cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/pviltausenddno-uhfffaoysa-n/chmo0000563
title: fast-atom bombardment mass spectrometry (FABMS) ()
title: InChI=1S/C16H16IN/c17-15-9-14-8-6-12-3-1-11(2-4-12)5-7-13(15)10-16(14)18/h1-4,9-10H,5-8,18H2

description: dataset for fast-atom bombardment mass spectrometry (FABMS) MS (FAB, 3-NBA, %) m/z = 350 (24) [M+H]+, 349 (71) [M]+, 345 (71) [M–C8H8]+.HRMS (FAB, [M]+, C16H16N127I) calcd.: 349.0332; found: 349.0324.
DOI: 10.14272/pviltausenddno-uhfffaoysa-n.1
title: C16H16IN
title: InChI=1S/C16H16IN/c17-15-9-14-8-6-12-3-1-11(2-4-12)5-7-13(15)10-16(14)18/h1-4,9-10H,5-8,18H2

description: This dataset contains experimental data from a sample (InChI=1S/C16H16IN/c17-15-9-14-8-6-12-3-1-11(2-4-12)5-7-13(15)10-16(14)18/h1-4,9-10H,5-8,18H2). The datasets from the following experiments are available: infrared absorption spectroscopy (IR) 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) fast-atom bombardment mass spectrometry (FABMS)
DOI: 10.14272/pviltausenddno-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C16H16IN/c17-15-9-14-8-6-12-3-1-11(2-4-12)5-7-13(15)10-16(14)18/h1-4,9-10H,5-8,18H2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (126 MHz, CDCl3, ppm) δ = 145.4 (Cq, CAr), 144.4 (+, CH, CAr), 143.7 (Cq, CAr), 138.8 (Cq, CAr), 138.7 (Cq, CAr), 133.0 (+, CH, CAr), 131.7 (+, CH, CAr), 130.1 (+, CH, CAr), 127.9 (+, CH, CAr), 126.6 (Cq, CAr), 123.2 (+, CH, CAr), 89.4 (Cq, CAr), 39.1 (–, CH2), 33.3 (–, CH2), 32.8 (–, CH2), 32.0 (–, CH2).
DOI: 10.14272/pviltausenddno-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C16H16IN/c17-15-9-14-8-6-12-3-1-11(2-4-12)5-7-13(15)10-16(14)18/h1-4,9-10H,5-8,18H2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (500 MHz, CDCl3, ppm) δ = 7.19 (ddd, J = 17.1 Hz, 7.8 Hz, 2.0 Hz, 2H, HAr), 6.58 (s, 1H, HAr), 6.43 (ddd, J = 18.2 Hz, 7.8 Hz, 2.0 Hz, 2H, HAr), 5.42 (s, 1H, HAr), 3.51 (s, 2H, NH2), 3.27–3.17 (m, 2H, HPc), 3.16–2.96 (m, 4H, HPc), 2.75–2.66 (m, 1H, HPc), 2.56 (ddd, J = 13.7 Hz, 10.0 Hz, 7.3 Hz, 1H, HPc).
DOI: 10.25721/wtr8-n207
title: Marie, ma petite Marie
DOI: 10.6084/m9.figshare.16912168
title: Supplementary Material for: Macrophage-Biomimetic Nanoparticles Ameliorate Ulcerative Colitis through Reducing Inflammatory Factors Expression

