cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4268684
title: BONE STRAIN INDEX AS A PREDICTOR OF FURTHER VERTEBRAL FRACTURE IN OSTEOPOROTIC WOMEN

description: Background: Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artificial Intelligence-based, to identify dual X-ray absorptiometry (DXA) variables able to characterise those patients who are prone to further fractures called Bone Strain Index, was evaluated in this study. Methods: In a prospective, longitudinal, multicentric study 172 female outpatients with at least one vertebral fracture at the first observation were enrolled. They performed a spine X-ray to calculate spine deformity index (SDI) and a lumbar and femoral DXA scan to assess bone mineral density (BMD) and bone strain index (BSI) at baseline and after a follow-up period of 3 years in average. At the end of the follow-up, 93 women developed a further vertebral fracture. The further vertebral fracture was considered as one unit increase of SDI. We assessed the predictive capacity of supervised Artificial Neural Networks (ANNs) to distinguish women consistently with a further fracture from those without a further fracture and to identify the variables expressing the maximal amount of relevant information to discriminate the two groups. ANNs were allowed to choose relevant input data automatically (TWIST-system). Moreover, we constructed a semantic connectivity map with Auto Contractive Map to offer some insight regarding the complex biological connections among the studied variables and the two conditions (further fracture vs no further fracture). Results: TWIST system selected 5 out of 13 available variables: age, menopause age, BMI, FTot BMC, FTot BSI. With training testing procedure, ANNs reached predictive accuracy of 79.36%, with a sensitivity of 75% and a specificity of 83.72%. The semantic connectivity map highlighted the role of BSI in predicting the risk of a further fracture. Conclusions: Artificial Intelligence is a useful method to analyse a complex system like that regarding osteoporosis, able to identify patients prone to a further fragility fracture. BSI appears to be a useful DXA index in identifying those patients who are at risk of further vertebral fractures.
DOI: 10.5281/zenodo.4268683
title: BONE STRAIN INDEX AS A PREDICTOR OF FURTHER VERTEBRAL FRACTURE IN OSTEOPOROTIC WOMEN

description: Background: Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artificial Intelligence-based, to identify dual X-ray absorptiometry (DXA) variables able to characterise those patients who are prone to further fractures called Bone Strain Index, was evaluated in this study. Methods: In a prospective, longitudinal, multicentric study 172 female outpatients with at least one vertebral fracture at the first observation were enrolled. They performed a spine X-ray to calculate spine deformity index (SDI) and a lumbar and femoral DXA scan to assess bone mineral density (BMD) and bone strain index (BSI) at baseline and after a follow-up period of 3 years in average. At the end of the follow-up, 93 women developed a further vertebral fracture. The further vertebral fracture was considered as one unit increase of SDI. We assessed the predictive capacity of supervised Artificial Neural Networks (ANNs) to distinguish women consistently with a further fracture from those without a further fracture and to identify the variables expressing the maximal amount of relevant information to discriminate the two groups. ANNs were allowed to choose relevant input data automatically (TWIST-system). Moreover, we constructed a semantic connectivity map with Auto Contractive Map to offer some insight regarding the complex biological connections among the studied variables and the two conditions (further fracture vs no further fracture). Results: TWIST system selected 5 out of 13 available variables: age, menopause age, BMI, FTot BMC, FTot BSI. With training testing procedure, ANNs reached predictive accuracy of 79.36%, with a sensitivity of 75% and a specificity of 83.72%. The semantic connectivity map highlighted the role of BSI in predicting the risk of a further fracture. Conclusions: Artificial Intelligence is a useful method to analyse a complex system like that regarding osteoporosis, able to identify patients prone to a further fragility fracture. BSI appears to be a useful DXA index in identifying those patients who are at risk of further vertebral fractures.
DOI: 10.5281/zenodo.5985787
title: Dati estratti da "Epidemia COVID-19", bollettino dell'Istituto Superiore di Sanità (ISS)

