description: Herbarium
DOI: 10.5255/ukda-sn-855330
title: Embodying Voice in Training and Performance: A Process-Oriented Approach, 2016-2019

description: This PhD thesis describes a practice-as-research (PaR) study on the psychophysical process of embodying voice in performance, understood as any vocal act, including speech, singing, vocalization and the like, where voice is a means of expression. The research developed a process-oriented approach to voicework to address some of the issues a performer may encounter during the process of embodying voice on stage, mainly related to unintentional expressions, so-called mistakes, and disruption of flow. As such, this study examines how the process-oriented psychology (processwork) of Arnold and Amy Mindell, sound art and new music can contribute to post-Grotowskian and psychophysical training and performance. The research methodology draws upon practice-as-research with reference to cognitive turn in performing arts. I structured the study on the basis of a feedback loop, where practical exploration both feeds into and is fed by theoretical and qualitative research. To articulate the performers’ experience of embodying voice in words, in this thesis I use phenomenological descriptions, complement them with phenomenotechnical explanations of the applied techniques, and ethnographic analysis of the Grotowski lineage of theatre. Through the inquiry, I critically analysed and revised training and performance objectives and the organizational and conceptual structures under which voicework is conducted within the post-Grotowskian lineage of theatre. I challenged the concept of “opening an actor”; the practice of favouring highly altered, “free from resistance” states on stage; and training performers' voices according to the premise of right or wrong sounds while sourcing from performer’s everyday life experience. The study revealed limitations and potentially long-lasting repercussions of such practices on a performer’s well-being. In this thesis, I present an originally formulated notion of dreamvoice that escapes a duality of validation between right/wrong. I argue for new ethics and aesthetics of voicework and acting in the realm of theatre informed by post-Grotowskian practice. I aim to provide a performer with tools empowering them to navigate their psychophysical process of embodying voice on stage in a more sustainable and self-directive way based on the mechanisms of what I define as a perception-expression loop of voice. As such, the thesis presents an approach to training focused on metaskills, and an original form of a performance-sound installation where the performers can maintain a lucid relationship with the vocal material, creatively utilise unintentional expressions in the performance dramaturgy and apply performance states that range from casual to heightened.
DOI: 10.6084/m9.figshare.17026018
title: Time Series Approach to the Evolution of Networks: Prediction and Estimation

description: The article analyzes nonnegative multivariate time series which we interpret as weighted networks. We introduce a model where each coordinate of the time series represents a given edge across time. The number of time periods is treated as large compared to the size of the network. The model specifies the temporal evolution of a weighted network that combines classical autoregression with nonnegativity, a positive probability of vanishing, and peer effect interactions between weights assigned to edges in the process. The main results provide criteria for stationarity versus explosiveness of the network evolution process and techniques for estimation of the parameters of the model and for prediction of its future values. Natural applications arise in networks of fixed number of agents, such as countries, large corporations, or small social communities. The article provides an empirical implementation of the approach to monthly trade data in European Union. Overall, the results confirm that incorporating nonnegativity of dependent variables into the model matters and incorporating peer effects leads to the improved prediction power.
DOI: 10.48370/ofd/4h30ro/ozdmhs
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_5216.jpg

description: :unav
DOI: 10.6084/m9.figshare.17026018.v2
title: Time Series Approach to the Evolution of Networks: Prediction and Estimation

description: The article analyzes nonnegative multivariate time series which we interpret as weighted networks. We introduce a model where each coordinate of the time series represents a given edge across time. The number of time periods is treated as large compared to the size of the network. The model specifies the temporal evolution of a weighted network that combines classical autoregression with nonnegativity, a positive probability of vanishing, and peer effect interactions between weights assigned to edges in the process. The main results provide criteria for stationarity versus explosiveness of the network evolution process and techniques for estimation of the parameters of the model and for prediction of its future values. Natural applications arise in networks of fixed number of agents, such as countries, large corporations, or small social communities. The article provides an empirical implementation of the approach to monthly trade data in European Union. Overall, the results confirm that incorporating nonnegativity of dependent variables into the model matters and incorporating peer effects leads to the improved prediction power.
DOI: 10.48370/ofd/4h30ro
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 5216

description: Herbarium
DOI: 10.48370/ofd/r2tvvi/lgvhce
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_3982.jpg

description: :unav
DOI: 10.48370/ofd/r2tvvi
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 3982

description: Herbarium
DOI: 10.48370/ofd/wnmpxc/8elpfk
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_2534.jpg

description: :unav
DOI: 10.48370/ofd/wnmpxc
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 2534

description: Herbarium
DOI: 10.48370/ofd/kpctab/he6lrp
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_1598.jpg

description: :unav
DOI: 10.48370/ofd/kpctab
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 1598

description: Herbarium
DOI: 10.48370/ofd/jyhotm/n5dnig
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_0682.jpg

description: :unav
DOI: 10.48370/ofd/jyhotm
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 682

description: Herbarium
DOI: 10.48370/ofd/5eabc0/7utgql
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_4224.jpg

description: :unav
DOI: 10.48370/ofd/5eabc0
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 4224

description: Herbarium
DOI: 10.48370/ofd/t5qty1/lkf43h
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_2521.jpg

description: :unav
DOI: 10.48370/ofd/t5qty1
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 2521

description: Herbarium
DOI: 10.48370/ofd/ftovez/lqqkzj
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_1452.jpg

description: :unav
DOI: 10.48370/ofd/ftovez
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 1452

description: Herbarium
DOI: 10.48370/ofd/dbzjl4/fhoa6q
title: HMW_Symphoricarpos_albus_(L.)_S._F._Blake_H_0156.jpg

description: :unav
DOI: 10.48370/ofd/dbzjl4
title: Śnieguliczka biała (Symphoricarpos albus (L.) S. F. Blake), 156

description: Herbarium
DOI: 10.48370/ofd/cqpszy/xytslj
title: HMW_Syringa_vulgaris_L._H_2507.jpg

description: :unav
DOI: 10.48370/ofd/cqpszy
title: Lilak pospolity (Syringa vulgaris L.), 2507

description: Herbarium
DOI: 10.48370/ofd/apneup/omx2by
title: HMW_Syringa_vulgaris_L._H_2506.jpg

description: :unav
DOI: 10.48370/ofd/apneup
title: Lilak pospolity (Syringa vulgaris L.), 2506

description: Herbarium
DOI: 10.48370/ofd/2eue85/mzjxni
title: HMW_Syringa_vulgaris_L._H_2505.jpg

description: :unav
DOI: 10.48370/ofd/2eue85
title: Lilak pospolity (Syringa vulgaris L.), 2505

description: Herbarium
DOI: 10.48370/ofd/uyapkd/iu2goy
title: HMW_Syringa_vulgaris_L._H_0675.jpg

description: :unav
DOI: 10.48370/ofd/uyapkd
title: Lilak pospolity (Syringa vulgaris L.), 675

description: Herbarium
DOI: 10.25721/28cd-fv91
title: Information sur le jeu de Lagalie et Blancher
DOI: 10.48370/ofd/bjiicl/xm2nga
title: HMW_Syringa_josikaea_J._Jacq._ex_Rchb._H_05199.jpg

description: :unav
DOI: 10.48370/ofd/bjiicl
title: Lilak Josiki (Syringa josikaea J. Jacq. ex Rchb.), 5199

description: Herbarium
DOI: 10.25721/ca90-kg59
title: Garabit-valse
DOI: 10.25721/a4yr-p653
title: Regret de marche nuptiale
DOI: 10.25721/qech-rj79
title: Regret "Vous êtes mon mari"