description: <b><i>Background and Aims:</i></b> Inflammatory mediator S100A9 is dramatically elevated in ulcerative colitis and correlates with disease severity. S100A9 is a potential molecule to target for the treatment of colitis, but to date, there is no effective targeting method. The aim of this study was to develop a safe and effective nano-delivery system targeting S100A9 and to evaluate its therapeutic efficacy in ulcerative colitis mouse model. <b><i>Methods:</i></b> We designed an oral nano-delivery system using poly (lactic acid-glycolic acid) (PLGA)-loaded S100A9 inhibitor tasquinimod to synthesize PLGA-TAS nanoparticles. TLR4-overexpressing macrophage membranes (MMs) were used to wrap the nanoparticles to make MM-PLGA-TAS, which allowed the nanoparticles to acquire the ability to specifically enrich the colitis region. <b><i>Results:</i></b> MM-PLGA-TAS was endocytosed by inflammatory phenotype RAW264.7 cells in vitro and can efficiently enrich in inflamed mouse colitis tissue in vivo. A chemically induced ulcerative colitis mouse model was used to evaluate the therapeutic effect of oral MM-PLGA-TAS. MM-PLGA-TAS significantly alleviated the symptoms of ulcerative colitis, and mechanically, MM-PLGA-TAS achieved immunomodulatory and suppressive effects by reducing S100a9 and other cytokines in the colitis region. <b><i>Conclusion:</i></b> We describe a convenient, orally targeted colitis drug delivery system that cures the disease in ulcerative colitis mice. This system substantially increases drug accumulation in inflamed colonic tissue, reduces the risk of systemic exposure, and is a promising therapeutic approach against ulcerative colitis.
DOI: 10.6084/m9.figshare.16912168.v1
title: Supplementary Material for: Macrophage-Biomimetic Nanoparticles Ameliorate Ulcerative Colitis through Reducing Inflammatory Factors Expression

description: <b><i>Background and Aims:</i></b> Inflammatory mediator S100A9 is dramatically elevated in ulcerative colitis and correlates with disease severity. S100A9 is a potential molecule to target for the treatment of colitis, but to date, there is no effective targeting method. The aim of this study was to develop a safe and effective nano-delivery system targeting S100A9 and to evaluate its therapeutic efficacy in ulcerative colitis mouse model. <b><i>Methods:</i></b> We designed an oral nano-delivery system using poly (lactic acid-glycolic acid) (PLGA)-loaded S100A9 inhibitor tasquinimod to synthesize PLGA-TAS nanoparticles. TLR4-overexpressing macrophage membranes (MMs) were used to wrap the nanoparticles to make MM-PLGA-TAS, which allowed the nanoparticles to acquire the ability to specifically enrich the colitis region. <b><i>Results:</i></b> MM-PLGA-TAS was endocytosed by inflammatory phenotype RAW264.7 cells in vitro and can efficiently enrich in inflamed mouse colitis tissue in vivo. A chemically induced ulcerative colitis mouse model was used to evaluate the therapeutic effect of oral MM-PLGA-TAS. MM-PLGA-TAS significantly alleviated the symptoms of ulcerative colitis, and mechanically, MM-PLGA-TAS achieved immunomodulatory and suppressive effects by reducing S100a9 and other cytokines in the colitis region. <b><i>Conclusion:</i></b> We describe a convenient, orally targeted colitis drug delivery system that cures the disease in ulcerative colitis mice. This system substantially increases drug accumulation in inflamed colonic tissue, reduces the risk of systemic exposure, and is a promising therapeutic approach against ulcerative colitis.
DOI: 10.25721/df8q-s129
title: Information sur les chansons
DOI: 10.21227/ceg1-cq78
title: Fake News on Healthcare

description: The Internet is a vast repository of useful knowledge, but it has been contaminated by the spread of false information. Relying on misinformation can be disastrous. According to a World Health Organization survey, about 6,000 individuals were hospitalised throughout the world as a result of fake news on COVID-19 in the first three months of 2020. During this time span, at least 800 individuals have died as a result of COVID-19 misrepresentation. Fake news datasets in healthcare, on the other hand, are rare, small, and extremely unbalanced in academia. This paper presents a curated dataset on fake news in the healthcare domain.
DOI: 10.21227/s1ff-xt16
title: Fake News on Healthcare

description: The Internet is a vast repository of useful knowledge, but it has been contaminated by the spread of false information. Relying on misinformation can be disastrous. According to a World Health Organization survey, about 6,000 individuals were hospitalised throughout the world as a result of fake news on COVID-19 in the first three months of 2020. During this time span, at least 800 individuals have died as a result of COVID-19 misrepresentation. Fake news datasets in healthcare, on the other hand, are rare, small, and extremely unbalanced in academia. This paper presents a curated dataset on fake news in the healthcare domain.
DOI: 10.21227/k0qs-bz38
title: Fake News on Healthcare