description: “EPIDEMIA COVID-19” è un bollettino prodotto dall’Istituto Superiore di Sanità (ISS) che riporta i dati della sorveglianza integrata concernente l’infezione da virus SARS-CoV-2. Il bollettino è redatto ai sensi dell’Ordinanza n. 640 del 27 febbraio 2020. Si legge nella nota introduttiva che “Il conteggio dei casi viene effettuato sommando tutti i casi di infezione da virus SARS-CoV2 confermati tramite test molecolari e antigenici, e segnalati al sistema di sorveglianza attraverso una piattaforma web dedicata. Il database nazionale è quindi la somma dei casi presenti nei 21 sistemi informativi regionali/PPAA costruiti ad hoc per la sorveglianza COVID-19”. Si riportano di seguito grafici di dati estratti dai rapporti, concernenti casistica su persone di età ≥ 12 anni, ricoveri e vittime. Le tabelle indicano anche lo stato vaccinale e la fascia d’età. Si presti attenzione che le classi relative alla vaccinazione sono state ridefinite a seconda dei Decreti Legge del Governo. I dati sono estratti partendo dal bollettino del 10 Novembre 2021 fino a quelli del 2 Febbraio 2022. <em>Si noti che i dati riportati </em><em>risultano dal conteggio sull’arco temporale di un mese. I bollettini differiscono per lo s</em><em>postamento</em><em> di una settimana. L’inizio dell’arco temporale è </em><em>in ritardo di un mese </em><em>per i contagi ma di più di un </em><em>mese </em><em>per gli altri dati.</em> Il primo bollettino considerato è quello 10-Novembre-2021 che riporta le diagnosi di contagio dal 08/10/2021 al 07/11/2021, il numero delle vittime tra il 17/09/2021 al 17/10/2021, e i dati di ospedalizzazioni e terapie intensive dal 01/10/2021 al 31/10/2021. Il bollettino più recente è quello 02-Febbraio-2022, pubblicato il 5 Febbraio 2022., contiene i dati delle diagnosi di contagio tra il 31 Dicembre 2021 ed il 30 Gennaio 2022, delle vittime tra il 10/12/2021 al 09/01/2022, delle ospedalizzazioni 17/12/2021 al 16/01/2022 , delle terapie intensive 17/12/2021 al 16/01/2022.
description: Vedi nuova versione
DOI: 10.48370/ofd/yugvns/sfvon8
title: Peronospora lathyri.verni Gustavsson F-2278.tif

description: :unav
DOI: 10.48370/ofd/yugvns/vvbkjd
title: Peronospora lathyri.verni Gustavsson F-2278 (2).tif

description: :unav
DOI: 10.48370/ofd/yugvns/gy7ake
title: Peronospora lathyri.verni Gustavsson F-2278 (4).tif

description: :unav
DOI: 10.48370/ofd/yugvns/utpxxm
title: Peronospora lathyri.verni Gustavsson F-2278 (3).tif

description: :unav
DOI: 10.48370/ofd/yugvns
title: Peronospora lathyri-verni Gustavsson, 2278

description: Fungarium Micromycetes
DOI: 10.5281/zenodo.5985718
title: Dati estratti da "Epidemia COVID-19", bollettino dell'Istituto Superiore di Sanità (ISS)

description: <br> “EPIDEMIA COVID-19” è un bollettino prodotto dall’Istituto Superiore di Sanità (ISS) che riporta i dati della sorveglianza integrata concernente l’infezione da virus SARS-CoV-2. Il bollettino è redatto ai sensi dell’Ordinanza n. 640 del 27 febbraio 2020. Si legge nella nota introduttiva che “Il conteggio dei casi viene effettuato sommando tutti i casi di infezione da virus SARS-CoV2 confermati tramite test molecolari e antigenici, e segnalati al sistema di sorveglianza attraverso una piattaforma web dedicata. Il database nazionale è quindi la somma dei casi presenti nei 21 sistemi informativi regionali/PPAA costruiti ad hoc per la sorveglianza COVID-19”. Si riportano di seguito grafici di dati estratti dai rapporti, concernenti casistica su persone di età ≥ 12 anni, ricoveri e vittime. Le tabelle indicano anche lo stato vaccinale e la fascia d’età. Si presti attenzione che le classi relative alla vaccinazione sono state ridefinite a seconda dei Decreti Legge del Governo. I dati sono estratti partendo dal bollettino del 10 Novembre 2021 fino a quelli del 2 Febbraio 2022. Il bollettino 02-Febbraio-2022, pubblicato il 5 Febbraio 2022 contiene i dati delle diagnosi tra il 31 Dicembre 2021 ed il 30 Gennaio 2022, delle vittime tra il 10/12/2021 al 09/01/2022., delle ospedalizzazioni 17/12/2021 al 16/01/2022., delle terapie intensive 17/12/2021 al 16/01/2022..
description: Vedi nuova versione
DOI: 10.48370/ofd/vyuqk3/lwszvy
title: Peronospora lathyri.verni Gustavsson F-0183.tif

