cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25fcyy
title: CCDC 2008860: Experimental Crystal Structure Determination

description: Related Article: Oksana Toma, Nicolas Mercier, Magali Allain, Francesco Meinardi, Alessandra Forni, Chiara Botta|2020|Cryst.Growth Des.|20|7658|doi:10.1021/acs.cgd.0c00872
DOI: 10.5517/ccdc.csd.cc25rt5y
title: CCDC 2018880: Experimental Crystal Structure Determination

description: Related Article: Xianghe Meng, Kaijin Kang, Youquan Liu, Jian Tang, Xingxing Jiang, Wenlong Yin, Zheshuai Lin, Mingjun Xia|2020|Cryst.Growth Des.|20|7588|doi:10.1021/acs.cgd.0c01163
DOI: 10.5517/ccdc.csd.cc25phcr
title: CCDC 2016654: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phhw
title: CCDC 2016658: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phw8
title: CCDC 2016670: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phgv
title: CCDC 2016657: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phlz
title: CCDC 2016661: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phn1
title: CCDC 2016663: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phky
title: CCDC 2016660: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phds
title: CCDC 2016655: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc25phm0
title: CCDC 2016662: Experimental Crystal Structure Determination

description: Related Article: Yijue Xu, Vijith Kumar, Maressa J. Z. Bradshaw, David L. Bryce|2020|Cryst.Growth Des.|20|7910|doi:10.1021/acs.cgd.0c01173
DOI: 10.5517/ccdc.csd.cc268lqt
title: CCDC 2034056: Experimental Crystal Structure Determination

description: Related Article: Chenghao Liu, Ehsan Hamzehpoor, Yoko Sakai-Otsuka, Thaksen Jadhav, Dmitrii F Perepichka|2020|Angew.Chem.,Int.Ed.|59|23030|doi:10.1002/anie.202009867
DOI: 10.5517/ccdc.csd.cc26ksk4
title: CCDC 2042886: Experimental Crystal Structure Determination

description: Related Article: Jun Xi, Ioannis Spanopoulos, Kijoon Bang, Jie Xu, Hua Dong, Yingguo Yang, Christos D. Malliakas, Justin M. Hoffman, Mercouri G. Kanatzidis, Zhaoxin Wu|2020|J.Am.Chem.Soc.|142|19705|doi:10.1021/jacs.0c09647
DOI: 10.5517/ccdc.csd.cc26ksl5
title: CCDC 2042887: Experimental Crystal Structure Determination

description: Related Article: Jun Xi, Ioannis Spanopoulos, Kijoon Bang, Jie Xu, Hua Dong, Yingguo Yang, Christos D. Malliakas, Justin M. Hoffman, Mercouri G. Kanatzidis, Zhaoxin Wu|2020|J.Am.Chem.Soc.|142|19705|doi:10.1021/jacs.0c09647
DOI: 10.5517/ccdc.csd.cc26ljw7
title: CCDC 2043609: Experimental Crystal Structure Determination

description: Related Article: E.A. Kvyatkovskaya, Z. Atioğlu, M. Akkurt, P.P. Epifanova, K.S. Valchuk, V.N. Khrustalev, A. Bhattarai|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1827|doi:10.1107/S2056989020014966
DOI: 10.5517/ccdc.csd.cc26ljx8
title: CCDC 2043610: Experimental Crystal Structure Determination

description: Related Article: B.R. Srinivasan, N.U. Parsekar, K.U. Narvekar|2020|IUCrData|5|x201498|doi:10.1107/S2414314620014984
DOI: 10.5517/ccdc.csd.cc26ln3l
title: CCDC 2043709: Experimental Crystal Structure Determination

description: Related Article: H. Khaldi, A. Djafri, Y. Megrouss, N. Khelloul, A. Chouaih, A. Djafri|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1832|doi:10.1107/S2056989020015017
DOI: 10.21258/1552060
title: Inventaire forestier national suisse - Tableau no 891795
DOI: 10.21258/1591148
title: Inventario forestale nazionale svizzero - Tabella n. 897694
DOI: 10.21258/1598245
title: Inventario forestale nazionale svizzero - Tabella n. 897574
DOI: 10.6084/m9.figshare.13366013
title: Data for network pharmacology