description: The Internet is a vast repository of useful knowledge, but it has been contaminated by the spread of false information. Relying on misinformation can be disastrous. According to a World Health Organization survey, about 6,000 individuals were hospitalised throughout the world as a result of fake news on COVID-19 in the first three months of 2020. During this time span, at least 800 individuals have died as a result of COVID-19 misrepresentation. Fake news datasets in healthcare, on the other hand, are rare, small, and extremely unbalanced in academia. This paper presents a curated dataset on fake news in the healthcare domain.
DOI: 10.6084/m9.figshare.16912174
title: Supplementary Material for: Perception of a New Prolonged-Release Buprenorphine Formulation in Patients with Opioid Use Disorder: The PREDEPO Study

description: <b><i>Aim:</i></b> The aim of the study was to assess the acceptance of patients with opioid use disorder (OUD) to switching their opioid dependence treatment (ODT) for a prolonged-release buprenorphine (PRB) injection according to their prior ODT (buprenorphine/naloxone [B/N] or methadone). <b><i>Methods:</i></b> This was an observational, retrospective/cross-sectional, multicentre study of adult patients diagnosed with OUD on ODT. Data collected from diaries were analysed to know their interest and opinion on PRB. Questions with fixed response options were included, and several Likert scales were used. <b><i>Results:</i></b> A total of 98 patients were enrolled (B/N: 50.0%, methadone: 50.0%). The mean age was 46.9 ± 8.43 years and 79.6% were males. PRB was similarly perceived by both groups in most variables analysed, receiving a mean score of 7.2/10 (B/N: 7.4, methadone: 7.0; <i>p</i> = 0.520), and approximately 65% of patients said they were willing to switch to PRB (B/N: 63.3%, methadone: 65.3%; <i>p</i> = 0.833). Of these, a higher percentage in the B/N group considered that switching would be easy/very easy (B/N: 90.3%, methadone: 46.9%; <i>p</i> &lt; 0.001) and that they would start PRB when available (B/N: 64.5%, methadone: 34.3%; <i>p</i> = 0.005). More than 90% would prefer the monthly injection (B/N: 93.6%, methadone: 100%; <i>p</i> = 0.514). One-third of patients in both groups were unsure/would not switch their ODT to PRB (B/N: 36.7%, methadone: 34.7%; <i>p</i> = 0.833). The main reason was administration by injection. <b><i>Conclusion:</i></b> Two-thirds of patients would switch their treatment for PRB, and most patients on B/N considered that switching would be easy. PRB could be a suitable alternative for OUD management.
DOI: 10.25721/3a2r-3233
title: Information sur les bruiteurs
DOI: 10.25721/bjf8-v933
title: En revenant des noces
DOI: 10.25721/9m8n-3e81
title: Jean gros Jean marie sa fille
DOI: 10.5281/zenodo.5635678
title: Property listings for sale Madrid

description: This dataset contains the property listings in Madrid as of October 31st, 2021 From each listing, we got the following fields: <strong>ID</strong>: the listing ID<br> <strong>Listing</strong>: It shows the listing it usually contains the type of property and the street. <br> <strong>Location</strong>: the place of the listing (city, town or neighborhood)<br> <strong>price</strong>: property price<br> <strong>old_price</strong>: previous price before the discount.<br> <strong>discount</strong>: percentage of discount<br> <strong>meters</strong>: size of the property in meters<br> <strong>sq_meter_price</strong>: price of each square meter.<br> <strong>rooms</strong>: number of rooms of the property<br> <strong>floor</strong>: the floor of the property<br> <strong>garage</strong>: It shows if the property has a garage<br> <strong>description</strong>: This field is the description of the property in Spanish. All the data has been scraped from idealista containing the listings as of October 31st, 2021 in Madrid. The process took about 30 hours. Spanish: El dataset contiene los inmuebles listados para su venta en la Comunidad de Madrid a fecha 31 de octubre de 2021. De cada una de las ofertas de venta se han recogido los siguientes campos: <strong>ID</strong>: Es el identificador del anuncio<br> <strong>Listing</strong>: Contiene el título del anuncio que normalmente se compone del tipo de vivienda y de la calle en la que se encuentra. <br> <strong>Location</strong>: es la ubicación del inmueble que puede ser la ciudad, el pueblo o el barrio. <br> <strong>price</strong>: precio de venta del inmueble.<br> <strong>old_price</strong>: campo solo disponible en los inmuebles rebajados e indica el precio anterior del inmueble<br> <strong>discount</strong>: porcentaje de descuento del precio.<br> <strong>meters</strong>: metros cuadrados de la vivienda<br> <strong>sq_meter_price</strong>: Precio del metro cuadrado del inmueble<br> <strong>rooms</strong>: Número de habitaciones del inmueble<br> <strong>floor</strong>: Planta del inmueble<br> <strong>garage</strong>: campo que indica si el inmueble dispone de garaje, cuando su valor es nulo indica que no aparece reflejado que disponga de garaje.<br> <strong>description</strong>: Es un campo abierto donde aparece la descripción del inmueble en venta. Los datos han sido extraídos del portal inmobiliario idealista y el dataset contiene los inmuebles listados a fecha 31/10/2021 en la Comunidad de Madrid. Ha sido obtenido mediante un web scraper desarrollado en python en un proceso que ha durado unas 30 horas.
DOI: 10.5281/zenodo.5635677
title: Property listings for sale Madrid

