cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22665028.v2
title: Supplementary Data for "No evidence of light inhibition on aerobic methanotrophs in coastal sediments using eDNA and eRNA"

description: Supplementary Data files for the study "No evidence of light inhibition on aerobic methanotrophs in coastal sediments using eDNA and eRNA". <em>Captions to supplementary data files</em> <strong>Supplementary Data 1</strong> The data file shows the taxonomy and read counts after processing the samples with the DADA 2 pipeline. Sample labels denote: S = Shallow site, D = Deep site, M = Medium light treatment, H = High light treatment. The numbers denote the sediment core number. A few samples had &lt; 5000 read counts and were excluded from the analysis (marked with an x in the sample name). <br> <strong>Supplementary Data 2</strong> Details of the total RNA-seq including sequencing facility identifiers, sample names, and number of sequences obtained before and after quality trimming etc. <br> <strong>Supplementary Data 3</strong> CH4 concentration, organic matter (%), and light intensity measured for each sample. CH4 concentrations and organic matter were measured in the top 0-1 cm sediment layer. Light intensity was measured at each location in the incubation chamber where each sample was placed. <br> <strong>Supplementary Data 4</strong> Sheet 1 shows the raw results from DADA 2 including all ASVs with read counts. Sheet 2 shows the relative abundance (%) of groups within known methanotrophic lineages. <br> <strong>Supplementary Data 5</strong> Sheet 1 shows reads counts from Kraken2+Bracken2 classifications of the extracted 16S rRNA sequences from the total RNA-seq data. Sheet 2 shows reads counts for groups within known methanotrophic lineages. <br> <strong>Supplementary Data 6</strong> Sheet 1 and sheet 2 shows the DIAMOND+MEGAN KEGG classifications of the non-rRNA reads, sheet 1 shows the number of annotated counts per KEGG KO, while sheet 2 shows the same data but normalized as counts per million reads (CPM). Sheet 3 shows the results of the separation of amoAB-pmoAB hits by extracting these reads from MEGAN, followed by UniProtKB classification using BLASTX. The sheet shows the results in counts. Sheet 4 shows the sum of CPM values for KEGG KOs within the KEGG category Photosynthesis.
DOI: 10.6084/m9.figshare.22665028.v1
title: Supplementary Data for "No evidence of light inhibition on aerobic methanotrophs in coastal sediments using eDNA and eRNA"

description: Supplementary Data files for the study "No evidence of light inhibition on aerobic methanotrophs in coastal sediments using eDNA and eRNA". <em>Captions to supplementary data files</em> <strong>Supplementary Data 1</strong> The data file shows the taxonomy and read counts after processing the samples with the DADA 2 pipeline. Sample labels denote: S = Shallow site, D = Deep site, M = Medium light treatment, H = High light treatment. The numbers denote the sediment core number. A few samples had &lt; 5000 read counts and were excluded from the analysis (marked with an x in the sample name). <br> <strong>Supplementary Data 2</strong> Details of the total RNA-seq including sequencing facility identifiers, sample names, and number of sequences obtained before and after quality trimming etc. <br> <strong>Supplementary Data 3</strong> CH4 concentration, organic matter (%), and light intensity measured for each sample. CH4 concentrations and organic matter were measured in the top 0-1 cm sediment layer. Light intensity was measured at each location in the incubation chamber where each sample was placed. <br> <strong>Supplementary Data 4</strong> Sheet 1 shows the raw results from DADA 2 including all ASVs with read counts. Sheet 2 shows the relative abundance (%) of groups within known methanotrophic lineages. <br> <strong>Supplementary Data 5</strong> Sheet 1 shows reads counts from Kraken2+Bracken2 classifications of the extracted 16S rRNA sequences from the total RNA-seq data. Sheet 2 shows reads counts for groups within known methanotrophic lineages. <br> <strong>Supplementary Data 6</strong> Sheet 1 and sheet 2 shows the DIAMOND+MEGAN KEGG classifications of the non-rRNA reads, sheet 1 shows the number of annotated counts per KEGG KO, while sheet 2 shows the same data but normalized as counts per million reads (CPM). Sheet 3 shows the results of the separation of amoAB-pmoAB hits by extracting these reads from MEGAN, followed by UniProtKB classification using BLASTX. The sheet shows the results in counts. Sheet 4 shows the sum of CPM values for KEGG KOs within the KEGG category Photosynthesis.
DOI: 10.6084/m9.figshare.21621240
title: Supplementary Data for "Long-term pollution does not significantly inhibit denitrification and DNRA by adapted benthic microbial communities"