description: Study on Network Pharmacology of traditional Chinese Medicine Folium nelumbinis in the treatment of Hyperlipidemia
DOI: 10.21258/1587590
title: Inventario forestale nazionale svizzero - Tabella n. 899497
DOI: 10.21258/1549666
title: Inventaire forestier national suisse - Tableau no 898545
DOI: 10.21258/1599388
title: Inventario forestale nazionale svizzero - Tabella n. 897630
DOI: 10.25794/reference/d56qaddg
title: Bradyrhizobium diazoefficiens USDA110 reannotation

description: Reannotation of Bradyrhizobium diazoefficiens USDA110 genome.
DOI: 10.5281/zenodo.4317313
title: GR4J-REG: regional gridded runoff dataset for northwest Russia based on GR4J model and nearest-neighbor regionalization technique

description: GR4J-REG: regional gridded runoff dataset for northwest Russia. Model: GR4J. Regionalization technique: Nearest-neighbor. Geographical domain: 25–57°E; 55–70°N. Spatial resolution: 0.5°x0.5°. Temporal resolution: daily. Period: 1979-2016.
DOI: 10.5281/zenodo.4317312
title: GR4J-REG: regional gridded runoff dataset for northwest Russia based on GR4J model and nearest-neighbor regionalization technique

description: GR4J-REG: regional gridded runoff dataset for northwest Russia. Model: GR4J. Regionalization technique: Nearest-neighbor. Geographical domain: 25–57°E; 55–70°N. Spatial resolution: 0.5°x0.5°. Temporal resolution: daily. Period: 1979-2016.
DOI: 10.21258/1582268
title: Inventario forestale nazionale svizzero - Tabella n. 902104
DOI: 10.5281/zenodo.4315225
title: Britain Breathing 2016-2019 Air Quality and Meteorological Dataset

description: This data set is a collection of daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK for the years 2016-2019, inclusive. The dataset contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model. The tools used to download and process these measurement datasets are available in this repository: https://github.com/UoMResearchIT/UoM_AQ_Data_Tools The dataset is designed for use with the region estimator toolset, available in this repository: https://github.com/UoMResearchIT/region_estimators Emissions over the UK for the EMEP model runs were generated using the NAEI 2016 UK emission dataset, available in netcdf form here: https://zenodo.org/record/3997165#.X9KUBF6nzUI. The running scripts, and operation inputs for EMEP, are available here: https://zenodo.org/record/3997301#.X9KUAF6nzUI and https://zenodo.org/record/3997271#.X9KV1F6nzUI. The dataset is presented in CSV format. The columns intended to be used as indexes are: time_stamp, date of measurements on that row site_id, measurement site ID, corresponding to sites in the three networks: AURN [indicated by AQ], Met Office [indicated by WEATHER], or pollen [indicated by POLLEN]. The data columns are: O3, PM10, PM2.5, NO2, NOXasNO2, SO2, daily mean and maximum values in ug/m3 (all with "_max", "_mean", and "_flag" tags) AURN measurement data O3_EMEP, NO2_EMEP, SO2_EMEP, NOXasNO2_EMEP, PM2.5_EMEP, PM10_EMEP, daily mean and maximum values in ug/m3 (all with "_max", and "_mean" tags) EMEP model forecasts alnus, ambrosia, artemisia, betula, corylus, fraxinus, platanus, poaceae, quercus, salix, ulmus, urtica, daily pollen grain counts temperature, relativehumidity, pressure, daily mean and maximum values in degC, %, and hPa (all with "_max", "_mean", and "_flag" tags). Met Office measurement data The "_flag" columns are intended to indicate data points which have been imputed. In this version of the dataset none have been, so these all will be zero.
DOI: 10.5281/zenodo.4317327
title: LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks

description: LSTM-REG: regional gridded runoff dataset for northwest Russia. Model: LSTM. Geographical domain: 25–57°E; 55–70°N. Spatial resolution: 0.5°x0.5°. Temporal resolution: daily. Period: 1980-2016.
DOI: 10.5281/zenodo.4317326
title: LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks

description: LSTM-REG: regional gridded runoff dataset for northwest Russia. Model: LSTM. Geographical domain: 25–57°E; 55–70°N. Spatial resolution: 0.5°x0.5°. Temporal resolution: daily. Period: 1980-2016.
DOI: 10.5281/zenodo.4317325
title: Hyperpolarization transfer pathways in inorganic materials

description: Hyperpolarization transfer pathways in inorganic materials: experimental data https://doi.org/10.1016/j.jmr.2020.106888
DOI: 10.5281/zenodo.4317324
title: Hyperpolarization transfer pathways in inorganic materials