description: This dataset contains the property listings in Madrid as of October 31st, 2021 From each listing, we got the following fields: <strong>ID</strong>: the listing ID<br> <strong>Listing</strong>: It shows the listing it usually contains the type of property and the street. <br> <strong>Location</strong>: the place of the listing (city, town or neighborhood)<br> <strong>price</strong>: property price<br> <strong>old_price</strong>: previous price before the discount.<br> <strong>discount</strong>: percentage of discount<br> <strong>meters</strong>: size of the property in meters<br> <strong>sq_meter_price</strong>: price of each square meter.<br> <strong>rooms</strong>: number of rooms of the property<br> <strong>floor</strong>: the floor of the property<br> <strong>garage</strong>: It shows if the property has a garage<br> <strong>description</strong>: This field is the description of the property in Spanish. All the data has been scraped from idealista containing the listings as of October 31st, 2021 in Madrid. The process took about 30 hours. Spanish: El dataset contiene los inmuebles listados para su venta en la Comunidad de Madrid a fecha 31 de octubre de 2021. De cada una de las ofertas de venta se han recogido los siguientes campos: <strong>ID</strong>: Es el identificador del anuncio<br> <strong>Listing</strong>: Contiene el título del anuncio que normalmente se compone del tipo de vivienda y de la calle en la que se encuentra. <br> <strong>Location</strong>: es la ubicación del inmueble que puede ser la ciudad, el pueblo o el barrio. <br> <strong>price</strong>: precio de venta del inmueble.<br> <strong>old_price</strong>: campo solo disponible en los inmuebles rebajados e indica el precio anterior del inmueble<br> <strong>discount</strong>: porcentaje de descuento del precio.<br> <strong>meters</strong>: metros cuadrados de la vivienda<br> <strong>sq_meter_price</strong>: Precio del metro cuadrado del inmueble<br> <strong>rooms</strong>: Número de habitaciones del inmueble<br> <strong>floor</strong>: Planta del inmueble<br> <strong>garage</strong>: campo que indica si el inmueble dispone de garaje, cuando su valor es nulo indica que no aparece reflejado que disponga de garaje.<br> <strong>description</strong>: Es un campo abierto donde aparece la descripción del inmueble en venta. Los datos han sido extraídos del portal inmobiliario idealista y el dataset contiene los inmuebles listados a fecha 31/10/2021 en la Comunidad de Madrid. Ha sido obtenido mediante un web scraper desarrollado en python en un proceso que ha durado unas 30 horas.
DOI: 10.25721/yz31-ax31
title: La belle rose
DOI: 10.25721/zznn-r198
title: Trei hurrah pour Guernesey
DOI: 10.5281/zenodo.5635652
title: Jigsaw Teapot - Synthesis of 3D jigsaw puzzles over freeform 2-manifolds.