description: <strong>Title:</strong> Long-term pollution does not significantly inhibit denitrification and DNRA by adapted benthic microbial communities <br> <strong>Supplementary Information</strong> <em>Captions to supplementary data files</em> <br> Data S1 The data file shows sequence facility ids, sample names, number of read-pairs yielded after sequencing, quality trimming and merging details, number of 16S rRNA gene sequences classified against SILVA, 18S rRNA gene sequences against NCBI NT, and meagenome sequences classified with DIAMOND against the MEGAN supplied KEGG database. <br> Data S2 The data file shows the denitrification and DNRA results from the IPT experiment. <br> Data S3 The data file shows: sheet 1) KEGG classifications in the nitrogen metabolism pathway, and sheet 2) all the KEGG classifications for all pathways detected in th samples. The table shows normalized reads counts derived from the MEGAN software normalized read counts function (sub-sample to lowest sample size). <br> Data S4 The data file shows MEGAN taxonomy linked to KEGG classifications (metal related genes, Dioxin pathway, and Nitrogen metabolism pathways). The results are based on linking taxonomy and KEGG classifications having the same read name. The table shows normalized counts as counts per million (CPM) values. <br> Data S5 The data file shows the edgeR results (only results with FDR &lt; 0.05 are reported). The table shows log fold-change values based on pairwise comparisons between stations. <br> Data S6 The data file shows 16S rRNA gene sequences (sheet 1) extracted from the metagenome (using SortMeRNA) and classified against the SILVA database. The table shows raw read counts.
DOI: 10.6084/m9.figshare.21621240.v4
title: Supplementary Data for "Long-term pollution does not significantly inhibit denitrification and DNRA by adapted benthic microbial communities"

description: <strong>Title:</strong> Long-term pollution does not significantly inhibit denitrification and DNRA by adapted benthic microbial communities <br> <strong>Supplementary Information</strong> <em>Captions to supplementary data files</em> <br> Data S1 The data file shows sequence facility ids, sample names, number of read-pairs yielded after sequencing, quality trimming and merging details, number of 16S rRNA gene sequences classified against SILVA, 18S rRNA gene sequences against NCBI NT, and meagenome sequences classified with DIAMOND against the MEGAN supplied KEGG database. <br> Data S2 The data file shows the denitrification and DNRA results from the IPT experiment. <br> Data S3 The data file shows: sheet 1) KEGG classifications in the nitrogen metabolism pathway, and sheet 2) all the KEGG classifications for all pathways detected in th samples. The table shows normalized reads counts derived from the MEGAN software normalized read counts function (sub-sample to lowest sample size). <br> Data S4 The data file shows MEGAN taxonomy linked to KEGG classifications (metal related genes, Dioxin pathway, and Nitrogen metabolism pathways). The results are based on linking taxonomy and KEGG classifications having the same read name. The table shows normalized counts as counts per million (CPM) values. <br> Data S5 The data file shows the edgeR results (only results with FDR &lt; 0.05 are reported). The table shows log fold-change values based on pairwise comparisons between stations. <br> Data S6 The data file shows 16S rRNA gene sequences (sheet 1) extracted from the metagenome (using SortMeRNA) and classified against the SILVA database. The table shows raw read counts.
DOI: 10.6084/m9.figshare.21621240.v3
title: Supplementary Data for "Long-term pollution does not significantly inhibit denitrification and DNRA by adapted benthic microbial communities"