description: Hyperpolarization transfer pathways in inorganic materials: experimental data https://doi.org/10.1016/j.jmr.2020.106888
DOI: 10.21258/1537335
title: Inventaire forestier national suisse - Tableau no 891952
DOI: 10.21258/1542855
title: Inventaire forestier national suisse - Tableau no 899000
DOI: 10.21258/1586013
title: Inventario forestale nazionale svizzero - Tabella n. 898765
DOI: 10.21258/1530949
title: Inventaire forestier national suisse - Tableau no 902177
DOI: 10.21258/1616877
title: Inventario forestale nazionale svizzero - Tabella n. 891670
DOI: 10.21258/1533145
title: Inventaire forestier national suisse - Tableau no 897575
DOI: 10.21258/1597768
title: Inventario forestale nazionale svizzero - Tabella n. 891816
DOI: 10.21258/1587481
title: Inventario forestale nazionale svizzero - Tabella n. 897908
DOI: 10.6084/m9.figshare.12053079.v1
title: Serum netrin-1 levels at presentation and delayed neurological sequelae in unintentional carbon monoxide poisoning

description: <b>Objectives:</b> The early identification of patients with a high risk of developing delayed neurological sequelae (DNS) can improve the quality of care in carbon monoxide (CO) poisoning cases. The aim of this study is to investigate whether the serum netrin-1 levels measured at presentation to the emergency department (ED) predicted the development of DNS after acute CO intoxication. <b>Methods:</b> This prospective observational study was conducted between 1 August 2018 and 31 July 2019 in a single tertiary hospital. The patients with acute CO intoxication and serum netrin-1 levels measured at the time of ED presentation were included in the study. All patients were followed up for six weeks regarding the development of DNS. The patients were divided into two groups, including those who developed DNS (DNS group) and those who did not (non-DNS group). <b>Results:</b> A total of 183 patients were included in the study, and 54 (29.5%) developed DNS. The median serum netrin-1 level at ED presentation was significantly lower in the DNS group (391.5 pg/mL [263.0–550.5]) than in the non-DNS group (626.0 pg/mL [505.9–755.6]) (<i>p</i> &lt; .001). Multivariate analysis revealed that a low serum netrin-1 level (adjusted odds ratio [AOR]: 8.02, 95% CI: 2.45–26.20), low Glasgow coma scale (GCS) score at ED presentation (AOR: 0.81, 95% CI: 0.68–0.97), long CO exposure time (AOR: 1.96, 95% CI: 1.49–2.56), and the presence of acute brain lesions (AOR: 8.24, 95% CI: 2.37–28.58) on diffusion-weighted imaging were independent predictors of DNS. Serum netrin-1 levels less than 432 pg/mL predicted the development of DNS with a sensitivity of 68.5% (95% CI: 54.4%-80.5%) and a specificity of 86.0% (95% CI: 78.8%-91.5%). <b>Conclusions:</b> Low serum netrin-1 levels were significantly associated with the development of DNS. Therefore, serum netrin-1 at ED presentation can help identify patients at risk of developing DNS following discharge.
DOI: 10.6084/m9.figshare.12053079
title: Serum netrin-1 levels at presentation and delayed neurological sequelae in unintentional carbon monoxide poisoning

description: <b>Objectives:</b> The early identification of patients with a high risk of developing delayed neurological sequelae (DNS) can improve the quality of care in carbon monoxide (CO) poisoning cases. The aim of this study is to investigate whether the serum netrin-1 levels measured at presentation to the emergency department (ED) predicted the development of DNS after acute CO intoxication. <b>Methods:</b> This prospective observational study was conducted between 1 August 2018 and 31 July 2019 in a single tertiary hospital. The patients with acute CO intoxication and serum netrin-1 levels measured at the time of ED presentation were included in the study. All patients were followed up for six weeks regarding the development of DNS. The patients were divided into two groups, including those who developed DNS (DNS group) and those who did not (non-DNS group). <b>Results:</b> A total of 183 patients were included in the study, and 54 (29.5%) developed DNS. The median serum netrin-1 level at ED presentation was significantly lower in the DNS group (391.5 pg/mL [263.0–550.5]) than in the non-DNS group (626.0 pg/mL [505.9–755.6]) (<i>p</i> &lt; .001). Multivariate analysis revealed that a low serum netrin-1 level (adjusted odds ratio [AOR]: 8.02, 95% CI: 2.45–26.20), low Glasgow coma scale (GCS) score at ED presentation (AOR: 0.81, 95% CI: 0.68–0.97), long CO exposure time (AOR: 1.96, 95% CI: 1.49–2.56), and the presence of acute brain lesions (AOR: 8.24, 95% CI: 2.37–28.58) on diffusion-weighted imaging were independent predictors of DNS. Serum netrin-1 levels less than 432 pg/mL predicted the development of DNS with a sensitivity of 68.5% (95% CI: 54.4%-80.5%) and a specificity of 86.0% (95% CI: 78.8%-91.5%). <b>Conclusions:</b> Low serum netrin-1 levels were significantly associated with the development of DNS. Therefore, serum netrin-1 at ED presentation can help identify patients at risk of developing DNS following discharge.
DOI: 10.5281/zenodo.4293402
title: Research data supporting "A Controlled Dendrimersome Nanoreactor System for Localised Hypochlorite-Induced Killing of Bacteria"