description: This 3D puzzle has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (https://www.cs.technion.ac.il/~irit/). This specific Utah teapot puzzle model has been created using function composition of puzzle tiles over the shell geometry of a teapot. Puzzle model is provided, in parts, in OBJ file format.
description: This research is also funded in parts by the ISRAEL SCIENCE FOUNDATION, Israel (Grant No. 597/18).
DOI: 10.5281/zenodo.5635651
title: Jigsaw Teapot - Synthesis of 3D jigsaw puzzles over freeform 2-manifolds.

description: This 3D puzzle has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (https://www.cs.technion.ac.il/~irit/). This specific Utah teapot puzzle model has been created using function composition of puzzle tiles over the shell geometry of a teapot. Puzzle model is provided, in parts, in OBJ file format.
description: This research is also funded in parts by the ISRAEL SCIENCE FOUNDATION, Israel (Grant No. 597/18).
DOI: 10.25721/1h1w-3072
title: A la claire fontaine
DOI: 10.5281/zenodo.5635643
title: Jigsaw Duck - Synthesis of 3D jigsaw puzzles over freeform 2-manifolds.

description: This 3D puzzle has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (https://www.cs.technion.ac.il/~irit/). This specific duck puzzle model has been created using function composition of puzzle tiles over the shell geometry of a duck. Puzzle model is provided, in parts, in OBJ file format.
description: This research is also funded in parts by the ISRAEL SCIENCE FOUNDATION, Israel (Grant No. 597/18).
DOI: 10.5281/zenodo.5635642
title: Jigsaw Duck - Synthesis of 3D jigsaw puzzles over freeform 2-manifolds.

description: This 3D puzzle has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (https://www.cs.technion.ac.il/~irit/). This specific duck puzzle model has been created using function composition of puzzle tiles over the shell geometry of a duck. Puzzle model is provided, in parts, in OBJ file format.
description: This research is also funded in parts by the ISRAEL SCIENCE FOUNDATION, Israel (Grant No. 597/18).
DOI: 10.25721/ey8b-8x56
title: J'ai un long voyage à faire
DOI: 10.6084/m9.figshare.16610029
title: Supplementary Material for: Non-ocular influence factors for primary glaucoma：an umbrella review of meta-analysis

description: Medicine
DOI: 10.6084/m9.figshare.16610029.v1
title: Supplementary Material for: Non-ocular influence factors for primary glaucoma：an umbrella review of meta-analysis

description: Medicine
DOI: 10.6084/m9.figshare.12966971
title: Orthogroup functional annotations and effector candidates

description: Functional annotations of all predicted proteins in a <i>Parastagonospora nodorum</i> pangenome.<br>Orthogroups were predicted using ProteinOrtho, each gene is annotated with the P. nodorum isolate that it is predicted in and the orthogroup that it belongs to.<br>The column "is_representative" indicates whether the protein was selected as the representative member of the orthogroup. This prefers copies in reference isolates, then copies closest to the average sequence length within the orthogroup.<br>Predicted whole protein functions were found by searching the Swiss-Prot database version 2020_02 (Bairoch & Apweiler, 2000) using MMSeqs2 version 11-e1a1c (--start-sens 3 -s 7.5 --sens-steps 3 -a) (Steinegger & Söding, 2017). Matches were considered reliable for functional annotation if they covered &gt;= 70% of both sequences, with &gt;=60% sequence identity, and an e-value &lt; 1e-10. Functional domains were predicted using InterProScan (P. Jones et al., 2014; Mitchell et al., 2019). Additionally, GO-terms and predicted product names were predicted using the web-servers of PANNZER (Koskinen et al., 2015) and eggNOG-Mapper (Huerta-Cepas et al., 2017). GO-term predictions from InterProScan, PANNZER, and eggNOG-Mapper were combined and filtered to exclude terms in the GO do_not_annotate “anti-slim” set (available at: http://geneontology.org/docs/download-ontology, downloaded: 2020-05-15) to remove uninformative terms, forming the final GO-term set for the predicted proteomes.<br>Effector-like sequences were predicted using the Predector pipeline (https://github.com/ccdmb/predector, version: 0.1.0-alpha) (Submitted), which incorporates several software analyses including SignalP versions 3.0, 4.1g, 5.0b (José Juan Almagro Armenteros et al., 2019; Dyrløv Bendtsen et al., 2004; Petersen et al., 2011), DeepSig (Savojardo et al., 2018), TargetP version 2.0 (Jose Juan Almagro Armenteros et al., 2019), DeepLoc version 1.0 (Almagro Armenteros et al., 2017), TMHMM version 2.0c (Krogh et al., 2001), Phobius version 1.01 (Käll et al., 2004), EffectorP versions 1 and 2 (Sperschneider et al., 2016; Sperschneider, Dodds, Gardiner, et al., 2018), ApoplastP version 1 (Sperschneider, Dodds, Singh, et al., 2018), LOCALIZER (Sperschneider et al., 2017), homology searches against dbCAN version 8 using HMMER version 3.3 (Mistry et al., 2013), and sequence matches against PHI-base version 4.9 (Urban et al., 2020) using MMSeqs2 version 11.e1a1c (Steinegger & Söding, 2017).Information from Predector, InterProScan, Pannzer, eggNOG-mapper, positive selection and orthogroup analyses were combined into a single table.<br>Raw orthogroup CDS codon alignments, gene trees, and BUSTED results are also included for all non-singleton orthogroups.
DOI: 10.6084/m9.figshare.12966971.v3
title: Orthogroup functional annotations and effector candidates