description: <strong>Title:</strong> Long-term pollution does not significantly inhibit denitrification and DNRA by adapted benthic microbial communities <br> <strong>Supplementary Information</strong> <em>Captions to supplementary data files</em> <br> Data S1 The data file shows sequence facility ids, sample names, number of read-pairs yielded after sequencing, quality trimming and merging details, number of 16S rRNA gene sequences classified against SILVA, 18S rRNA gene sequences against NCBI NT, and meagenome sequences classified with DIAMOND against the MEGAN supplied KEGG database. <br> Data S2 The data file shows the denitrification and DNRA results from the IPT experiment. <br> Data S3 The data file shows: sheet 1) KEGG classifications in the nitrogen metabolism pathway, and sheet 2) all the KEGG classifications for all pathways detected in th samples. The table shows normalized reads counts derived from the MEGAN software normalized read counts function (sub-sample to lowest sample size). <br> Data S4 The data file shows MEGAN taxonomy linked to KEGG classifications (metal related genes, Dioxin pathway, and Nitrogen metabolism pathways). The results are based on linking taxonomy and KEGG classifications having the same read name. The table shows normalized counts as counts per million (CPM) values. <br> Data S5 The data file shows the edgeR results (only results with FDR &lt; 0.05 are reported). The table shows log fold-change values based on pairwise comparisons between stations. <br> Data S6 The data file shows 16S rRNA gene sequences (sheet 1) extracted from the metagenome (using SortMeRNA) and classified against the SILVA database. The table shows raw read counts.
DOI: 10.34740/kaggle/dsv/5467964
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.22664998
title: The vertical distributions of soil microbial biomass and soil organic carbon across terrestrial ecosystems

description: The data were used for the global synthesis on the topic of the vertical distribution of microbial biomass carbon (MBC). The data were collected from 82 articles, covering 163 observations. <br> To quantify the vertical distribution patterns of MBC and SOC, we used an exponential decay function to fit MBC and SOC with soil depth: log10(Y)= -kd + m where Y is the MBC or SOC density (g cm-3) at depth d (midpoints of depth intervals, cm), k characterizes the relative decrease rate of MBC or SOC with depth, the greater k, the faster MBC or SOC decreases along the soil profile. m indicates the power of ten of MBC content at the soil surface. We used kMBC and kSOC to represent the rates of decrease in MBC and SOC, respectively. We square-root transformed kMBC and kSOC to make these two variables comparable and to facilitate visualization of the relationship.
DOI: 10.6084/m9.figshare.22664998.v1
title: The vertical distributions of soil microbial biomass and soil organic carbon across terrestrial ecosystems

description: The data were used for the global synthesis on the topic of the vertical distribution of microbial biomass carbon (MBC). The data were collected from 82 articles, covering 163 observations. <br> To quantify the vertical distribution patterns of MBC and SOC, we used an exponential decay function to fit MBC and SOC with soil depth: log10(Y)= -kd + m where Y is the MBC or SOC density (g cm-3) at depth d (midpoints of depth intervals, cm), k characterizes the relative decrease rate of MBC or SOC with depth, the greater k, the faster MBC or SOC decreases along the soil profile. m indicates the power of ten of MBC content at the soil surface. We used kMBC and kSOC to represent the rates of decrease in MBC and SOC, respectively. We square-root transformed kMBC and kSOC to make these two variables comparable and to facilitate visualization of the relationship.
DOI: 10.5281/zenodo.7849162
title: 46 - Revista Digital do Laboratório de Artes Visuais