description: Raw research data supporting: Potter M. et al., ACS Nano (2020), DOI: 10.1021/acsnano.0c07459.
DOI: 10.5281/zenodo.4293401
title: Research data supporting "A Controlled Dendrimersome Nanoreactor System for Localised Hypochlorite-Induced Killing of Bacteria"

description: Raw research data supporting: Potter M. et al., ACS Nano (2020), DOI: 10.1021/acsnano.0c07459.
DOI: 10.21258/1565209
title: Inventaire forestier national suisse - Tableau no 899028
DOI: 10.5281/zenodo.4315066
title: Chang'e 5 RF recording at 8463.7 MHz with Allen Telescope Array before LOI-1 (X polarization)

description: This dataset contains a recording of X-band telemetry signals from the Chinese lunar sample retrieve mission Chang'e 5 done on 2020-11-28 before the LOI-1 (lunar orbit injection 1) manoeuvre that inserted the spacecraft into an elliptical lunar orbit. The recording was done at Allen Telescope Array using antenna 1h in dual XY (linear) polarization. The hardware configuration was as follows: antenna 1h was connected to RFCB LO d, which was tuned to a frequency of 8463.7 MHz and used an output IF of 512 MHz. A USRP N321 was connected to the IF output of the RFCB and digitized both polarizations. The USRP used an external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 1.92 Msps. A GNU Radio flowgraph was used to record IQ samples to disk. The GNU Radio flowgraph stored the IQ data for each channel as 16-bit integers in the GNU Radio metatata file format, with detached headers. After recording, the data was converted to SigMF format. This dataset contains the IQ data for the X polarization. The original metadata headers are also provided. The Y polarization can be found in this dataset.
DOI: 10.5281/zenodo.4315065
title: Chang'e 5 RF recording at 8463.7 MHz with Allen Telescope Array before LOI-1 (X polarization)

description: This dataset contains a recording of X-band telemetry signals from the Chinese lunar sample retrieve mission Chang'e 5 done on 2020-11-28 before the LOI-1 (lunar orbit injection 1) manoeuvre that inserted the spacecraft into an elliptical lunar orbit. The recording was done at Allen Telescope Array using antenna 1h in dual XY (linear) polarization. The hardware configuration was as follows: antenna 1h was connected to RFCB LO d, which was tuned to a frequency of 8463.7 MHz and used an output IF of 512 MHz. A USRP N321 was connected to the IF output of the RFCB and digitized both polarizations. The USRP used an external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 1.92 Msps. A GNU Radio flowgraph was used to record IQ samples to disk. The GNU Radio flowgraph stored the IQ data for each channel as 16-bit integers in the GNU Radio metatata file format, with detached headers. After recording, the data was converted to SigMF format. This dataset contains the IQ data for the X polarization. The original metadata headers are also provided. The Y polarization can be found in this dataset.
DOI: 10.5281/zenodo.4317087
title: Chang'e 5 RF recording at 8463.7 MHz with Allen Telescope Array before LOI-1 (Y polarization)