description: :unav
DOI: 10.5281/zenodo.5992737
title: Origin and evolution of nephrite, diopsidites and giant diopside crystals from the contact zones of the Pounamu Ultramafics, Westland, New Zealand

description: Paper in New Zealand Journal of Geology and Geophysics
DOI: 10.48370/ofd/vyuqk3
title: Peronospora lathyri-verni Gustavsson, 183

description: Fungarium Micromycetes
DOI: 10.5281/zenodo.5992736
title: Origin and evolution of nephrite, diopsidites and giant diopside crystals from the contact zones of the Pounamu Ultramafics, Westland, New Zealand

description: Paper in New Zealand Journal of Geology and Geophysics
DOI: 10.48370/ofd/qgyzar/6itd9o
title: Peronospora lathyri.verni Gustavsson F-2736.tif

description: :unav
DOI: 10.48370/ofd/qgyzar/cgkvpi
title: Peronospora lathyri.verni Gustavsson F-2736 (2).tif

description: :unav
DOI: 10.48370/ofd/qgyzar/88p1v9
title: Peronospora lathyri.verni Gustavsson F-2736 (4).tif

description: :unav
DOI: 10.48370/ofd/qgyzar/kgoqug
title: Peronospora lathyri.verni Gustavsson F-2736 (3).tif

description: :unav
DOI: 10.48370/ofd/qgyzar
title: Peronospora lathyri-verni Gustavsson, 2736

description: Fungarium Micromycetes
DOI: 10.48370/ofd/soazye/edsf8r
title: Peronospora lathyri.verni Gustavsson F-0685.jpg

description: :unav
DOI: 10.48370/ofd/soazye/9oasbk
title: Peronospora lathyri.verni Gustavsson F-0685.tif

description: :unav
DOI: 10.48370/ofd/soazye
title: Peronospora lathyri-verni Gustavsson, 685

description: Fungarium Micromycetes
DOI: 10.48370/ofd/94axum/5jom8w
title: Peronospora lathyri.verni Gustavsson F-3561.tif

description: :unav
DOI: 10.48370/ofd/94axum
title: Peronospora lathyri-verni Gustavsson, 3561

description: Fungarium Micromycetes
DOI: 10.48370/ofd/doqe5n/akm33o
title: Peronospora mayorii Gäumann F-0160.tif

description: :unav
DOI: 10.5281/zenodo.5992891
title: In-operando visualization of redox flow battery in membrane-free microfluidic platform

description: These data sheets are voltage-capacity characteristics of in-operando visualization in terms of mass-, and charge-transfer.
DOI: 10.48370/ofd/doqe5n
title: Peronospora mayorii Gäumann, 160

description: Fungarium Micromycetes
DOI: 10.5281/zenodo.5992890
title: In-operando visualization of redox flow battery in membrane-free microfluidic platform

description: These data sheets are voltage-capacity characteristics of in-operando visualization in terms of mass-, and charge-transfer.
DOI: 10.48370/ofd/2b0jjk/mxwnmg
title: Peronospora melampyri (Bucholtz) J. Davis F-0185.tif

description: :unav
DOI: 10.48370/ofd/2b0jjk
title: Peronospora melampyri (Bucholtz) J. Davis, 185

description: Fungarium Micromycetes
DOI: 10.48370/ofd/qtaq6x/uoulgm
title: Peronospora minor (Caspary) Gäumann F-1056.tif

description: :unav
DOI: 10.48370/ofd/qtaq6x
title: Peronospora minor (Caspary) Gäumann, 1056

description: Fungarium Micromycetes
DOI: 10.48370/ofd/9z17qg/ya4w3s
title: Peronospora myosotidis de Bary F-1786.tif

description: :unav
DOI: 10.48370/ofd/9z17qg
title: Peronospora myosotidis de Bary, 1786

description: Fungarium Micromycetes
DOI: 10.48370/ofd/dmgnlz/c1ilww
title: Peronospora myosotidis de Bary F-0182.tif

description: :unav
DOI: 10.48370/ofd/dmgnlz
title: Peronospora myosotidis de Bary, 182