description: Planilha utilizada na tese intitulada "<strong>Avaliação de periódicos na Área da Educação: índice de citação e características citacionais", </strong>a qual foi desenvolvida no Programa de Pós-Graduação da Pontifícia Universidade Católica do Paraná, com a orientação da professora Doutora Alboni Marisa Dudeque Pianovski Vieira. A tese foi defendida em 24 de fevereiro de 2023. Esse material pertence a um conjunto de 60 periódicos analisados e uma planilha matriz com todos os resultados quantitativos e gráficos. A descrição dessa planilha é a seguinte: Os dados foram organizados, coletados e descritos em planilhas do Microsoft Excel. A busca foi feita para catalogar as características de citação de periódicos da Área da Educação pertencentes ao Fórum de Editores de Periódicos da Área de Educação da Região Sul (FEPAE-Sul). Foi delimitado para 15 artigos (quando havia) para casa primeira publicação do periódico ou, no caso do fluxo contínuo, os 15 primeiros artigos foram usados. A organização da planilha consiste no ano de publicação, número de organização dos artigos utilizados, título do artigo analisado, ano da referência, se a referência é nacional (N) ou internacional (I) e a descrição do tipo de referência. Em cada planilha possui análise dos anos de 2019, 2020 e 2021 com coletas e descrição.
DOI: 10.6073/pasta/c1322bd2fb3e6eac0749a83033d24ab6
title: WSC - Temperature and relative humidity data from 150 locations in and around Madison, Wisconsin from 2012 - 2020

description: To study the urban heat island and other local climatic processes in Madison, Wisconsin, in March 2012, 135 HOBO U23 Pro v2 temperature/relative humidity sensors in RS1 solar shields (Onset Computing) were attached to streetlight and utility poles in and around Madison, Wisconsin. Additional locations were added in 2012 and 2013 for a total of 150 locations. The sensors were installed at a height of 3.5 meters, and they automatically record instantaneous temperature and relative humidity every 15 minutes. This dataset includes all temperature/humidity measurements and a separate file with the coordinates of each measurement location.
DOI: 10.58167/gfp8kz
title: AK0001 / A-K -kokoelma, Kansanperinteen arkisto