description: This dataset contains a recording of X-band telemetry signals from the Chinese lunar sample retrieve mission Chang'e 5 done on 2020-11-28 before the LOI-1 (lunar orbit injection 1) manoeuvre that inserted the spacecraft into an elliptical lunar orbit. The recording was done at Allen Telescope Array using antenna 1h in dual XY (linear) polarization. The hardware configuration was as follows: antenna 1h was connected to RFCB LO d, which was tuned to a frequency of 8463.7 MHz and used an output IF of 512 MHz. A USRP N321 was connected to the IF output of the RFCB and digitized both polarizations. The USRP used an external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 1.92 Msps. A GNU Radio flowgraph was used to record IQ samples to disk. The GNU Radio flowgraph stored the IQ data for each channel as 16-bit integers in the GNU Radio metatata file format, with detached headers. After recording, the data was converted to SigMF format. This dataset contains the IQ data for the Y polarization. The original metadata headers are also provided. The X polarization can be found in this dataset.
DOI: 10.5281/zenodo.4317086
title: Chang'e 5 RF recording at 8463.7 MHz with Allen Telescope Array before LOI-1 (Y polarization)

description: This dataset contains a recording of X-band telemetry signals from the Chinese lunar sample retrieve mission Chang'e 5 done on 2020-11-28 before the LOI-1 (lunar orbit injection 1) manoeuvre that inserted the spacecraft into an elliptical lunar orbit. The recording was done at Allen Telescope Array using antenna 1h in dual XY (linear) polarization. The hardware configuration was as follows: antenna 1h was connected to RFCB LO d, which was tuned to a frequency of 8463.7 MHz and used an output IF of 512 MHz. A USRP N321 was connected to the IF output of the RFCB and digitized both polarizations. The USRP used an external 10 MHz reference and PPS coming from the observatory distribution system. The IQ sample rate of the USRP was 1.92 Msps. A GNU Radio flowgraph was used to record IQ samples to disk. The GNU Radio flowgraph stored the IQ data for each channel as 16-bit integers in the GNU Radio metatata file format, with detached headers. After recording, the data was converted to SigMF format. This dataset contains the IQ data for the Y polarization. The original metadata headers are also provided. The X polarization can be found in this dataset.
DOI: 10.21258/1591052
title: Inventario forestale nazionale svizzero - Tabella n. 899028
DOI: 10.6084/m9.figshare.12675410.v2
title: Three draft genomes of closely related low-light-adapted Prochlorococcus from enrichment cultures

description: The datasets provided here support the P1344, P1363 and P1361 Prochlorococcus genomes, available as whole genome assemblies at NCBI Genbank (https://www.ncbi.nlm.nih.gov) under accession numbers GCA_012933605.1, GCA_012933595.1, and GCA_012933585.1, respectively, and at JGI Integrated Microbial Genomes (https://img.jgi.doe.gov) with IMG genome IDs 2878082593, 2878079704, and 2878076891. Raw enrichment culture Illumina reads used to build these genomes are available at the NCBI Sequence Read Archive under accession numbers SRR11497176, SRR11497177, and SRR11497178.
DOI: 10.21258/1567551
title: Inventaire forestier national suisse - Tableau no 891786
DOI: 10.5281/zenodo.4313192
title: #energy_graph on renewable shares in electricity and CO2 emission factors in Australia and Germany

description: This is the little graph I used for my #energy_graph tweet, including the underlying data, in an Excel file. The tweet is here: https://twitter.com/WPSchill/status/1336633040676720640?s=20 I occasionally tweet stuff like this. Follow me, if you like ;) https://twitter.com/WPSchill
DOI: 10.21258/1550369
title: Inventaire forestier national suisse - Tableau no 898197
DOI: 10.21258/1556982
title: Inventaire forestier national suisse - Tableau no 899209
DOI: 10.21258/1602818
title: Inventario forestale nazionale svizzero - Tabella n. 899042
DOI: 10.21258/1596330
title: Inventario forestale nazionale svizzero - Tabella n. 896994
DOI: 10.21258/1531437
title: Inventaire forestier national suisse - Tableau no 891951
DOI: 10.5519/qd.tsseb2hd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4678589 records)
DOI: 10.21258/1573002
title: Inventaire forestier national suisse - Tableau no 891902
DOI: 10.21258/1588350
title: Inventario forestale nazionale svizzero - Tabella n. 891686
DOI: 10.25345/c5vb8r
title: MassIVE MSV000086578 - GNPS F5 Qingdao University 20201211
title: the first attempt for F5 extract from an endophyte, Qingdao University
DOI: 10.21258/1582967
title: Inventario forestale nazionale svizzero - Tabella n. 899640
DOI: 10.6084/m9.figshare.12979709.v4
title: Microbes in Challenger Deep sediments

description: File1: 16S miTags were extracted from clean forward metagenomic reads using rna_hmm3.py.File2: Gene annotation
DOI: 10.21258/1541671
title: Inventaire forestier national suisse - Tableau no 899526