description: Fungarium Micromycetes
DOI: 10.48370/ofd/lywlgj/faudy4
title: Peronospora polygoni.convolvuli Gustavsoon F-1057 (2).tif

description: :unav
DOI: 10.48370/ofd/lywlgj/yiqcfs
title: Peronospora polygoni.convolvuli Gustavsoon F-1057.tif

description: :unav
DOI: 10.48370/ofd/lywlgj/xc6mmo
title: Peronospora polygoni.convolvuli Gustavsoon F-1057 (3).tif

description: :unav
DOI: 10.48370/ofd/lywlgj
title: Peronospora polygoni-convolvuli Gustavsoon, 1057

description: Fungarium Micromycetes
DOI: 10.48370/ofd/utpdh8/trppft
title: Peronospora polygoni.convolvuli Gustavsoon F-1052 (2).tif

description: :unav
DOI: 10.48370/ofd/utpdh8/odmptu
title: Peronospora polygoni.convolvuli Gustavsoon F-1052.jpg

description: :unav
DOI: 10.48370/ofd/utpdh8/dv4cbq
title: Peronospora polygoni.convolvuli Gustavsoon F-1052 (4).tif

description: :unav
DOI: 10.48370/ofd/utpdh8/5nplhl
title: Peronospora polygoni.convolvuli Gustavsoon F-1052.tif

description: :unav
DOI: 10.48370/ofd/1wzliw/qqxvrr
title: Peronospora polygoni.convolvuli Gustavsoon F-0910.tif

description: :unav
DOI: 10.48370/ofd/utpdh8/oyclsj
title: Peronospora polygoni.convolvuli Gustavsoon F-1052 (3).tif

description: :unav
DOI: 10.48370/ofd/1wzliw/xqclee
title: Peronospora polygoni.convolvuli Gustavsoon F-0910 (2).tif

description: :unav
DOI: 10.48370/ofd/utpdh8
title: Peronospora polygoni-convolvuli Gustavsoon, 1052

description: Fungarium Micromycetes
DOI: 10.48370/ofd/1wzliw/ki05hi
title: Peronospora polygoni.convolvuli Gustavsoon F-0910 (4).tif

description: :unav
DOI: 10.48370/ofd/1wzliw/24cs1g
title: Peronospora polygoni.convolvuli Gustavsoon F-0910 (3).tif

description: :unav
DOI: 10.48370/ofd/1wzliw
title: Peronospora polygoni-convolvuli Gustavsoon, 910

description: Fungarium Micromycetes
DOI: 10.48370/ofd/5r9trp/d7dmo6
title: Peronospora ranunculi Gäumann F-0204.tif

description: :unav
DOI: 10.48370/ofd/5r9trp
title: Peronospora ranunculi Gäumann, 204

description: Fungarium Micromycetes
DOI: 10.48370/ofd/mx3hqn/kylfn0
title: Peronospora ranunculi Gäumann F-0203.tif

description: :unav
DOI: 10.48370/ofd/mx3hqn
title: Peronospora ranunculi Gäumann, 203

description: Fungarium Micromycetes
DOI: 10.48370/ofd/9rvbwv/vcbmt0
title: Peronospora ranunculi Gäumann F-0202.tif

description: :unav
DOI: 10.48370/ofd/9rvbwv
title: Peronospora ranunculi Gäumann, 202

description: Fungarium Micromycetes
DOI: 10.48370/ofd/kwhsgw/1lp6xf
title: Peronospora ranunculi Gäumann F-2554.tif

description: :unav
DOI: 10.48370/ofd/kwhsgw
title: Peronospora ranunculi Gäumann, 2554

description: Fungarium Micromycetes
DOI: 10.48370/ofd/ddqeea/grn6az
title: Peronospora ranunculi Gäumann F-0006.tif

description: :unav
DOI: 10.48370/ofd/ddqeea
title: Peronospora ranunculi Gäumann, 6

description: Fungarium Micromycetes
DOI: 10.48370/ofd/vphx6d/s0jfbi
title: Peronospora ranunculi Gäumann F-1793 (5).tif

description: :unav
DOI: 10.48370/ofd/vphx6d/nekjp4
title: Peronospora ranunculi Gäumann F-1793 (3).tif

description: :unav
DOI: 10.48370/ofd/vphx6d/uq1otf
title: Peronospora ranunculi Gäumann F-1793 (2).tif