description: Purola, Tilta, o.s. Purola Purola o.s. Purola, Tilta,s. 1870, emäntä, laulu. 08 Laulu "Ah, aikoja nuoruuteni". 09 Laulu "Kullalleni minä laulelen". 10 Laulu "Suru on tullut syrämeen". 11 Laulu "Mäen rinteelle mökkini laitan". 12 Laulu "Minä käteni lämpimän tarjosin". 14 Laulu "Yksi neito nuori". 40 Viisulaulu "Yksi kauppias Intiasta". Torppa, Adrian Torppa, Adrian,s. 26.9.1895 Vetelissä, viulu. 41 Hääpolska. 42 Hääpolska. 43 Retu-Matin valssi. 44 Purpurin ympärysvalssi. 45 Purpurin ottovalssi. 46 Purpurin loppuvalssi. 47 Purpurin ristaus. 48 Katrilli. 49 Purpurin alotus. 50 Purpurin lopetus. 51 Kättelymarssi. Ojala, Fridjoff (Friiti),s. 1892 k. 1951 Kaustisella, viulu 94 Hääpolska. Nummela, Eemeli, ent. Åby Nurmela ent. Åby, Eemeli (Henrik Emil) Matinpoika,s. 23.8.1893 Evijärvellä, viulu 52 Polska. 53 Masurkka. 54 Polska. 55 Polska. 56 Masurkka. 57 Polska. 58 Polska. Kauppinen, Onni Kauppinen, Onni Mainio,Sefaniaanpoika s. 5.2.1907 Halsualla, laulu, viulu 03 Laulu "Halsuan pieni kappeli". 28 Häiden alotusmarssi. 29 Siliavalssi. 30 Talonpojan sotiisi. 31 Kaksi polkkaa. 32 Purpurin ristausmarssi. 33 Hääpolska "Iso lintu, merikotka". 34 Hääpolska. 35 Häämarssi. 36 Hääpolska. 37 Polkka. 38 Hääpolska 39 Purpurin siliavalssi. Karvonen, Viljo Karvonen, Viljo,s. 1906, autonkuljettaja, kantele. 19 Haka-Jaaskan polkka. 20 Siliavalssi. 21 Sotiisi. 22 Keisarin polkka. 27 Sotiisi. Hepokangas, Juho Hepokangas, Juho,s. 1889 Kortesjärvellä, kaksirivinen harmonikka. 59 Polska. 60 Polska. 61 Polska. 62 Masurkka. 63 Siliavalssi. 64 Polska. 65 Polska. 66 Polska. Simoinen, Selma Simoinen, Selma,laulu 04 Laulu "Ne on poveni kukkia vainen". 05 Karjankutsuntaa. 06 Laulu "Ne on poveni kukkia vainen". Salmela, Yrjö Salmela, Yrjö,s. 1891, ent. konstaapeli, maanviljelijä, viulu. 15 Siliavalssi (kanteleella säestää Viljo Karvonen). 16 Markun Joonaan valssi. 17 Leku-Santun polkka eli Soinin polkka. 18 Piikkipolkka. 23 Hää(raha)polska. 24 Koivukankaan Pentin marssisotiisi. 25 Naputuspolkka. 26 Sotiisi. Simoinen, Sofia, o.s. Purola Simoinen, Sofia o.s. Purola,s. 1859, emäntä, laulu. 01Kreeta Haapasalon sepittämä laulu "Minun leipäni pieniin on pantu", oppinut laulun suoraan ystävältään Kreetalta. 02 Laulu "Mitäs minä vanha ämmä", oppinut Kreeta Haapasalolta. 03 Lehmikarjan kutsuntaa. 13 Mustalaislaulu "Hai, ui". Hautala, Iivari Hautala, Iivari,s. 1897 Evijärvellä, maanviljelijä, viulu. 67 Härmäläinen häämarssi. 68 Hepo-Jussin polska. 69 Mamperin Manun valssi. 70 Hepo-Jussin polska. 71 Varpusen polska. 72 Kultaisen vasikan polska. 73 Taatipolska. 74 Polkka. 75 Sotiisi. 76 Masurkka. 77 Väinämöisen polkka. 78 Rintalan Heikin valssi. 79 Lapatossun polkka. 80 Pirun nukutuspolkka. 81 Polska. 82 Polska. 83 Polska. 84 Rahapolska. 85 "Rosentti" eli "Pitkin puuta", tanssisävelmä. 86 Masurkka. 87 Tampereen likkojen pyykkipolkka. 88 Taatipolska. 89 Polska. 90 Romsin Aapon polska. 91 Polska. 92 Sotiisi. 93 Polkka.
DOI: 10.5281/zenodo.7849222
title: 47 - Revista Educação e Linguagens

description: Planilha utilizada na tese intitulada "<strong>Avaliação de periódicos na Área da Educação: índice de citação e características citacionais", </strong>a qual foi desenvolvida no Programa de Pós-Graduação da Pontifícia Universidade Católica do Paraná, com a orientação da professora Doutora Alboni Marisa Dudeque Pianovski Vieira. A tese foi defendida em 24 de fevereiro de 2023. Esse material pertence a um conjunto de 60 periódicos analisados e uma planilha matriz com todos os resultados quantitativos e gráficos. A descrição dessa planilha é a seguinte: Os dados foram organizados, coletados e descritos em planilhas do Microsoft Excel. A busca foi feita para catalogar as características de citação de periódicos da Área da Educação pertencentes ao Fórum de Editores de Periódicos da Área de Educação da Região Sul (FEPAE-Sul). Foi delimitado para 15 artigos (quando havia) para casa primeira publicação do periódico ou, no caso do fluxo contínuo, os 15 primeiros artigos foram usados. A organização da planilha consiste no ano de publicação, número de organização dos artigos utilizados, título do artigo analisado, ano da referência, se a referência é nacional (N) ou internacional (I) e a descrição do tipo de referência. Em cada planilha possui análise dos anos de 2019, 2020 e 2021 com coletas e descrição.
DOI: 10.6073/pasta/ea69ebede543bed70ecd316a5a0a104f
title: PEC01 Elemental chemistry of plant tissue collected for the Konza LTER aboveground plant biomass on Konza Prairie core watersheds