description: Functional annotations of all predicted proteins in a <i>Parastagonospora nodorum</i> pangenome.<br>Orthogroups were predicted using ProteinOrtho, each gene is annotated with the P. nodorum isolate that it is predicted in and the orthogroup that it belongs to.<br>The column "is_representative" indicates whether the protein was selected as the representative member of the orthogroup. This prefers copies in reference isolates, then copies closest to the average sequence length within the orthogroup.<br>Predicted whole protein functions were found by searching the Swiss-Prot database version 2020_02 (Bairoch & Apweiler, 2000) using MMSeqs2 version 11-e1a1c (--start-sens 3 -s 7.5 --sens-steps 3 -a) (Steinegger & Söding, 2017). Matches were considered reliable for functional annotation if they covered &gt;= 70% of both sequences, with &gt;=60% sequence identity, and an e-value &lt; 1e-10. Functional domains were predicted using InterProScan (P. Jones et al., 2014; Mitchell et al., 2019). Additionally, GO-terms and predicted product names were predicted using the web-servers of PANNZER (Koskinen et al., 2015) and eggNOG-Mapper (Huerta-Cepas et al., 2017). GO-term predictions from InterProScan, PANNZER, and eggNOG-Mapper were combined and filtered to exclude terms in the GO do_not_annotate “anti-slim” set (available at: http://geneontology.org/docs/download-ontology, downloaded: 2020-05-15) to remove uninformative terms, forming the final GO-term set for the predicted proteomes.<br>Effector-like sequences were predicted using the Predector pipeline (https://github.com/ccdmb/predector, version: 0.1.0-alpha) (Submitted), which incorporates several software analyses including SignalP versions 3.0, 4.1g, 5.0b (José Juan Almagro Armenteros et al., 2019; Dyrløv Bendtsen et al., 2004; Petersen et al., 2011), DeepSig (Savojardo et al., 2018), TargetP version 2.0 (Jose Juan Almagro Armenteros et al., 2019), DeepLoc version 1.0 (Almagro Armenteros et al., 2017), TMHMM version 2.0c (Krogh et al., 2001), Phobius version 1.01 (Käll et al., 2004), EffectorP versions 1 and 2 (Sperschneider et al., 2016; Sperschneider, Dodds, Gardiner, et al., 2018), ApoplastP version 1 (Sperschneider, Dodds, Singh, et al., 2018), LOCALIZER (Sperschneider et al., 2017), homology searches against dbCAN version 8 using HMMER version 3.3 (Mistry et al., 2013), and sequence matches against PHI-base version 4.9 (Urban et al., 2020) using MMSeqs2 version 11.e1a1c (Steinegger & Söding, 2017).Information from Predector, InterProScan, Pannzer, eggNOG-mapper, positive selection and orthogroup analyses were combined into a single table.<br>Raw orthogroup CDS codon alignments, gene trees, and BUSTED results are also included for all non-singleton orthogroups.