description: :unav
DOI: 10.48370/ofd/vphx6d/aomszc
title: Peronospora ranunculi Gäumann F-1793 (4).tif

description: :unav
DOI: 10.48370/ofd/vphx6d/mz7cgs
title: Peronospora ranunculi Gäumann F-1793.tif

description: :unav
DOI: 10.48370/ofd/vphx6d
title: Peronospora ranunculi Gäumann, 1793

description: Fungarium Micromycetes
DOI: 10.48370/ofd/riesdr/x2zuo6
title: Peronospora ranunculi Gäumann F-0207.tif

description: :unav
DOI: 10.48370/ofd/riesdr
title: Peronospora ranunculi Gäumann, 207

description: Fungarium Micromycetes
DOI: 10.48370/ofd/rzvnlx/5tu6y5
title: Peronospora ranunculi Gäumann F-0208.tif

description: :unav
DOI: 10.48370/ofd/rzvnlx
title: Peronospora ranunculi Gäumann, 208

description: Fungarium Micromycetes
DOI: 10.48370/ofd/v7ei0l/0mnndj
title: Peronospora ranunculi Gäumann F-0210.tif

description: :unav
DOI: 10.48370/ofd/v7ei0l
title: Peronospora ranunculi Gäumann, 210

description: Fungarium Micromycetes
DOI: 10.48370/ofd/nvdych/uur4rw
title: Peronospora ranunculi Gäumann F-0201.tif

description: :unav
DOI: 10.48370/ofd/nvdych
title: Peronospora ranunculi Gäumann, 201

description: Fungarium Micromycetes
DOI: 10.48370/ofd/0ak3op/ksfsbf
title: Peronospora ranunculi Gäumann F-0211.tif

description: :unav
DOI: 10.48370/ofd/0ak3op
title: Peronospora ranunculi Gäumann, 211

description: Fungarium Micromycetes
DOI: 10.48370/ofd/lxqoo1/g2fnd8
title: Peronospora ranunculi Gäumann F-3762.tif

description: :unav
DOI: 10.48370/ofd/lxqoo1
title: Peronospora ranunculi Gäumann, 3762

description: Fungarium Micromycetes
DOI: 10.48370/ofd/o4rhnd/btdgc2
title: Peronospora ranunculi Gäumann F-0205.tif

description: :unav
DOI: 10.48370/ofd/o4rhnd
title: Peronospora ranunculi Gäumann, 205

description: Fungarium Micromycetes
DOI: 10.48370/ofd/h0nkzx/xszmfm
title: Peronospora ranunculi Gäumann F-0206.tif

description: :unav
DOI: 10.48370/ofd/h0nkzx
title: Peronospora ranunculi Gäumann, 206

description: Fungarium Micromycetes
DOI: 10.48370/ofd/w7vgpt/mmtgie
title: Peronospora ranunculi Gäumann F-0209.tif

description: :unav
DOI: 10.48370/ofd/w7vgpt
title: Peronospora ranunculi Gäumann, 209

description: Fungarium Micromycetes
DOI: 10.5281/zenodo.5973211
title: Link to dataset related to article "DNA Methylation Signature in Monozygotic Twins Discordant for Psoriatic Disease"

description: The record contains raw data related to the article "DNA Methylation Signature in Monozygotic Twins Discordant for Psoriatic Disease". <strong>Background:</strong> Psoriatic disease is a multifactorial inflammatory condition spanning from skin and nail psoriasis (Pso) to spine and joint involvement characterizing psoriatic arthritis (PsA). Monozygotic twins provide a model to investigate genetic, early life environmental exposure and stochastic influences to complex diseases, mainly mediated by epigenetics. <strong>Methods:</strong> We performed a genome-wide DNA methylation study on whole blood of monozygotic twins from 7 pairs discordant for Pso/PsA using the Infinium Methylation EPIC array (Illumina). MeDiP-qPCR was used to confirm specific signals. Data were replicated in an independent cohort of seven patients with Pso/PsA and 3 healthy controls. Transcriptomic profiling was performed by RNAsequence on the same 7 monozygotic twin pairs. <strong>Results:</strong> We identified 2,564 differentially methylated positions between psoriatic disease and controls, corresponding to 1,703 genes, 59% within gene bodies. There were 19 regions with at least two DMPs within 1 kb of distance and significant within-pair Δ<em>β</em>-values (<em>p</em> &lt; 0.005), among them SNX25, BRG1 and SMAD3 genes, all involved in TGF-β signaling pathway, were identified. Co-expression analyses on transcriptome data identified IL-6/JAK/STAT3 and TNF-α pathways as important signaling axes involved in the disease, and they also suggested an altered glucose metabolism in patients' immune cells, characteristic of pro-inflammatory T lymphocytes. <strong>Conclusion:</strong> The study suggests the presence of an epigenetic signature in affected individuals, pointing to genes involved in immunological and inflammatory responses. This result is also supported by transcriptome data, that altogether suggest a higher activation state of the immune system, that could promote the disease status.
DOI: 10.5281/zenodo.5973210
title: Link to dataset related to article "DNA Methylation Signature in Monozygotic Twins Discordant for Psoriatic Disease"