description: Dataset contains elemental chemistry (N, C, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, S, Si, Ti, V, and Zn) of dried and ground, end-of-season, above-ground live tissue from grasses, forbs, and woody plants collected on Tully soils in the watersheds 001d, 004b, and 020b. N and C are provided as percentages; all other elements are provided as parts per million (ppm). Within plant growth type (grasses, forbs, and woody) and year, elemental concentrations were measured on one pooled (2g) sample containing four (0.5g) subsamples of ground and dried plant tissue (subsamples included from recent years were named TA2, TB2, TC2, and TD2; subsamples included from older years were named: TA2, TA4, TB2, TB4). For more information on plant sampling see the description of the Konza LTER PAB01 aboveground plant biomass dataset (Blair & Nippert). Elemental chemistry was analyzed using combustion analysis for percent N and using hot plate digestion and inductively coupled plasma atomic emission spectroscopy (ICP-AES) for concentrations of metals (ppm) at the Cornell Nutrient Analysis Laboratory (https://cnal.cals.cornell.edu/).
DOI: 10.6073/pasta/cd913a57d7f138b832b7f90b53ae21be
title: Biomass Inventories at Harvard Forest EMS Tower since 1993

description: In 1993, we installed 40 circular, 10 m radius biometric plots in the footprint of the EMS tower on Prospect Hill. We randomly placed the plots within 100 m increments along ten 500 m transects that extend from the tower in the northwest and southwest directions. In 2001, we removed three plots (G3, H3, H4) from the datasets and ceased measurements there due to their inundation by a beaver pond. In 1999, we installed 6 additional circular, 10 m radius biometric plots on the Simes Lot, adjacent to Prospect Hill to study the effects of a selective harvest that occurred there in the winter of 2000-01. In the summer of 2001, we expanded the harvested plots in size to 15 m radius and ceased measurements at one plot (X4) because it was unaffected by the harvest. The harvest also affected three of the original tower plots (A4, A5, B5), which were expanded in size as a part of the harvest plot group. Consequently, there are 34 tower plots and 8 harvest plots. We have taken the following ecological measurements at each site: tree growth, woody debris, litter, leaf area increment (LAI), leaf chemistry, and soil respiration and moisture.
DOI: 10.6073/pasta/6cff694ce8fecb3e589e415b762b091e
title: PEC01 Elemental chemistry of plant tissue collected for the Konza LTER aboveground plant biomass on Konza Prairie core watersheds

description: Dataset contains elemental chemistry (N, C, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, S, Si, Ti, V, and Zn) of dried and ground, end-of-season, above-ground live tissue from grasses, forbs, and woody plants collected on Tully soils in the watersheds 001d, 004b, and 020b. N and C are provided as percentages; all other elements are provided as parts per million (ppm). Within plant growth type (grasses, forbs, and woody) and year, elemental concentrations were measured on one pooled (2g) sample containing four (0.5g) subsamples of ground and dried plant tissue (subsamples included from recent years were named TA2, TB2, TC2, and TD2; subsamples included from older years were named: TA2, TA4, TB2, TB4). For more information on plant sampling see the description of the Konza LTER PAB01 aboveground plant biomass dataset (Blair & Nippert). Elemental chemistry was analyzed using combustion analysis for percent N and using hot plate digestion and inductively coupled plasma atomic emission spectroscopy (ICP-AES) for concentrations of metals (ppm) at the Cornell Nutrient Analysis Laboratory (https://cnal.cals.cornell.edu/).