description: The record contains raw data related to the article "DNA Methylation Signature in Monozygotic Twins Discordant for Psoriatic Disease". <strong>Background:</strong> Psoriatic disease is a multifactorial inflammatory condition spanning from skin and nail psoriasis (Pso) to spine and joint involvement characterizing psoriatic arthritis (PsA). Monozygotic twins provide a model to investigate genetic, early life environmental exposure and stochastic influences to complex diseases, mainly mediated by epigenetics. <strong>Methods:</strong> We performed a genome-wide DNA methylation study on whole blood of monozygotic twins from 7 pairs discordant for Pso/PsA using the Infinium Methylation EPIC array (Illumina). MeDiP-qPCR was used to confirm specific signals. Data were replicated in an independent cohort of seven patients with Pso/PsA and 3 healthy controls. Transcriptomic profiling was performed by RNAsequence on the same 7 monozygotic twin pairs. <strong>Results:</strong> We identified 2,564 differentially methylated positions between psoriatic disease and controls, corresponding to 1,703 genes, 59% within gene bodies. There were 19 regions with at least two DMPs within 1 kb of distance and significant within-pair Δ<em>β</em>-values (<em>p</em> &lt; 0.005), among them SNX25, BRG1 and SMAD3 genes, all involved in TGF-β signaling pathway, were identified. Co-expression analyses on transcriptome data identified IL-6/JAK/STAT3 and TNF-α pathways as important signaling axes involved in the disease, and they also suggested an altered glucose metabolism in patients' immune cells, characteristic of pro-inflammatory T lymphocytes. <strong>Conclusion:</strong> The study suggests the presence of an epigenetic signature in affected individuals, pointing to genes involved in immunological and inflammatory responses. This result is also supported by transcriptome data, that altogether suggest a higher activation state of the immune system, that could promote the disease status.
DOI: 10.6084/m9.figshare.19130258
title: Ground penetrating radar data from the foreland of the glacier Midtdalsbreen (Norway) in spring 2019.

description: This dataset contains raw ground penetrating radar (GPR) files collected over three sites at the foreland of the glacier Midtdalsbreen (60°34'N 7°27'E) in southern Norway. The GPR data files can be viewed using Reflexw software. Information on data collection is provided in the corresponding observer logs file (GPR_obslogs.xlsx). The data was collected in the spring, when the ground was snow covered.Once processed, this data can reveal structures within glacial landforms and provide information on the electrical properties of the substrates beneath the survey lines.GPR data were collected using a MALÅ system with rough terrain antennas with central frequencies of 25 MHz and 100 MHz. At the Lateral Moraine and Terminal Moraine sites, data were collected along and perpendicular to moraine crests and at the Hummocky Site, data collection was undertaken on a grid. See Watts et al. (2022) for detail on data collection.
DOI: 10.48370/ofd/vsmczd/thf94y
title: Peronospora rumicis Corda F_3856 (2).tif

description: :unav
DOI: 10.48370/ofd/vsmczd/aajl76
title: Peronospora rumicis Corda F_3856 (4).tif

description: :unav
DOI: 10.48370/ofd/vsmczd/vhwojb
title: Peronospora rumicis Corda F_3856.tif

description: :unav
DOI: 10.48370/ofd/vsmczd/tj1jf8
title: Peronospora rumicis Corda F_3856 (3).tif

description: :unav
DOI: 10.48370/ofd/vsmczd/bxbmer
title: Peronospora rumicis Corda F_3856 (5).tif

description: :unav
DOI: 10.48370/ofd/vsmczd
title: Peronospora rumicis Corda, 3856

description: Fungarium Micromycetes